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**GRAND CAYMAN, Cayman Islands, July 11th, 2024/Chainwire/--**Zero Hash, the leading crypto and stablecoin infrastructure platform, today announced that its platform customers can now enable the SUI token from Sui, the Layer 1 blockchain in permitted jurisdictions. Sui was designed with the objective of providing unparalleled speed, security, and scalability. It achieves near instant transaction finality and supports up to 297,000 transactions per second. Utilizing the innovative Move programming language, Sui enables developers to build advanced, secure smart contracts. The Sui blockchain has already processed over four billion transactions, showcasing its capability to support large-scale applications and services. Zero Hash's full stack technical and global regulatory infrastructure facilitates the compliant movement of value of fiat, crypto, and stablecoins. Zero Hash’s APIs and SDKs enable businesses to seamlessly embed blockchain technology, including Sui, with minimal friction. With access to over 65 digital assets and 22 blockchains, now including Sui, Zero Hash powers businesses to build highly connected and interoperable crypto and stablecoin products, for the new era of money movement and value transfer. Greg Siourounis, Managing Director of the Sui Foundation, commented, “We are thrilled that Zero Hash now supports Sui. Zero Hash’s infrastructure is vital for connecting fiat and crypto economies, making it easier for businesses and users to interact with the Sui technology platform.”Joaqin Ayuso de Paul, Global Head of Product at Zero Hash, added, “The integration of SUI blockchain into Zero Hash’s ecosystem is another example of us providing our customers simplified and compliant access to innovative technology to design new and improved ways to move value globally." About Sui Sui is a first-of-its-kind Layer 1 blockchain and smart contract platform designed from the ground up to make digital asset ownership fast, private, secure, and accessible to everyone. Its object-centric model, based on the Move programming language, enables parallel execution, sub-second finality, and rich on-chain assets. With horizontally scalable processing and storage, Sui supports a wide range of applications with unrivaled speed at low cost. Sui is a step-function advancement in blockchain and a platform on which creators and developers can build amazing user-friendly experiences. For more information about Sui, users can visit https://sui.io. About Zero Hash Zero Hash is a B2B2C crypto-as-a-service infrastructure platform that allows any platform to embed digital assets natively into their own customer experience quickly and easily through a matter of API endpoints. Zero Hash’s turnkey solution handles the entire backend complexity and regulatory licensing required to offer crypto products. Zero Hash Holdings, through its subsidiaries, powers neo-banks, broker-dealers, payment groups as well as non-financial brands to offer crypto and stablecoin-powered products. Zero Hash Holdings is backed by investors, including Point72 Ventures, Bain Capital Ventures, and NYCA. Zero Hash LLC is a FinCen-registered Money Service Business and a regulated Money Transmitter that can operate in 51 US jurisdictions. Zero Hash LLC and Zero Hash Liquidity Services LLC are licensed to engage in virtual currency business activity by the New York State Department of Financial Services. In Canada, Zero Hash LLC is registered as a Money Service Business with FINTRAC. Zero Hash Australia Pty Ltd. is registered with AUSTRAC as a Digital Currency Exchange Provider, with DCE registered provider number DCE100804170-001. This registration enables Zero Hash to offer its crypto services in Australia. Zero Hash Australia Pty Ltd. is registered on the New Zealand register of financial service providers, with Financial Service Provider (FSP) number FSP1004503. A FSP in New Zealand is a registration and does not mean that Zero Hash Australia Pty Ltd. is licensed by a New Zealand regulator to provide crypto services. Zero Hash Australia Pty Ltd.’s registration on the New Zealand register of financial service providers does not mean that Zero Hash Australia is subject to active regulation or oversight by a New Zealand regulator. Zero Hash Europe B.V. is registered as a Virtual Asset Services Provider (VASP) registration by the Dutch Central Bank (Relation number: R193684). Zero Hash Europe Sp. Zoo is registered as a VASP by the Tax Administration Chamber of Poland in Katowice (Registration number RDWW – 1212). Users can connect with Zero Hash on LinkedIn, or visit www.zerohash.com for more information. Disclosures: Zero Hash services and product offerings may not be available in all jurisdictions. Zero Hash accounts are not subject to FDIC or SIPC protections, or any such equivalent protections that may exist outside of the US. Zero Hash’s technical support and enablement of any asset is not an endorsement of such asset and is not a recommendation to buy, sell, or hold any crypto asset. The value of any cryptocurrency, including digital assets pegged to fiat currency, commodities, or any other asset, may go to zero. Contact Sui Foundation media@sui.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **GRAND CAYMAN, Cayman Islands, July 11th, 2024/Chainwire/--**Zero Hash, the leading crypto and stablecoin infrastructure platform, today announced that its platform customers can now enable the SUI token from Sui, the Layer 1 blockchain in permitted jurisdictions. Sui was designed with the objective of providing unparalleled speed, security, and scalability. It achieves near instant transaction finality and supports up to 297,000 transactions per second . Utilizing the innovative Move programming language, Sui enables developers to build advanced, secure smart contracts. The Sui blockchain has already processed over four billion transactions , showcasing its capability to support large-scale applications and services. 297,000 transactions per second 297,000 transactions per second over four billion transactions over four billion transactions Zero Hash's full stack technical and global regulatory infrastructure facilitates the compliant movement of value of fiat, crypto, and stablecoins. Zero Hash’s APIs and SDKs enable businesses to seamlessly embed blockchain technology, including Sui, with minimal friction. With access to over 65 digital assets and 22 blockchains, now including Sui, Zero Hash powers businesses to build highly connected and interoperable crypto and stablecoin products, for the new era of money movement and value transfer. Greg Siourounis, Managing Director of the Sui Foundation, commented, “We are thrilled that Zero Hash now supports Sui. Zero Hash’s infrastructure is vital for connecting fiat and crypto economies, making it easier for businesses and users to interact with the Sui technology platform.”Joaqin Ayuso de Paul, Global Head of Product at Zero Hash, added, “The integration of SUI blockchain into Zero Hash’s ecosystem is another example of us providing our customers simplified and compliant access to innovative technology to design new and improved ways to move value globally." Greg Siourounis, Managing Director of the Sui Foundation, commented, “We are thrilled that Zero Hash now supports Sui. Zero Hash’s infrastructure is vital for connecting fiat and crypto economies, making it easier for businesses and users to interact with the Sui technology platform.”Joaqin Ayuso de Paul, Global Head of Product at Zero Hash, added, “The integration of SUI blockchain into Zero Hash’s ecosystem is another example of us providing our customers simplified and compliant access to innovative technology to design new and improved ways to move value globally." About Sui Sui is a first-of-its-kind Layer 1 blockchain and smart contract platform designed from the ground up to make digital asset ownership fast, private, secure, and accessible to everyone. Its object-centric model, based on the Move programming language, enables parallel execution, sub-second finality, and rich on-chain assets. With horizontally scalable processing and storage, Sui supports a wide range of applications with unrivaled speed at low cost. Sui is a step-function advancement in blockchain and a platform on which creators and developers can build amazing user-friendly experiences. For more information about Sui, users can visit https://sui.io . https://sui.io https://sui.io About Zero Hash Zero Hash is a B2B2C crypto-as-a-service infrastructure platform that allows any platform to embed digital assets natively into their own customer experience quickly and easily through a matter of API endpoints. Zero Hash’s turnkey solution handles the entire backend complexity and regulatory licensing required to offer crypto products. Zero Hash Holdings, through its subsidiaries, powers neo-banks, broker-dealers, payment groups as well as non-financial brands to offer crypto and stablecoin-powered products. Zero Hash Holdings is backed by investors, including Point72 Ventures, Bain Capital Ventures, and NYCA. Zero Hash LLC is a FinCen-registered Money Service Business and a regulated Money Transmitter that can operate in 51 US jurisdictions. Zero Hash LLC and Zero Hash Liquidity Services LLC are licensed to engage in virtual currency business activity by the New York State Department of Financial Services. In Canada, Zero Hash LLC is registered as a Money Service Business with FINTRAC. Zero Hash Australia Pty Ltd. is registered with AUSTRAC as a Digital Currency Exchange Provider, with DCE registered provider number DCE100804170-001. This registration enables Zero Hash to offer its crypto services in Australia. Zero Hash Australia Pty Ltd. is registered on the New Zealand register of financial service providers, with Financial Service Provider (FSP) number FSP1004503. A FSP in New Zealand is a registration and does not mean that Zero Hash Australia Pty Ltd. is licensed by a New Zealand regulator to provide crypto services. Zero Hash Australia Pty Ltd.’s registration on the New Zealand register of financial service providers does not mean that Zero Hash Australia is subject to active regulation or oversight by a New Zealand regulator. Zero Hash Europe B.V. is registered as a Virtual Asset Services Provider (VASP) registration by the Dutch Central Bank (Relation number: R193684). Zero Hash Europe Sp. Zoo is registered as a VASP by the Tax Administration Chamber of Poland in Katowice (Registration number RDWW – 1212). Users can connect with Zero Hash on LinkedIn , or visit www.zerohash.com for more information. LinkedIn LinkedIn www.zerohash.com www.zerohash.com Disclosures: Zero Hash services and product offerings may not be available in all jurisdictions. Zero Hash accounts are not subject to FDIC or SIPC protections, or any such equivalent protections that may exist outside of the US. Zero Hash’s technical support and enablement of any asset is not an endorsement of such asset and is not a recommendation to buy, sell, or hold any crypto asset. The value of any cryptocurrency, including digital assets pegged to fiat currency, commodities, or any other asset, may go to zero. Contact Sui Foundation media@sui.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Zero Hash Integrates Sui Blockchain Accessibility

Authors: (1) Chenhao Shi, Shanghai Jiao Tong University; (2) Ruibang Liu, Shanghai Jiao Tong University; (3) Guoqiang Li†, Shanghai Jiao Tong University. Table of Links Abstract & Introduction Preliminaries Normalization Algorithm Evaluation Conclusion & References V. CONCLUSION In this paper, we propose an algorithm based on data flow analysis to construct a paradigm for R1CS. The correctness and equivalence of R1CS have long been challenging to study due to the diversity and flexibility of constraint construction methods. Our algorithm aims to eliminate the differences between equivalent R1CS constraint systems through a series of abstraction processes. We introduce ordering methods for internal variables and constraints in R1CS, which provide reference information for the final paradigm generation. Through experimental results, we demonstrate that our algorithm can identify equivalent R1CS resulting from constraint merging, intermediate variable selection, and variable and constraint reordering, and transform them into a unique paradigm. Therefore, R1CS plays an indispensable part in zk-SNARKS. Further work includes establishing rules for merging constraints and a more comprehensive benchmark. This requires us to conduct more in-depth research and exploration of the generation rules of R1CS to improve the algorithm. REFERENCES [1] S. Goldwasser, S. Micali, and C. Rackoff, “The knowledge complexity of interactive proof-systems,” in Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio Micali, 2019, pp. 203–225. [2] Zcash company, https://z.cash/, 2014. [3] E. B. Sasson, A. Chiesa, C. Garman, et al., “Zerocash: Decentralized anonymous payments from bitcoin,” in 2014 IEEE symposium on security and privacy, IEEE, 2014, pp. 459–474. [4] G. Drevon, J-r1cs – a json lines format for r1cs, 2019. [5] E. Albert, M. Belles-Mu ´ noz, M. Isabel, C. Rodrı ˜ eguez- ´ Nu´nez, and A. Rubio, “Distilling constraints in zero- ˜ knowledge protocols,” in Computer Aided Verification: 34th International Conference, CAV 2022, Haifa, Israel, August 7–10, 2022, Proceedings, Part I, Springer, 2022, pp. 430–443. [6] E. Ben-Sasson, A. Chiesa, M. Riabzev, N. Spooner, M. Virza, and N. P. Ward, “Aurora: Transparent succinct arguments for r1cs,” in Advances in Cryptology– EUROCRYPT 2019: 38th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Darmstadt, Germany, May 19–23, 2019, Proceedings, Part I 38, Springer, 2019, pp. 103–128. [7] J. Lee, S. Setty, J. Thaler, and R. Wahby, “Lineartime and post-quantum zero-knowledge snarks for r1cs,” Cryptology ePrint Archive, 2021. [8] A. Golovnev, J. Lee, S. Setty, J. Thaler, and R. S. Wahby, “Brakedown: Linear-time and post-quantum snarks for r1cs,” Cryptology ePrint Archive, 2021. [9] Y. Muzi, F. Muyue, B. Gu, et al., Semantic comparison method and device between a kind of source code and binary code, 2019. [10] G. Li, L. Zhongqi, Y. Dongsheng, and Y. Liu, Compiling method from intermediate language (il) program to c language program of instruction list, 2013. [11] Z. Yongsheng, C. Zhiyong, C. Rongtao, and W. Zhili, A kind of assembly language is to the code conversion method of higher level language and device, 2015. [12] E. Ben-Sasson, A. Chiesa, E. Tromer, and M. Virza, “Succinct {non-interactive} zero knowledge for a von neumann architecture,” in 23rd USENIX Security Symposium (USENIX Security 14), 2014, pp. 781–796. [13] V. Buterin, “Quadratic arithmetic programs: From zero to hero,” URl: https://medium. com/@ VitalikButerin/quadratic-arithmetic-programs-fromzero-to-hero-f6d558cea649, 2016. [14] H. Vollmer, Introduction to circuit complexity: a uniform approach. Springer Science & Business Media, 1999. [15] J. Groth, “On the size of pairing-based non-interactive arguments,” in Advances in Cryptology–EUROCRYPT 2016: 35th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Vienna, Austria, May 8-12, 2016, Proceedings, Part II 35, Springer, 2016, pp. 305–326. [16] J. B. Dennis, “First version of a data flow procedure language,” in Programming Symposium: Proceedings, Colloque sur la Programmation Paris, April 9–11, 1974, Springer, 2005, pp. 362–376. [17] P. C. Treleaven, D. R. Brownbridge, and R. P. Hopkins, “Data-driven and demand-driven computer architecture,” ACM Computing Surveys (CSUR), vol. 14, no. 1, pp. 93–143, 1982. [18] W. Xing and A. Ghorbani, “Weighted pagerank algorithm,” in Proceedings. Second Annual Conference on Communication Networks and Services Research, 2004., IEEE, 2004, pp. 305–314. [19] L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: Bring order to the web,” Technical report, stanford University, Tech. Rep., 1998. This paper is under CC BY 4.0 DEED license. available on arxiv

Data-Flow-Based Normalization Generation Algorithm of R1CS: Conclusion & References

Verifiable Privacy-Preserving Computing: Conclusion & References

This paper is under CC 4.0 license. available on arxiv Authors: (1) Tariq Bontekoe, University of Groningen; (2) Dimka Karastoyanova, University of Groningen; (3) Fatih Turkmen, University of Groningen. Table of Links Abstract & Introduction Preliminaries Zero-knowledge proofs and verifiable computing Privacy-preserving computations Requirements: an application’s perspective Verifiable, privacy-preserving computing Open challenges and future directions Conclusion & References 6. Verifiable, privacy-preserving computing An increasing number of papers discusses the combination of PETs with verifiable computing in settings where both privacy and correctness should be guaranteed. In this section we gather, classify and compare existing works that add (public) verifiability to (decentralized) privacypreserving computations. Moreover, we answer the following question RQ3: How do existing solutions compare with regards to the requirements/properties of Section 5? 6.1. Methodology To obtain a comprehensive list of current works, we first determined a small set of recent, relevant articles on (publicly) verifiable/auditable privacy-preserving computations. Specifically, we determined at least one very recent ‘seed’ article for different underlying (privacy-preserving) computation techniques, by querying the Google Scholar and Scopus databases. The most recent, at the time of writing, matching verifiable MPC paper [72] was found with the query: (public verifiability OR public auditability) AND (MPC OR SMPC OR multi-party computation OR secure multi-party computation). Two relevant, recent verifiable HE papers [73], [74] were found using: (verifiable OR public verifiability OR public auditability) AND (homomorphic encryption OR fully homomorphic encryption). The final seed paper [54] on DLT based privacy-preserving computations was, up front, known to be recent and relevant, hence no specific query was used. Subsequently, we checked all papers in the reference list of these ‘seed’ articles to the second degree. Next to this, all articles that referenced our ‘seed’ articles were also checked. After filtering the articles on their relevance, based on title, abstract, and a quick scan, we found 32 relevant works, presenting a novel or improved solution for verifiable privacy-preserving computations. 6.2. Classification Table 1 shows an overview of all papers and the constructions used to combine privacy and verifiability, classified according to the underlying computation mechanism that was employed. In the text below, we discuss the solutions in more detail per verifiability mechanism used: ZKPs, MACs, and TEE, and other approaches. ZKPs are used in MPC, HE, and DLT approaches. Most MPC based solutions rely on some form of LSSS, e.g., Shamir’s secret sharing (SSS) [31] or SPDZ(-like) schemes [32]. Approaches using PHE (e.g., CDN [97]) or GCs were observed less often. Zero-knowledge proofs. MPC. The majority of verifiable MPC solutions uses commitments to secret shares of input/output values and intermediate computations in combination with NIZKs to guarantee correctness. We distinguish three types of NIZKs that are used with varying properties. The first set of works [40], [72], [82], [84], uses Σ-protocols and the FS heuristic to obtain NIZKs from standard assumptions. Two of the SPDZ(-like) protocols ([40], [82]) additionally rely on MACs similar to those used in the original SPDZ protocol. A downside of Σ-protocols is the large proof size and significant effort required on the verifier’s side for larger computations. Hence, recent works [76], [83] often apply the more efficient compressed Σ-protocol theory [27], or Σ-bullets [26], which provides similar security guarantees. As an even more efficient approach, other works [6], [77], [78], [79] use distributed, often adaptive, zk-SNARKs to assure correctness. [81] presents an MPC based solution relying on crowd verifiable ZKPs. Meaning that a prover tries convince a group of verifiers (‘crowd’), where each verifier only provides a bounded amount of randomness, thus requiring a mostly honest crowd. HE. Only in recent years, the first solutions using ZKPs to create verifiable HE have appeared. [88] presents the first zk-SNARK suitable for the BV scheme [98]. It does however require specific setup parameters for the FHE scheme that fit the group orders of the zk-SNARK, leading to decreased efficiency of FHE operations. [95] improves upon this work by not restricting the HE parameter space. However, both solutions are limited to ‘simpler’ FHE schemes, i.e., without complex ciphertext maintenance operations, since these are not supported by the homomorphic hashing scheme used. [86] proposes a method that does not rely on homomorphic hashes, but rather uses Rinocchio: a zk-SNARK that efficiently operates on rings. [73, Sec. 5.1] compares Rinocchio against three popular efficient ZKPs and show a decrease in prover time by a factor ∼1.5–25000. An open question for Rinocchio is how to perform ciphertext maintenance operations. This generally necessitates operations not supported by rings or switching between different rings, which is not supported by Rinocchio. DLT. Some schemes do not rely on a specific PET for privacy-preserving computations. Instead, they combine DLT with ZKP to achieve a similar result. This especially concerns the use of zk-SNARKs, due to their small, often constant, proof size and efficient verification. While most applications focus purely on financial transactions, e.g., Zerocash [7], we also identify works that focus on smart contracts. [26] proposes Σ-bullets to create a privacypreserving payment system in smart contract platforms. The provided functionality does, however, not directly generalize to non-payment related computations. [53] proposes the Hawk framework for verifying generic smart contract functions whilst preserving privacy of input and output using zk-SNARKs. DPC, as proposed in [54], can be seen as an evolution of Hawk that also provides function privacy using composable zk-SNARKs. We note that all of these works only verify local computations executed by a single party. However, due to publishing results and proofs on a blockchain, the entire system could be used for verifiable, privacy-preserving computations with multiple parties. Message authentication codes are predominantly used in HE approaches, but also appear for MPC. MACs. MPC. While MACs are often used to achieve active security in MPC, e.g., SPDZ [32], it is not directly clear how to obtain public verifiability, due to often having to rely on a secret key for verification. [5] uses MACs to design a scheme where n clients verifiably outsource a computation to m workers. However, only clients are able to verify the computation, and at least one worker needs to act honestly to guarantee correctness and data privacy. HE. We see three different approaches for combining HE ciphertexts and MACs, agreeing with [73]: (1) Encryptthen-MAC; (2) Encrypt-and-MAC; (3) MAC-then-Encrypt. The Encrypt-then-MAC approach is used in [87]. The authors propose to first homomorphically hash BGV [99] ciphertexts, and then add a homomorphic MAC computed from that hash. However, the presented solution is rather limited, and only supports quadratic circuits. Due to the lack of other works using this approach, it is unclear whether it can be generalized to general arithmetic circuits. Encrypt-and-MAC approaches have a MAC and ciphertext that are independent of one another, both are computed directly from the plaintext. The MAC therefore needs to be both hiding and support the ciphertext operations required for the FHE scheme. [94] introduces a privacypreserving homomorphic MAC that supports multiplication and addition operations. This primitive is subsequently used to construct an efficient Encrypt-and-MAC construction for HE. However, it is unclear how the MAC supports other types of operations that are required for most FHE schemes. We found the most occurring approach of the three to be MAC-then-Encrypt, where the MAC is computed from the plaintext and concatenated to the plaintext before encryption. [91] is the first to introduce a fully homomorphic MAC scheme that can be used to verify HE computations over boolean circuits. While the MACs of this scheme are succinct, using this approach to verify HE computations is no more efficient than running the computation itself. An improvement upon this scheme, with more efficient verification of HE computations on arithmetic circuits, is proposed in [92]. The approach is however limited to arithmetic circuits of bounded depth. [93], a more recent work using the same approach, does not have any of these limitations and can thus be used to verify HE evaluations of any arithmetic circuit. None of the above schemes are publicly verifiable, due to requiring access to the full secret key to verify the MAC. A TEE is a collective name for (mostly) hardware-based solutions that protect data privacy while enabling the use of that data in computations. TEEs require specific hardware, e.g., Intel SGX or AMD SEV, that compute on encrypted data, by only decrypting it inside a secure part of the CPU. Some TEEs also provide attestation mechanisms to verify the code running inside. Trusted execution environment. [96] proposes Ekiden, a blockchain solution where smart contracts over private data are executed off-chain. Specialized compute nodes with TEEs execute the smart contract and publish a remote attestation to correct execution. The consensus nodes on their turn should have the ability to verify these attestations to update the smart contract results on-chain. [90] presents a construction for FHE in TEE that makes use of TEE attestation as well. Clients can send their encrypted data to a TEE in the cloud, and only need to attest that it runs exactly the right computations. TEEs naturally support more computations than other approaches, however the supported operations are more limited and significantly slower than when they were executed by regular CPUs. Optimizing FHE computations for TEEs can lead to notable performance improvements [73]. For both approaches it is unclear how to achieve public verifiability, i.e., how attestation can be (efficiently) performed by external parties. Moreover, while TEEs are often relatively efficient compared to other methods, they do require trust in the specialized hardware that is used. Some works did not fit any of the above categories. Nonetheless, the approaches are worth mentioning. Other. [75] outsources all computations to a trusted third party (TTP). This TTP produces NIZK proofs to guarantee correctness and is trusted to preserve input privacy. This solution is relatively efficient, however requiring a TTP is unrealistic in many real-world use cases. [80] introduces a pairing-based instantiation of functiondependent commitments, leading to efficient verification of correctness. However, the proposed construction only supports linear functions and it is unclear whether this can be extended to non-linear functions. [85] presents an efficient, constant round, publicly verifiable MPC protocol that only makes black-box use of cryptographic primitives. Their solution is based on a BMRtype garbled circuit [37] and requires black-box access to a statistically secure OT scheme and a circular 2-correlation robust hash function. Notably, correctness and public verifiability only hold when there is at least one honest party. [8] constructs an MPC protocol using an SWHE scheme supporting a single multiplication. Verifiable key switching, using NIZKs, is adopted to support computations with an arbitrary number of multiplications. [74] presents an approach for verifiable FHE, they call ‘blind hashing’, where the hash of the plaintext is appended to the plaintext before encryption, i.e., this is highly similar to MAC-then-Encrypt. The hash is subsequently verified by also computing the hash of the decrypted result and ensuring it equals the hash that was included in the ciphertext. We note that this preliminary publication lacks security proof or reasoning, and only explains how to perform a single matrix multiplication. It is unclear how this approach extends to other operations, such as needed for ciphertext maintenance and more complex circuits. Finally, [89] proposes to use Yao’s GC [35] for one-time verifiable computations between a client and server. Subsequently, they propose to add FHE on top to transform this one-time approach into a reusable one, allowing multiple evaluations of the same function on different input data. It should be noted that a different computation needs a new setup, and computing the setup is rather inefficient. 6.3. Efficiency 1 – 3 An overview of the asymptotic complexities of each scheme is provided in Table 2. In case the authors did not mention the complexity of their solution in the article, and we could not find it in related works, we marked it as ‘N/A’. Specifically, we describe the computation ( 1 ) and communication ( 2 ) complexity of the verifiable computation. Whenever possible, we split the costs between those used to create the function result and those used to create the proof or tag needed for verification. If a solution makes use of a generic building block, rather than specifying the exact scheme used, we describe the type of building block used, e.g., LSSS, HE, or zk-SNARK. Since verification ( 3 ) is executed apart from the computation in the case of public verifiability, we list its complexity in a separate column. Finally, we list the asymptotic number of commitments used by each solution, if they are used and if their complexity is not yet included in the other columns. Commitments lead to additional computation and communication costs, depending on the specific commitment scheme used. In short, we observe that MPC approaches have a higher communication and a lower computation cost than HE based approaches. Moreover, we see that both MAC and zk-SNARK based approaches have efficient verification, especially when compared to other approaches. MACs do often require more communication than zk-SNARKs, but are more efficient computationally. 6.4. Properties 4 – 8 Based on our discussion in Section 5 we noted several important aspects that determine the suitability of a scheme for several domains. Table 3 provides an overview of the relevant aspects regarding security and usability. Next to this, we list whether the original work has evaluated their solution in a practical scenario, and whether they provide a publicly available implementation. Below, we compare the different solution approaches by discussing the most striking observations and challenges for future work. There is an observable difference between MPC- and HE-based solutions regarding the assumptions for which they provide data privacy. This is largely due to the fact that MPC distributes data over multiple parties, whereas HE-based solutions generally send all their data to a central computation server. Data privacy 4 . In MPC based solutions, privacy depends on the scheme used. While some schemes guarantee privacy when only one of the parties is honest, others require an honest majority. For HE, data privacy of each ciphertext is guaranteed, and only parties holding the secret key can view the private data. However, in many use cases with distributed data or decentralized computations, inputs must be encrypted under a secret-shared secret key. In that case, the same observations as for MPC-based solutions hold. While almost all schemes provide unconditional correctness, the same cannot be said for public verifiability. Most HE solutions do not offer public verifiability, in contrast to MPCbased solutions. The cause for this is most likely due to the fact that most HE solutions rely on MACs. Correctness and public verifiability 5 & 6 . Next to this, the combination of ZKPs and HE schemes is currently too inefficient and constrained. We determine three main groups of security assumptions. The first group relies on TEE for both privacy and correctness. While these schemes do not require any non-standard cryptographic assumptions and are also very efficient, they do require access to and trust in very specific hardware. Due to recent attacks on CPU hardware, e.g., Spectre and Meltdown, parties may be reluctant to use this. Security assumptions 7 & 8 . The second group uses zk-SNARKs to achieve public verifiability. These schemes are known to be highly efficient, but have to rely on non-standard cryptographic assumptions, such as knowledge-of-exponent type assumptions, in return. Moreover, many of these schemes rely on a trusted setup to guarantee security, which may be undesirable in many cases. The last group consists of constructions relying mostly on Σ-protocols and MACs. Such constructions are often less efficient, but they compensate this by relying only on standard cryptographic assumptions, without requiring specific hardware. Regarding post-quantum security ( 8 ) we note that most HE schemes themselves are by default post-quantum secure and thus many of the MAC-based constructions discussed above are likely to derive this property, even though this is not directly discussed. However, most MPC based constructions are not post-quantum secure. Only [72] makes use of mostly post-quantum secure primitives. Even though it is not fully post-quantum secure, with some small adaptions it likely will be.

Verifiable Privacy-Preserving Computing: Verifiable, privacy-preserving computing

This paper is under CC 4.0 license. available on arxiv Authors: (1) Tariq Bontekoe, University of Groningen; (2) Dimka Karastoyanova, University of Groningen; (3) Fatih Turkmen, University of Groningen. Table of Links Abstract & Introduction Preliminaries Zero-knowledge proofs and verifiable computing Privacy-preserving computations Requirements: an application’s perspective Verifiable, privacy-preserving computing Open challenges and future directions Conclusion & References 5. Requirements: an application’s perspective In this section, we discuss how verifiability is combined with privacy-preserving computing in practical settings that are frequently observed for verifiability and/or privacypreserving computations. We discuss two frequent scenarios: verifiable outsourcing and blockchain; and two use cases showing specific requirements: e-voting and genomics. These were the most frequently observed scenarios and use cases in existing literature. For each application we summarize the domain-specific requirements and efficiency trade-offs, that answer when combined. Each property has a specific mark X assigned, that will be used for reference in subsequent sections. RQ2 5.1. Verifiable outsourcing One of the first and most straightforward applications of verifiable PETs is that of verifiable outsourcing, where a (group of) data owner(s) outsource(s) a costly computation to a computing party with more resources. This computing party produces a computation result as well as a ZKP, trusted execution environment (TEE) attestation, or MAC to guarantee correctness. When the used verification method allows for verification that is more efficient than the original computation, the scheme is considered efficient. Pinocchio [21] was one of the first efficient ZKP schemes for general computations, making it practical for verifiable outsourcing. Since then, many new ZKPs schemes with better efficiency or security have been constructed, e.g., [22], [25], [49], [50]. However, when applying these schemes directly, they do not provide data privacy in most use cases, even though this is a necessity when computing over private data distributed over multiple parties. Since, in most cases, only the clients that outsource their computation need to be able to guarantee correctness, public verifiability is often not necessary. Most importantly, verifiable outsourcing is only beneficial when the verification (and/or preprocessing) time of the clients is significantly smaller than the time it would take to compute the outcome by themselves, i.e., the solution should be efficient. 5.2. Blockchain Blockchain was introduced as a platform to realize decentralized online currency, but has since become a much broader platform. The recent surge in blockchain and DLT applications, such as dApps, has also had significant impact on the development of new ZKP schemes, an important solution for verifiability. However, blockchain applications also frequently rely upon ZKPs to provide applications that guarantee correctness, whilst maintaining privacy. Examples of such applications range from private monetary transactions, e.g., Zerocash [7], Zether [26], and others [51], [52], to private smart contracts, e.g., Hawk [53], and decentralized private computation (DPC), e.g., ZEXE [54]. Due to the decentralized nature of blockchain, with a lack of central authority to guarantee correctness over private data, verifiable computing methods are a logical, and often unavoidable, solution. For most blockchain applications, regular verifiability will often not be sufficient. Since every (future) connected party must be able to verify any message posted to the blockchain, public verifiability is a necessary requirement. To prevent scaling issues in blockchain applications, developers attempt to keep verification times low, and onchain messages as small as possible. Therefore, solutions that sacrifice prover time for lower verification times and sublinear (or constant) sized verification messages are favorable. Finally, we note that interactive protocols are, generally speaking, not suitable for distributed ledgers as that would require direct communication, conflicting the principles of DLT. Since non-interactivity is a prerequisite for public verifiability, we do not include this requirement by itself. 5.3. Verifiable privacy-preserving genomics Usage of privacy-preserving computations in genomics has taken a big leap in the past decade [55], [56], [57] and even led to a yearly competition on human genome privacy [58], [59], [60], with specific tracks on the use of PETs and blockchain. The first application of verifiable computing in the genomic domain is discussed in [61], as far as we are aware. Specifically, the authors propose the use of HE and homomorphic MACs to outsource computation of the disease susceptibility (DS) score to a third party in a privacypreserving and verifiable manner. The increase in efficient methods for verifiable computing in recent years, has also led to the first solutions in this domain. A method to perform an elastic net regression among multiple parties in a privacy-preserving manner, by outsourcing the computation to a pair of non-colluding cloud providers, is proposed in [62]. Correctness of the result is guaranteed by verifying that the evaluation of the final gradient is close to zero. [63] presents a solution for evaluating secure count or top-k queries on multi-source genomic data. All data is encrypted using an additively homomorphic encryption scheme and shared with two non-colluding cloud providers. The cloud parties evaluate the query, with privacy of query, input data, and result, and use MACs to guarantee correctness. [64] presents a verifiable homomorphic secret sharing scheme for evaluating polynomials of low degree. Verifiable genomic analyses, and verifiable computations on outsourced data are mentioned as possible use cases. Lastly, [65] computes the minor allele frequency (MAF) score over multi-source data in a blockchain setting. The more expensive computations, those on additively HE ciphertexts, are performed off-chain, ZKPs are used to guarantee correctness thereof. It is clear that the use cases in genomics are very diverse and have different requirements regarding efficiency or which parties can verify. However, it is abundantly clear that genomic data requires strong privacy guarantees. Due to the long-livedness of genomic data, i.e., a person’s genome also reveals significant private information on their family and descendants, store-now-decrypt-later attacks form a serious risk, especially at the dawn of practical quantum computing [66]. The use of post-quantum secure solutions should therefore be seriously considered for any work in the genomic domain. 5.4. Verifiable e-voting An electronic voting system allows a population to cast their votes using electronic rather than physical means. The need for data privacy follows directly from the fact that, in general, elections and voting processes require voter privacy. Using verifiability techniques to make elections completely verifiable by the general public has been a topic of active research for many years, e.g., [67], [68]. The developments in blockchain and more efficient ZKPs have led to many new proposals for e-voting solutions. [69] discusses the use of NIZK proofs to make threshold homomorphic encryption-based voting secure. A novel SNARK-friendly, additively-homomorphic verifiable encryption scheme is proposed and applied to verify vote validity is presented in [9]. Recently, [70] proposes an end-to-end verifiable and secret election scheme, based on NIZKs and MPC. Blockchains and cloud servers can both be used to store the secure ballots. It is easy to see that public verifiability is a requirement in verifiable e-voting schemes. Next to this, trust is one of the most important factors to guarantee that e-voting schemes are to be adopted in practice [71]. It may therefore make sense, to only use solutions that are post-quantum secure and rely on standard assumptions to improve the level of trust, potentially at the cost of some efficiency.

Applications of Verifiable Privacy-Preserving Computing

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Shanshan Han & Qifan Zhang, UCI; (2) Wenxuan Wu, Texas A&M University; (3) Baturalp Buyukates, Yuhang Yao & Weizhao Jin, USC; (4) Salman Avestimehr, USC & FedML. Table of Links Abstract and Introduction Problem Setting The Proposed Two-Stages Anomaly Detection Verifiable Anomaly Detection using ZKP Evaluations Related Works Conclusion & References 7 CONCLUSION We present a cutting-edge anomaly detection technique specifically designed for the real-world FL systems. Our approach utilizes an early cross-round check that activates subsequent anomaly detection exclusively in the presence of attacks. When attacks happen, our approach removes anomaly client models efficiently, ensuring that the local models submitted by benign clients remain unaffected. Further, by leveraging ZKPs, participating clients get to verify the integrity of the anomaly detection and removal performed by the server. The practical design and the inherent efficiency of our approach make it well-suited for real-world FL systems. Our plans for future works include extending our approach to asynchronous FL and vertical FL scenarios. REFERENCES T. Aoki. On the stability of the linear transformation in banach spaces. Journal of the Mathematical Society of Japan, 2(1-2):64–66, 1950. E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov. How to backdoor federated learning. In AISTATS, 2020a. Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics, pp. 2938–2948. PMLR, 2020b. Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. Analyzing federated learning through an adversarial lens. In International Conference on Machine Learning, pp. 634– 643. PMLR, 2019. P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer. Machine learning with adversaries: Byzantine tolerant gradient descent. In NeurIPS, 2017. David Byrd and Antigoni Polychroniadou. Differentially private secure multi-party computation for federated learning in financial applications. In Proceedings of the First ACM International Conference on AI in Finance, pp. 1–9, 2020. Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konecnˇ y, H Brendan McMa- ` han, Virginia Smith, and Ameet Talwalkar. Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097, 2018. Xiaoyu Cao and Neil Zhenqiang Gong. Mpaf: Model poisoning attacks to federated learning based on fake clients. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3396–3404, 2022. Xiaoyu Cao and Neil Zhenqiang Gong. Mpaf: Model poisoning attacks to federated learning based on fake clients. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3396–3404, 2022. Xiaoyu Cao, Zaixi Zhang, Jinyuan Jia, and Neil Zhenqiang Gong. Flcert: Provably secure federated learning against poisoning attacks. IEEE Transactions on Information Forensics and Security, 17: 3691–3705, 2022. Mingqing Chen, Rajiv Mathews, Tom Ouyang, and Franc¸oise Beaufays. Federated learning of outof-vocabulary words. arXiv preprint arXiv:1903.10635, 2019. Y. Chen, L. Su, and J. Xu. Distributed statistical machine learning in adversarial settings: Byzantine gradient descent. ACM on Measurement and Analysis of Computing Systems, 1(2):1–25, 2017. Alexander Chowdhury, Hasan Kassem, Nicolas Padoy, Renato Umeton, and Alexandros Karargyris. A review of medical federated learning: Applications in oncology and cancer research. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I, pp. 3–24. Springer, 2022. M. Fang, X. Cao, J. Jia, and N. Gong. Local model poisoning attacks to Byzantine-robust federated learning. In USENIX Security, 2020. B. Feng, L. Qin, Z. Zhang, Y. Ding, and S. Chu. ZEN: An optimizing compiler for verifiable, zero-knowledge neural network inferences. 2021. Cryptology ePrint Archive. Grant S Fletcher. Clinical epidemiology: the essentials. Lippincott Williams & Wilkins, 2019. R. Freivalds. Probabilistic machines can use less running time. In IFIP Congress, 1977. Shuhao Fu, Chulin Xie, Bo Li, and Qifeng Chen. Attack-resistant federated learning with residualbased reweighting. arXiv preprint arXiv:1912.11464, 2019. Clement Fung, Chris JM Yoon, and Ivan Beschastnikh. The limitations of federated learning in sybil settings. In RAID, pp. 301–316, 2020. S. Goldwasser, S. Micali, and C. Rackoff. The knowledge complexity of interactive proof systems. SIAM Jour. on Comp., 18(1):186–208, 1989. J. Groth. On the size of pairing-based non-interactive arguments. In Eurocrypt, 2016. Rachid Guerraoui, Sebastien Rouault, et al. The hidden vulnerability of distributed learning in ´ byzantium. In International Conference on Machine Learning, pp. 3521–3530. PMLR, 2018. S. Han, H. Wang, J. Wan, and J. Li. An iterative scheme for leverage-based approximate aggregation. In IEEE ICDE, 2019. Shanshan Han, Baturalp Buyukates, Zijian Hu, Han Jin, Weizhao Jin, Lichao Sun, Xiaoyang Wang, Chulin Xie, Kai Zhang, Qifan Zhang, et al. Fedmlsecurity: A benchmark for attacks and defenses in federated learning and federated llms. arXiv preprint arXiv:2306.04959, 2023. Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Franc¸oise Beaufays, Sean Augenstein, Hubert Eichner, Chloe Kiddon, and Daniel Ramage. Federated learning for mobile ´ keyboard prediction. arXiv preprint arXiv:1811.03604, 2018. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778, 2016. L. He, S. P. Karimireddy, and M. Jaggi. Byzantine-robust decentralized learning via self-centered clipping. 2022. Available on arXiv:2202.01545. Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, and Tianyi Chen. Cafe: Catastrophic data leakage in vertical federated learning. Advances in Neural Information Processing Systems, 34:994–1006, 2021. Sai Praneeth Karimireddy, Lie He, and Martin Jaggi. Byzantine-robust learning on heterogeneous datasets via bucketing. arXiv preprint arXiv:2006.09365, 2020. Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, G. Edward Suh, Moinuddin K. Qureshi, and Hsien-Hsin S. Lee. Cocktail party attack: Breaking aggregation-based privacy in federated learning using independent component analysis. In International Conference on Machine Learning, 2022. URL https://api.semanticscholar.org/CorpusID: 252211968. Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay apa Reddi, and Michael Mitzenmacher. Gradient disaggregation: Breaking privacy in federated learning by reconstructing the user participant matrix. In International Conference on Machine Learning, pp. 5959–5968. PMLR, 2021. S. Lee, H. Ko, J. Kim, and H. Oh. vCNN: Verifiable convolutional neural network based on zksnarks. 2020. Cryptology ePrint Archive. David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, and Joseph Dureau. Federated learning for keyword spotting. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6341–6345, 2019. Suyi Li, Yong Cheng, Wei Wang, Yang Liu, and Tianjian Chen. Learning to detect malicious clients for robust federated learning. arXiv preprint arXiv:2002.00211, 2020. T. Liu, X. Xie, and Y. Zhang. ZkCNN: Zero knowledge proofs for convolutional neural network predictions and accuracy. In ACM CCS, 2021. A. Lyon. Why are normal distributions normal? The British Journal for the Philosophy of Science, 65(3):621–649, 2014. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pp. 1273–1282. PMLR, 2017a. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pp. 1273–1282. PMLR, 2017b. J. Osborne. Improving your data transformations: Applying the Box-Cox transformation. Practical Assessment, Research, and Evaluation, 15(1):12, 2010. Mustafa Safa Ozdayi, Murat Kantarcioglu, and Yulia R Gel. Defending against backdoors in federated learning with robust learning rate. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pp. 9268–9276, 2021. Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui. Robust aggregation for federated learning. IEEE Transactions on Signal Processing, 70:1142–1154, 2022. Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Franc¸oise Beaufays. Federated learning for emoji prediction in a mobile keyboard. arXiv preprint arXiv:1906.04329, 2019. M. Rosenblatt. A central limit theorem and a strong mixing condition. National Academy of Sciences, 42(1):43–47, 1956. R. M. Sakia. The Box-Cox transformation technique: A review. Journal of the Royal Statistical Society: Series D, 41(2):169–178, 1992. E. B. Sasson, A. Chiesa, C. Garman, M. Green, I. Miers, E. Tromer, and M. Virza. Zerocash: Decentralized anonymous payments from bitcoin. In IEEE S&P, 2014. Jingwei Sun, Ang Li, Louis DiValentin, Amin Hassanzadeh, Yiran Chen, and Hai Li. Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective. Advances in Neural Information Processing Systems, 34:12613–12624, 2021. Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan. Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963, 2019. Circom Contributors. Circom zkSNARK ecosystem, 2022. https://github.com/iden3/circom. Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy, and Ling Liu. Data poisoning attacks against federated learning systems. In European Symposium on Research in Computer Security, pp. 480– 501. Springer, 2020. Richard Tomsett, Kevin Chan, and Supriyo Chakraborty. Model poisoning attacks against distributed machine learning systems. In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, volume 11006, pp. 481–489. SPIE, 2019. H. Wang, K. Sreenivasan, S. Rajput, H. Vishwakarma, S. Agarwal, J. Sohn, K. Lee, and D. Papailiopoulos. Attack of the tails: Yes, you really can backdoor federated learning. In NeurIPS, 2020. Jianhua Wang. Pass: Parameters audit-based secure and fair federated learning scheme against free rider. arXiv preprint arXiv:2207.07292, 2022. S. Weisberg. Yeo-Johnson power transformations. 2001. Available at https://www.stat.umn.edu/arc/yjpower.pdf. Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta. SLSGD: Secure and Efficient Distributed Ondevice Machine Learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 213–228. Springer, 2020. H. Yang, X. Zhang, M. Fang, and J. Liu. Byzantine-resilient stochastic gradient descent for distributed learning: A Lipschitz-inspired coordinate-wise median approach. In IEEE CDC, 2019. Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett. Byzantine-robust distributed learning: Towards optimal statistical rates. In International Conference on Machine Learning, pp. 5650–5659. PMLR, 2018. Zaixi Zhang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. Implementation of fldetector. https://github.com/zaixizhang/FLDetector, 2022a. Zaixi Zhang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. Fldetector: Defending federated learning against model poisoning attacks via detecting malicious clients. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2545–2555, 2022b. Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Joseph Gonzalez, Kannan Ramchandran, and Prateek Mittal. Neurotoxin: Durable backdoors in federated learning. In International Conference on Machine Learning, 2022c. URL https: //api.semanticscholar.org/CorpusID:249889464. A APPENDIX A.1 PROOF OF THE RANGE OF PPV B ZKP IMPLEMENTATION B.1 CHOICE OF THE ZKP SYSTEM In our implementation, we use the Groth16 (Groth, 2016) zkSNARK scheme implemented in the Circom library (Circom Contributors, 2022) for all the computations described above. We chose this ZKP scheme because its construction ensures constant proof size (128 bytes) and constant verification time. Because of this, Groth16 is popular for blockchain application due to small on-chain computation. There are other ZKP schemes based on different construction that can achieve faster prover time (Liu et al., 2021), but the proof size is too big and verification time is not constant, which is a problem if verifier lacks computational power. The construction of a ZKP scheme that is efficient for both prover and verifier is still an open research direction. B.2 SUPPLEMENTARY EXPERIMENTS FOR IMPORTANCE LAYER

Zero-Knowledge-Proof-Based Anomaly Detection: Conclusion & References

This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhipeng Wang, Department of Computing, Imperial College London; (2) Nanqing Dong Department of Computer Science, University of Oxford; (3) Jiahao Sun, Data Science Institute, Imperial College London; (4) William Knottenbelt, Department of Computing, Imperial College London. TABLE OF LINKS Abstract & Introduction Related Work & Preliminaries Methodology Theoretical and Empirical Analysis Results Conclusion & References Conclusion We present a novel and pioneering approach called zkFL, which utilizes ZKPs to ensure a trustworthy aggregation process on the centralized aggregator. Through rigorous theoretical analysis, we establish that zkFL effectively addresses the challenge posed by a malicious aggregator during the model aggregation phase. Moreover, we extend zkFL to a blockchain-based system, significantly reducing the verification burden on the clients. The empirical analysis demonstrates that our design achieves superior levels of security and privacy compared to traditional FL systems while maintaining a favorable training speed for the clients. These results showcase the practical feasibility and potential advantages of zkFL
and its blockchain-based version in real-world applications. Acknowledgments This work was supported in part by the FLock Research Grant. References Diego F Aranha, Emil Madsen Bennedsen, Matteo Campanelli, Chaya Ganesh,
Claudio Orlandi, and Akira Takahashi. Eclipse: enhanced compiling method for
pedersen-committed zksnark engines. In IACR International Conference on PublicKey Cryptography, pages 584–614. Springer, 2022. Karim Baghery, Markulf Kohlweiss, Janno Siim, and Mikhail Volkhov. Another
look at extraction and randomization of groth’s zk-snark. In Financial Cryptography and Data Security: 25th International Conference, FC 2021, Virtual Event,
March 1–5, 2021, Revised Selected Papers, Part I 25, pages 457–475. Springer, Nir Bitansky. Verifiable random functions from non-interactive witnessindistinguishable proofs. Journal of Cryptology, 33(2):459–493, 2020. Joseph Bonneau, Andrew Miller, Jeremy Clark, Arvind Narayanan, Joshua A Kroll, and Edward W Felten. Sok: Research perspectives and challenges for bitcoin and cryptocurrencies. In 2015 IEEE Symposium on Security and Privacy, pages 104– 121. IEEE, 2015. Sean Bowe, Jack Grigg, and Daira Hopwood. Recursive proof composition without a trusted setup. Cryptology ePrint Archive, 2019. Lukas Burkhalter, Hidde Lycklama, Alexander Viand, Nicolas K¨uchler, and Anwar Hithnawi. Rofl: Attestable robustness for secure federated learning. arXiv preprint arXiv:2107.03311, 2021. Xiaoyu Cao and Neil Zhenqiang Gong. Mpaf: Model poisoning attacks to federated learning based on fake clients. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3396–3404, 2022. Nanqing Dong, Zhipeng Wang, Jiahao Sun, Michael Kampffmeyer, Yizhe Wen, Shuoying Zhang, William Knottenbelt, and Eric Xing. Defending against malicious behaviors in federated learning with blockchain. arXiv preprint arXiv:2307.00543, 2023. Haohua Duan, Liyao Xiang, Xinbing Wang, Pengzhi Chu, and Chenghu Zhou. A new zero knowledge argument for general circuits and its application. IEEE Transactions on Information Forensics and Security, 2023. Juan Garay and Aggelos Kiayias. Sok: A consensus taxonomy in the blockchain era. In Topics in Cryptology–CT-RSA 2020: The Cryptographers’ Track at the RSA Conference 2020, San Francisco, CA, USA, February 24–28, 2020, Proceedings, pages 284–318. Springer, 2020. Oded Goldreich and Yair Oren. Definitions and properties of zero-knowledge proof systems. Journal of Cryptology, 7(1):1–32, 1994. Jens Groth. On the size of pairing-based non-interactive arguments. In International Conference on the Theory and Applications of Cryptographic Techniques, pages 305–326. Springer, 2016. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770–778, 2016. Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In CVPR, pages 4700–4708, 2017. Huawei Huang, Wei Kong, Sicong Zhou, Zibin Zheng, and Song Guo. A survey of state-of-the-art on blockchains: Theories, modelings, and tools. ACM Computing Surveys (CSUR), 54(2):1–42, 2021. Wael Issa, Nour Moustafa, Benjamin Turnbull, Nasrin Sohrabi, and Zahir Tari. Blockchain-based federated learning for securing internet of things: A comprehensive survey. ACM Computing Surveys, 55(9):1–43, 2023. Peter Kairouz, H Brendan McMahan, Brendan Avent, Aur´elien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2):1–210, 2021. Joe Kilian. A note on efficient zero-knowledge proofs and arguments. In Proceedings of the twenty-fourth annual ACM symposium on Theory of computing, pages 723– 732, 1992. Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim. Blockchained ondevice federated learning. IEEE Communications Letters, 24(6):1279–1283, 2019. Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. Yijing Li, Xiaofeng Tao, Xuefei Zhang, Junjie Liu, and Jin Xu. Privacy-preserved federated learning for autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 23(7):8423–8434, 2021. Tianyi Liu, Xiang Xie, and Yupeng Zhang. Zkcnn: Zero knowledge proofs for convolutional neural network predictions and accuracy. In ACM SIGSAC Conference on Computer and Communications Security, pages 2968–2985, 2021. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In AISTATS, pages 1273–1282. PMLR, 2017. Silvio Micali, Michael Rabin, and Salil Vadhan. Verifiable random functions. In 40th annual symposium on foundations of computer science (cat. No. 99CB37039), pages 120–130. IEEE, 1999. Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system. Decentralized business review, 2008. Torben Pryds Pedersen. Non-interactive and information-theoretic secure verifiable secret sharing. In Annual international cryptology conference, pages 129–140. Springer, 1991. Youyang Qu, Md Palash Uddin, Chenquan Gan, Yong Xiang, Longxiang Gao, and John Yearwood. Blockchain-enabled federated learning: A survey. ACM Computing Surveys, 55(4):1–35, 2022. Eli Ben Sasson, Alessandro Chiesa, Christina Garman, Matthew Green, Ian Miers, Eran Tromer, and Madars Virza. Zerocash: Decentralized anonymous payments from bitcoin. In 2014 IEEE Symposium on Security and Privacy, pages 459–474. IEEE, 2014. Xiaoqiang Sun, F Richard Yu, Peng Zhang, Zhiwei Sun, Weixin Xie, and Xiang Peng. A survey on zero-knowledge proof in blockchain. IEEE network, 35(4):198– 205, 2021. Zhipeng Wang, Stefanos Chaliasos, Kaihua Qin, Liyi Zhou, Lifeng Gao, Pascal Berrang, Benjamin Livshits, and Arthur Gervais. On how zero-knowledge proof blockchain mixers improve, and worsen user privacy. In Proceedings of the ACM Web Conference 2023, pages 2022–2032, 2023. Jiasi Weng, Jian Weng, Gui Tang, Anjia Yang, Ming Li, and Jia-Nan Liu. pvcnn: Privacy-preserving and verifiable convolutional neural network testing. IEEE Transactions on Information Forensics and Security, 18:2218–2233, 2023. Jiasi Weng, Jian Weng, Jilian Zhang, Ming Li, Yue Zhang, and Weiqi Luo. Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Transactions on Dependable and Secure Computing, 18(5):2438– 2455, 2019. Gavin Wood. Ethereum: A secure decentralised generalised transaction ledger. Ethereum project yellow paper, 151:1–32, 2014. Juncen Zhu, Jiannong Cao, Divya Saxena, Shan Jiang, and Houda Ferradi. Blockchain-empowered federated learning: Challenges, solutions, and future directions. ACM Computing Surveys, 55(11):1–31, 2023.

Zero Knowledge Proof based Gradient Aggregation for Federated Learning: Conclusion & References

As an open-source enthusiast, I am always interested in the functioning of the open-source ecosystem and the public distribution of software. I recently attended MumbaiFOSS, a conference organized by FOSS United and FOSSEE to promote Free and Open Source Software in Mumbai. The FOSSMumbai conference happened yesterday on the 11th of March 2023, at IIT Bombay. In this blog, I share my experiences and takeaways from the conference, including informative talks on building reliable APIs, digital personal data protection, public policies, and the importance of open-source beyond just yearly programs and swag. About MumbaiFOSS is a collective effort of FOSS United and FOSSEE to revive the spirit of Free and Open source software in Mumbai. MumbaiFOSS is a non-profit foundation that promotes a ecosystem in India. It offers funding and grants to FOSS projects, organizes events to empower open-source developers, and hosts a not-for-profit learning platform . FOSS United Free and Open Source Software Mon School The , or simply , is a project pioneered in IIT Bombay that promotes using Free and Libre Open Source Software (FLOSS) in academics and research. It conducts a plethora of activities to improve education and research quality. Free/Libre and Open Source Software for Education FOSSEE Foss United partnered with FOSSEE to bring together developers, policymakers, and open-source enthusiasts for a conference. MumbaiFOSS was about FOSS (of course!), its issues and trends, and FOSS policies. This was a complete package of happiness for someone who has just scratched the surface of the open-source monolith. Prologue My two college mates accompanied me, coincidentally having the same name - Pratik. The journey from the main gate to the VMCC auditorium felt like a lifetime, but we arrived on time. We were welcomed with a yummy breakfast until the conference started. I expected at least 100 people to be present, but to my surprise, the numbers were relatively low, though satisfactory. Traveling in Mumbai on early morning weekends has the power to change people's desires. The Conference, in Brief Vishal Arya, the swiss army knife of FOSS United, and Riya Mathew, community manager of FOSS United, made the opening note. They told us the story behind FOSS United, the purpose of MumbaiFOSS, and their journey from the beginning. Before the opening note, Prof. Kannan Moudgalaya of IIT Bombay gave an overview of FOSSEE, Spoken tutorials, and projects FOSSEE promotes. Apart from this, we had nine talks, one-panel discussion, and one open discussion throughout the day. From informative talks on building reliable APIs and digital personal data protection to a fireside chat on tech policy, this conference offered a complete package of happiness for anyone interested in the open-source monolith. Read on to discover more about MumbaiFOSS and the importance of open-source beyond just yearly programs and swag. Building Reliable APIs with Apache APISIX by Navendu Pottekkat by Ravi Dwivedi and Arun Mathai Prav: a community-owned chat app by Sumanto Kar and Nagesh Karmali eSim developed by FOSSEE by Amogh Lele Demystifying WebRTC by Ashish Sahni Data Fabric by Poruri Sai Rahul Let's discuss the Digital Personal Data Protection Bill 2022 by Bhavesh Bhatt Transforming Web Apps with Machine Learning by Venkatesh Hariharan FOSS United's Public Policy Initiatives by Kaustubh Maske Patil Self Hosting 101 - Reasons to self-host and nifty tools - a panel discussion between Vaishnavi, Manish, Saurav, Ashish, Dhiraj, Rohit, and Anas Khan Open Source: Supporting, Contributing, and Evangelising by Kailash Nadh and Venkatesh Hariharan Fireside chat: Why engineers should care about tech policy? Venky's talk on FOSS United's Public Policy Initiatives was my favorite. Hearing him talk about his experience, with interesting stories and analogies, was indeed insightful. Fireside Chat This was the main event of the conference. It was a half an hour discussion between Kailash Nadh, Rishabh Rushabh Mehta, and Venkatesh Hariharan. The discussion was mainly focused on new tech policies and our stand on them. How tech policies are changing, why we should care about it, how politicians and political agendas shape these policies, and how we can take steps towards making an impact were a few things that were discussed. It's important to have more technologists involved in public policies than lawyers. And, I Met Kailash Nadh! Kailash Nadh has been CTO of Zerodha since 2013. I was fascinated when I read that Zerodha's engineering team only comprises 33 people, and nobody has left the team in all these years for better opportunities. Damn, I was excited to meet this man! As everybody started leaving the auditorium after the conference, I saw him talking to Rushabh and a few team members. I went there and asked him for a photo together, and he gently agreed. I couldn't stop but express my admiration towards his work at Zerodha and how much I admire his startup. He thanked me, and I went on cloud nine. Afterthoughts Open source is a lot more than I used to think. It is beyond a few yearly open-source programs, goodies/swags, and adding certificates on LinkedIn. It is beyond contributing to projects backed by large corporations and projects that enhance developer/web experience. It is also about being free from proprietary software, empowering and encouraging the government and societies to use FOSS, reaching more students, educating them, and many more. MumbaiFOSS was the first conference I attended. And it was absolutely fantastic! Everything was planned and managed so well that I couldn't find any issues to rant on. And the food was tasty, too! Kudos to the operations and management team for pulling it off so well. Epilogue I plan to attend more such conferences. I searched for some while going back home, and I might go there if everything aligns well. Of course, I will update you all! I am most active on , where I post such updates. Twitter You can also subscribe to my E-mail newsletter to have my next blog in your inbox! Until then, keep contributing! 🌿 Also published here

MumbaiFOSS: My Experience, Thoughts, and Opinions

References
Zcash - Aleo - Zcash protocol specification Zexe protocol specification Zcash 1. About Zcash ? A to learn about Zcash. short video Features: Anonymous version of BTC, UTXO-like model Can only be used for payment scenarios with no programmability 2. Main Concepts Note: Zcash has undergone several and we only focus on the latest version. This article mainly introduces the core concepts of Zcash. protocol upgrades 2.1 Key Components sk : spend key, generated uniformly ask: spend authorizing key ak : spend validating key nk : nullifier deriving key rivk : randomness dk: diversifier key ivk : private key / incoming viewing key ovk : outgoing viewing key pkd : transmission key Spending key: secret key Full viewing key: decrypt the transaction context Incoming viewing key Outgoing viewing key Shielded payment address: should be different each time You can learn about the computation methods of thesekeys in the Zcash protocol specification: section 4.2.3, page 36. 2.2 Notes Note is the basic unit of the Zcash protocol, similar to the UTXO of BTC; in Zcash, the inputs and outputs of all transactions are notes. Of course, Zcash also supports non-anonymous transactions, identical to Bitcoin’s transaction model. Therefore, in order to further understand Zcash, you must know the data structure of note first: {d,pk_d}: address information of the owner of the note v : the corresponding amount of the note {ρ,ψ}: compute the random number of the nullifier rcm : being used to compute the random number of the note commitment In the Zcash protocol, the note cannot be made public due to the requirement of privacy. Therefore, the corresponding commitment needs to be computed to represent the note. The computation method is as follows: 2.3 Action Transfer A transaction may contain multiple action transfers, and each action transfer will spend the old note and generate a new note. The data structure is as follows: cv^{net}: being used for the balance check for action transfer in the form of binding signature without being included in the zk proof rt^{Orchard}: the treestate of the previous block, which is used to verify the spent note validity nf: the logo of the spent note, which is used to prevent the double spend rk: being used to verify that the spender has the right to the spend the note in the form of spendAuthSig signature verification cm_x: commitment of generating a new note epkepk temporary public key, which is used to decrypt the noteplaint information of the new note C^{enc},C^{out}: the encrypted ciphertext of the new note enbaleSpends, enableOutputs: indicating the type of the current action transfer π: zk proof 2.4 Action Statement The public inputs are: The privacy inputs are: The statements to prove: Spent note integrity is uniquely bound to the noteplaint Spent note validity, existence proof of the cm tree Value commitment integrity is uniquely bound to rcv , old value, and new value Nullifier integrity prevents double spend and maintains a spent note set Spent note legitimacy Address integrity New note integrity Flag legitimacy 2.5 Transaction Constructions and Examples 2.5.1 Transaction construction The entire transaction structure consists of four parts: Public info (1 - 5) Transparent transactions info (6 - 9) Sapling transactions info (10 - 16) Orchard transaction info (17 - 25) 2.5.2 From Transparent to Shield The Orchard protocol includes two types of addresses, transparent address (TA) and shield address (SA). Generally, in order to execute a private transaction, it is necessary to transfer from TA to SA first. The corresponding transaction structure should be: Public info (1 - 5) Transparent transactions info (6 - 9) tx_in _* : actual value tx_out _* : default value Sapling transactions info (10 - 16) All : default value Orchard transaction info (17 - 25) All: actual value 2.5.3 From Shield to Shield The Orchard protocol includes two types of addresses, transparent address (TA) and shield address (SA). Generally, in order to execute a private transaction, it is necessary to transfer from TA to SA first. The corresponding transaction structure should be: Public info (1 - 5) Transparent transactions info (6 - 9) All : default value Sapling transactions info (10 - 16) All : default value Orchard transaction info (17 - 25) All: actual value 2.5.4 From Shield to Transparent The Orchard protocol includes two types of addresses, transparent address (TA) and shield address (SA). Generally, in order to execute a private transaction, it is necessary to transfer from TA to SA first. The corresponding transaction structure should be: Public info (1 - 5) Transparent transactions info (6 - 9) tx_in _* : default value tx_out _* : actual value Sapling transactions info (10 - 16) All : default value Orchard transaction info (17 - 25) All: actual value 2.6 How is Privacy Achieved? The generated note is represented by cm, and the spent note is represented by nf. There is no connection between nf and cm. Therefore, no one can know in which transaction any generated note was spent. Unlinkable Private The transaction information does not contain the sender address and the spendAuthSig is a one-time signature (it is different every time, so the public key is different, rk) Sender address: The transaction does not contain the receiver’s address and the new note plaint is encrypted by the receiver’s public key (the receiver’s private address is also one-time) Receiver address: Hide the note in the form of pedersen commitment, and ensure the balance attribute of the transaction by bindsig Value: Aleo 1. The Similarities and Differences With Zcash Zcash can only execute private transactions based on the OUTX model and have no programmability; therefore, the main difference between Aleo and Zcash is privacy programmability; and the similarity is that they both support privacy attributes (transaction privacy is not only limited to assets). 2. Aleo vs Zcash 2.1 Unit Unlike the note of Zcash, the basic operation unit of Aleo is record (UTXO in BTC). Find the main differences between the two below: Although the names of the specific parameters are different, there is a corresponding relationship between the two from the functional point of view: corresponding to the address information of the note owner, commitment-related information, nf/sn-related information, and value-related information, respectively. Therefore, the structures of the two are quite similar; the main differences exist in the birth predicate and death predicate of the record, which are two Boolean-type functions, representing the conditions that need to be met when a record is in the birth(generate) and death(spend) stages. This part supports user-defined, so it’s programmable. 2.2 Transaction Construction There are still some resemblances compared with the main construction of the transaction of Zcash (2.5.1): The corresponding serial number sn of the spent record, which is represented by nf in Zcash, is unique The commitment of the new record The plaint of the new record, including owner information, corresponding birth/death predicate, etc 2.3 Prover Statement You need to prove: The old record validity The old record legitimacy (have the right to spend the record) The new record validity The birth/death predicate validity (similar to the balance check in Zcash) 3. More 3.1 Technologies not mentioned From the perspective of the paper, the privacy design of Aleo 's programmable privacy design is more similar to the earlier Zcash white paper ( ), the similar key structure, the similar note structure and the similar name (nf is called sn in zerocash, serial number). This article makes a comparison based on the latest paper of Zcash and the ZEXE of Aleo. Although there are differences in specific details, such as the key structure and the specific cryptographic methods used, the high-level design is generally the same. In addition to the technical details described above, there are still some other technical details that have not been mentioned yet, such as the delegate prover scheme, zero-knowledge proof algorithm, recursion/aggregation scheme, and so on. People interested in them can study further. zerocash 3.2 Why are they all UTXO -based, not account-based? Remark 2.3 (Zexe protocol specification: section 2.3, page 11)

Private Transactions: Insights to the Design Principles in ZCash and Aleo

Transacciones privadas: información sobre los principios de diseño en ZCash y Aleo

In 2009, Bitcoin was launched making it the first-ever peer-to-peer digital money; the first financial application built on blockchain technology. One could argue that started with BTC in 2009. DeFi (Decentralized Finance) is a growing ecosystem of actual, working protocols and applications, which are delivering value to several thousands of users, and transacting the equivalent of hundreds of millions of dollars in digital assets, every day. DeFi The very foundations of a new global financial system are being laid with applications that enable everything from making simple money transfers and payments to lending, borrowing, trading, portfolio management, and even insurance contracts. With decentralized finance on the blockchain, you can do anything that you are currently doing with your traditional finance system, without giving a commission or the requirement to go through middlemen or brokers. But DeFi lacks one important feature which is often overlooked. Sounds exciting, doesn’t it? PRIVACY! But wait….. Why is privacy important? is necessary for ensuring freedom on the internet. When your transactions are being watched — or when your transaction history is available to be known — a person isn't totally free to make their own decisions. Privacy your account number or receiving address, one can check your entire history of transactions and even what crypto assets you hold. Just by knowing To save you from this tragedy, FinFarm brings you a detailed comparison between top privacy-focused cryptocurrencies. Bytecoin (BCN) Launched back in 2012, described itself as a private, decentralized cryptocurrency with an open-source code and was the first cryptocurrency designed to protect user privacy. It is a cryptocurrency that works on the blockchain operated by a multitude of nodes around the world and uses Ring signatures, which have since also been incorporated into other similar coins (see below), to mix different outputs of similar value into one transaction, making it almost impossible to deduce where the money came from. On the Bytecoin blockchain, all addresses are obscured. Bytecoin (BCN) Bytecoin allows users to generate numerous unlinkable one-time addresses from a single set of keys. This makes it nearly impossible to establish a connection between transactions or addresses. Bytecoin does not support smart contracts, and the high risk of user privacy being attacked is still prevalent, even though it adopts a decentralized mixed currency technology. Dash (DASH) Dash, whose name is derived from the term " was in January 2014 as a fork of Litecoin (LTC). Dash utilizes a centralized currency mixing scheme where users simply transfer sums of money to multiple addresses several times; to mix and obfuscate transactional history. digital cash," launched The centralized currency mixing scheme has high applicability in various cryptocurrency systems; however, the existing solution requires the sender and receiver to mix coins online. If the sender and receiver can't reach an agreement on the amount of mixed currency, the transaction must be postponed. There is a common delay problem with Dash and the currency mixer is centrally deployed. Dash does not support smart contracts, and the third-party mixed-currency provider mechanism relies on the credibility of the third party, which encounters unpredictable risks. In recent years, Dash's development has focused on attempting to make Dash a payment tool with strong circulation value instead of the emphasis on privacy protection. Verge (XVG) Verge was first in October 2014 as ‘ ’, a fork of (PPC). In February 2016, it was Verge in order to for easier mass-market adoption and to distinguish itself from (DOGE), with which it has no direct connection. Verge Currency uses the Proof of Work (POW) mining principle. It is also one of a few cryptocurrencies to have multi-algorithm support. Verge has 5 different hash functions: Scrypt, X17, Lyra2rev2, myr-groestl, and blake2s. Such design is intended to allow for more equal access to the mining ecosystem. launched DogeCoinDark Peercoin renamed rebrand Dogecoin Verge’s Dual-Key Stealth Addressing allows senders to create an unlimited number of one-time destination addresses on behalf of the recipient without any interaction between the parties. Stealth addresses are a method by which additional obfuscation can be implemented to further protect the receiving party when transacting with Verge. When multiple users send funds to a stealth address, rather than these transactions appearing on the blockchain as multiple payments to the same address, they instead appear as multiple payments going to different addresses. Smart Contracts on Verge are under development. Monero (XMR) Monero was launched in April 2014 and features the “proof of work” (POW) consensus mechanism to enable the creation or minting of the XMR asset. Proof of work is often also called ‘mining’ and Monero’s mining algorithm; , is an AES intensive and memory-consuming operation; which is designed to encourage mining with the central processing unit (CPU) of a computer. RandomX Monero uses the Ring Confidential Transactions algorithm for its privacy encryption method. The method mixes the signer's public key with another public key set and then signs the message, making it impossible for intruders to distinguish which public key corresponds to the actual signer; therefore, protecting the user's real identity. Monero's participating users do not need to communicate with other participating nodes, they can participate in mixed-currency by themselves, providing effective protection measures for commonly distributed denial-of-service (DDOS) attacks and information disclosure in the decentralized mixed-currency mechanism. mixed-currency Monero does not support smart contracts, and the high risk of a user’s privacy being attacked is still present, even though it adopts its decentralized mixed currency technology. Users need to rely on the public key of other users when using . If other users are malicious, the problem of users’ privacy disclosure may arise. There are a few cases that claim to trace transactions on the Monero network but we neither confirm nor contradict those claims. Ring Confidential Transactions technology Zcash (ZEC) was launched in October 2016 and was originally based on Bitcoin’s codebase. It is a cryptocurrency that uses encryption technology to provide users with optional privacy protection. Originally named , using the ZeroCash protocol in November 2013, this was forked into Zcash cryptocurrency in 2016 with the specific purpose to add a development fund that is taken from block rewards. Zcash ZeroCoin Zcash payments are published on the blockchain and users can utilize optional privacy functions to hide the sender, receiver, and amount of transactions on the blockchain. Only those who have the key can see the contents of the transaction. The user has full control and can choose to provide the key to others to prove payment for auditing purposes. Zcash now uses a called to encrypt user information. Zk-SNARKs is an encryption method based on pure mathematical theory. The encryption method is self-contained, it has the advantage of not depending on an external operating environment; therefore, has a wider range of application scenarios. ZeroKnowledge Proof technology zk-SNARKs Since Zcash uses the same underlying architecture as Bitcoin's network, it can only support simple transactions, similar to the Bitcoin network with a pre-set privacy protection mechanism. Beam (BEAM) Beam is a scalable, privacy-oriented cryptocurrency based on the elegant breakthroughs of the , which completely conceals the values and metadata of transactions in a printable way, which also reduces bloating on the blockchain. In addition to enhanced privacy and fungibility, this allows for much greater scalability. - Beam takes up far less disk space than other privacy-oriented cryptocurrencies. mimblewimble protocol In layman’s terms The cryptographic protocols used in Beam require no trusted setup and Beam uses the BeamHashIII Proof-of-Work algorithm; a memory-hard mathematical challenge that is designed to encourage GPU miners. Like Bitcoin, scarcity is ensured by periodic halving. Complex transactions are possible, such as escrows, time-locked transactions, atomic swaps, and much more. Beam has further evolved from the mimblewimble protocol to solidify best-in-class privacy-preserving mechanisms with dandelion++ and lelantus-MW. Beam supports in-wallet decentralized applications (DAPPS) and smart contract deployment, which are named ‘Shaders’. Anyone can create their on Beam, and even mint My Hackernoon profile image is an NFT that I bought on Beam and by this NFT you can own privacy tokens Confidential NFT’s. neither track my assets nor other NFTs in my portfolio. And one of the most of Beam is that you do not have to pay gas fees on failed transactions! YES, No gas fees for failed transactions. underrated features Recently, an NFT public sale on Ethereum went horribly wrong and users accumulated more than $145million in failed transaction fees. This would never happen on Beam as failed transactions do not incur gas fees. Here is a Detailed Comparision for you: Parameters ByteCoin Dash Zcash Monero Verge Beam Algorithm Cryptonight X11 Equihash RandomX Scrypt, X17, Lyra2rev2, MYR groestl, and Blake2S Beam Hash III Scalability Better in terms of Blockchain size May implement PoS to improve scalability Blockchain is heavier than Bitcoin Worst Scalability in terms of blockchain size Better than other privacy coins Excellent scalability, lighter than Bitcoin Auditability Can be done but with restrictions Can be done but with restrictions Can be done but with restrictions Not useful Can be done Can be done and optional for a user Smart Contracts Support No No No No Under Development Yes Dapps No No No No Upcoming Yes Gas Fees for failed transactions Yes Yes Yes Yes Yes No DeFi Ecosystem (DEX, Lending & Borrowing ,etc) No No No No Upcoming Upcoming Create your own privacy tokens No No No No Upcoming Yes NFTs No No No No No Yes

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