Privatyze

privatyze

Cypherpunks Write Code: The Mysterious Satoshi Nakamoto

সাইফারপাঙ্কস কোড লিখুন: রহস্যময় সাতোশি নাকামোটো

Cypherpunks schreiben Code: Der mysteriöse Satoshi Nakamoto

Código de escritura de Cypherpunks: El misterioso Satoshi Nakamoto

Cypherpunks écrit du code : le mystérieux Satoshi Nakamoto

साइफरपंक्स कोड लिखें: रहस्यमय सातोशी नाकामोतो

サイファーパンクがコードを書く: 謎のサトシ・ナカモト

사이퍼펑크의 코드 작성: 신비한 나카모토 사토시

Cypherpunks escrevem código: o misterioso Satoshi Nakamoto

Шифропанки пишут код: загадочный Сатоши Накамото

Şifrepunklar Kod Yazıyor: Gizemli Satoshi Nakamoto

Cypherpunks viết mã: Satoshi Nakamoto bí ẩn

密码朋克编写代码：神秘的中本聪

Differentially Private Data Science - What, Why And How?

Authors:
(1) Hamid Bateni, Nobitex Labs (hamid@europe.com);
(2) Keyvan Kambakhsh, Nobitex Labs (keyvankambakhsh@gmail.com). Table of Links Abstract and 1 Introduction 2 Proof of Liability and 2.1 Commitment 2.2 Merkle Tree 2.3 Leaves Structure 2.4 Proof Statement 3 Proof of Reserve 3.1 Ethereum 3.2 Bitcoin 4 Proof of Solvency 5 Future Works and References 2.4 Proof Statement In this section, we will discuss the entire proof statement of our proof of liabilities process. In simple terms, we’ve developed a commitment tree with a specific leaf structure that allows us to make the following proof statement: ”I know a Merkle tree with a public root and nodes, the leaves of which contain data about my user balances. The last right leaf in this tree contains data about the total sum of my liabilities per asset.” There are two important aspects of this proof statement: • I have a Zero-Knowledge Proof (ZKP) for this tree that demonstrates that the data in the last leaf is calculated correctly and that there is no negative balance in any leaf. This ZKP proof allows me to verify the integrity and correctness of the total liabilities without having to reveal the actual amounts.[6] • My users can download the snapshot related to their balances from the website and also get the path to verify that their balances are included in the total liabilities. This allows users to independently confirm that their balances have been taken into account in the proof of liabilities. Based on this approach, as a business, I only need to publish the Merkle root and nodes, not the leaf data. With the ZKP, I prove that my total obligations are included in the last right leaf without having to make this data public. This maintains the privacy of the total liabilities while still allowing users and auditors to verify the correctness of the proof of liabilities. In the Proof of Solvency section, we will revisit the last right leaf and discuss how it aids us in maintaining the privacy of the total liabilities while enabling efficient and correct verification. Note: In implementation, it is advisable to use a Merkle Sparse Tree. This reduces some complexity and makes the circuit process more efficient. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Hamid Bateni, Nobitex Labs (hamid@europe.com); (2) Keyvan Kambakhsh, Nobitex Labs (keyvankambakhsh@gmail.com). Authors: Authors: (1) Hamid Bateni, Nobitex Labs (hamid@europe.com); (2) Keyvan Kambakhsh, Nobitex Labs (keyvankambakhsh@gmail.com). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Proof of Liability and 2.1 Commitment 2 Proof of Liability and 2.1 Commitment 2.2 Merkle Tree 2.2 Merkle Tree 2.3 Leaves Structure 2.3 Leaves Structure 2.4 Proof Statement 2.4 Proof Statement 3 Proof of Reserve 3 Proof of Reserve 3.1 Ethereum 3.1 Ethereum 3.2 Bitcoin 3.2 Bitcoin 4 Proof of Solvency 4 Proof of Solvency 5 Future Works and References 5 Future Works and References 2.4 Proof Statement In this section, we will discuss the entire proof statement of our proof of liabilities process. In simple terms, we’ve developed a commitment tree with a specific leaf structure that allows us to make the following proof statement: ”I know a Merkle tree with a public root and nodes, the leaves of which contain data about my user balances. The last right leaf in this tree contains data about the total sum of my liabilities per asset.” There are two important aspects of this proof statement: • I have a Zero-Knowledge Proof (ZKP) for this tree that demonstrates that the data in the last leaf is calculated correctly and that there is no negative balance in any leaf. This ZKP proof allows me to verify the integrity and correctness of the total liabilities without having to reveal the actual amounts.[6] • My users can download the snapshot related to their balances from the website and also get the path to verify that their balances are included in the total liabilities. This allows users to independently confirm that their balances have been taken into account in the proof of liabilities. Based on this approach, as a business, I only need to publish the Merkle root and nodes, not the leaf data. With the ZKP, I prove that my total obligations are included in the last right leaf without having to make this data public. This maintains the privacy of the total liabilities while still allowing users and auditors to verify the correctness of the proof of liabilities. In the Proof of Solvency section, we will revisit the last right leaf and discuss how it aids us in maintaining the privacy of the total liabilities while enabling efficient and correct verification. Note: In implementation, it is advisable to use a Merkle Sparse Tree. This reduces some complexity and makes the circuit process more efficient. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Private Proof of Solvency: Proof Statement

Authors:
(1) Hamid Bateni, Nobitex Labs (hamid@europe.com);
(2) Keyvan Kambakhsh, Nobitex Labs (keyvankambakhsh@gmail.com). Table of Links Abstract and 1 Introduction 2 Proof of Liability and 2.1 Commitment 2.2 Merkle Tree 2.3 Leaves Structure 2.4 Proof Statement 3 Proof of Reserve 3.1 Ethereum 3.2 Bitcoin 4 Proof of Solvency 5 Future Works and References Abstract The ”Private Proof of Solvency” project is a groundbreaking solution in the realm of Proof of Solvency, offering a secure, efficient, and privacy-preserving method for crypto custody providers such as centralized cryptocurrency exchanges or enterprise custody providers. By leveraging the inherent state concept of every blockchain and pioneering cryptographic techniques, our approach ensures businesses can prove their reserves without revealing their transactions, addresses, or the total amount of liabilities. 1 Introduction Crypto custody providers currently face the challenge of maintaining numerous addresses for user assets. Conventional methods to create a proof of reserve require the consolidation of these assets into single or multiple known wallet addresses. Our innovative approach eliminates this process by utilizing the inherent state concept of every blockchain. The state, achieved by processing blockchain transactions on the blockchain protocol nodes, holds data such as the balance associated with an address. For instance, Ethereum maintains this state in the Merkle Patricia data structure, while Bitcoin employs a LevelDB database with a key-value structure that keeps the active Unspent Transaction Outputs (UTXOs). In Bitcoin terms, the balance represents the total active UTXOs an address holds. Our project introduces a novel process for businesses to provide proof of reserve: Create a proof of liabilities tree based on user data on the business database.


Sign a message with the private key of the addresses they want to prove reserve with.


Provide these messages as private input for our Zero-Knowledge Proof (ZKP) circuit.


Submit the output to a contract and announce their new submission for checking. By leveraging ZKP, businesses can prove their reserves without the need to reveal their transactions, addresses, or the total amount of liabilities, thereby maintaining privacy while ensuring the integrity of the process. In essence, the ”Private Proof of Solvency” project offers a robust, privacy-preserving solution that significantly enhances the Proof of Solvency process for crypto custody providers, paving the way for a more secure financial ecosystem. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Hamid Bateni, Nobitex Labs (hamid@europe.com); (2) Keyvan Kambakhsh, Nobitex Labs (keyvankambakhsh@gmail.com). Authors: Authors: (1) Hamid Bateni, Nobitex Labs (hamid@europe.com); (2) Keyvan Kambakhsh, Nobitex Labs (keyvankambakhsh@gmail.com). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Proof of Liability and 2.1 Commitment 2 Proof of Liability and 2.1 Commitment 2.2 Merkle Tree 2.2 Merkle Tree 2.3 Leaves Structure 2.3 Leaves Structure 2.4 Proof Statement 2.4 Proof Statement 3 Proof of Reserve 3 Proof of Reserve 3.1 Ethereum 3.1 Ethereum 3.2 Bitcoin 3.2 Bitcoin 4 Proof of Solvency 4 Proof of Solvency 5 Future Works and References 5 Future Works and References Abstract The ”Private Proof of Solvency” project is a groundbreaking solution in the realm of Proof of Solvency, offering a secure, efficient, and privacy-preserving method for crypto custody providers such as centralized cryptocurrency exchanges or enterprise custody providers. By leveraging the inherent state concept of every blockchain and pioneering cryptographic techniques, our approach ensures businesses can prove their reserves without revealing their transactions, addresses, or the total amount of liabilities. prove their reserves without revealing their transactions, addresses, or the total amount of liabilities. 1 Introduction Crypto custody providers currently face the challenge of maintaining numerous addresses for user assets. Conventional methods to create a proof of reserve require the consolidation of these assets into single or multiple known wallet addresses. Our innovative approach eliminates this process by utilizing the inherent state concept of every blockchain. The state, achieved by processing blockchain transactions on the blockchain protocol nodes, holds data such as the balance associated with an address. For instance, Ethereum maintains this state in the Merkle Patricia data structure, while Bitcoin employs a LevelDB database with a key-value structure that keeps the active Unspent Transaction Outputs (UTXOs). In Bitcoin terms, the balance represents the total active UTXOs an address holds. Our project introduces a novel process for businesses to provide proof of reserve: Create a proof of liabilities tree based on user data on the business database. Sign a message with the private key of the addresses they want to prove reserve with. Provide these messages as private input for our Zero-Knowledge Proof (ZKP) circuit. Submit the output to a contract and announce their new submission for checking. Create a proof of liabilities tree based on user data on the business database. Create a proof of liabilities tree based on user data on the business database. Sign a message with the private key of the addresses they want to prove reserve with. Sign a message with the private key of the addresses they want to prove reserve with. Provide these messages as private input for our Zero-Knowledge Proof (ZKP) circuit. Provide these messages as private input for our Zero-Knowledge Proof (ZKP) circuit. Submit the output to a contract and announce their new submission for checking. Submit the output to a contract and announce their new submission for checking. By leveraging ZKP, businesses can prove their reserves without the need to reveal their transactions, addresses, or the total amount of liabilities, thereby maintaining privacy while ensuring the integrity of the process. In essence, the ”Private Proof of Solvency” project offers a robust, privacy-preserving solution that significantly enhances the Proof of Solvency process for crypto custody providers, paving the way for a more secure financial ecosystem. more secure financial ecosystem. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Introducing Private Proof of Solvency

Table of Links Abstract and I. Introduction II. Preliminaries III. Proposed Design: IBis IV. Detailed Construction V. Implementation on DAML VI. Evaluation VII. Conclusion and References V. IMPLEMENTATION ON DAML A. Blockchain Platform We implemented the proof of concept framework using Daml (Digital Asset Modeling Language) [27] atop the Canton blockchain ledger protocol [28]. We used Daml version 2.8.3 with the corresponding Daml ledger model and Canton protocol. The rationale for utilizing Daml in implementing our framework is twofold. First, in Daml, a smart contract codifies the terms of the agreement between parties, including the rights and obligations of each party. A Daml contract template describes the data schema of the contract and rules for manipulating the data. In our implementation, these contract templates align closely with the data models outlined in Sec.IV-A. Second, the Daml ledger model uses smart contracts as privacy-enabled data containers, enforcing data access controls as specified in the contract. The access control permissions of a party/user are explicitly stated in contract templates by assigning the party one of the following predefined roles: • signatory: A party whose authority is required to create the contract or archive it. Every contract must have at least one signatory. Signatories are guaranteed to see actions on that contract. • observer: A party that is guaranteed to see actions that create and archive the contract. • controller: A party that can exercise a particular choice (i.e., invoke a function) on the contract. • choice observer: A party that is guaranteed to see a particular choice being exercised on the contract. While similar approaches can be implemented with other smart contract languages, the contract-level access control typically does not extend to the ledger itself. For example, although chaincodes in Hyperledger Fabric can enforce access control, all channel members still synchronize the entire ledger [50]. In contrast, the Canton blockchain ledger protocol extends the Daml ledger model to the ledger level, where a blockchain node synchronizes only the contract data relevant to its party permissions. Therefore, by adopting Daml and Canton, we facilitate the integration of many AOs and COs onto the same IBIS platform, while ensuring that commercially sensitive license agreements between an AO and COs, dataset metadata, and model metadata remain concealed from other parties, even at the ledger level. Table III lists the resources in IBIS along with their authorizations. B. Smart Contract Implementation Listing 1 illustrates the license smart contract template, constructed based on the data schema in Table I. The copyrightOwner and modelOwner are designated as signatories of the template (Line 10). This reflects the bilateral nature of the license agreement. Consequently, authorization from both parties is required to create a license contract. Subsequently, once the contract is created, both parties can access it, while no other party has access to this contract either onchain or on-ledger. Hence, this implementation closely aligns with the access control requirements specified in Table III. In addition, the identifier attribute of each license serves as the primary key (specified by Line 12-13), which can facilitate efficient queries during the graph traversal. Similarly, dataset and model metadata are represented in smart contract templates according to their corresponding data models, except that only the modelOwner is designated as the signatory of the DatasetMeta and ModelMeta contracts. This setup enforces two access control rules: • Only modelOwner has the authority to create and access these contracts. • modelOwner is restricted to accessing DatasetMeta or ModelMeta contracts created by themselves. Full implementation details are open sourced[3]. Fig.7 illustrates the class diagram of IBIS design. C. Multi-party Signing The generation of a bilaterally signed license contract adheres to Daml’s Propose and Accept Pattern[4]. In this pattern, one party initiates a proposal contract, which the counterparty can either accept or reject. The acceptance (or rejection) is implemented as a contract choice[5], allowing the counterparty to exercise their decision. Upon exercising the accept choice, a result contract is generated, symbolizing the agreement between the two parties. In our implementation, the proposal contract takes the form of the draft license agreement, and it is depicted in Listing 2. LicenseAgreement shares the same data schema as License (see Listing 1), with the copyrightOwner designated as the signatory. This ensure only the copyrightOwner has the authority to create a contract. Alternatively, modelOwner, as the controller of the Accept choice in Line 16, has the authority to exercise the choice, resulting in the creation of a License contract. This setup ensures that only the modelOwner specified in a LicenseAgreement contract has the authority to exercise the Accept choice of that contract. Authors:
(1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(3) Guangsheng Yu, CSIRO Data61;
(4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. This paper is available on arxiv under CC BY 4.0 DEED license. [3] Open released at https://github.com/yilin-sai/ai-copyright-framework. [4] https://docs.daml.com/daml/patterns/initaccept.html# the-propose-and-accept-pattern. [5] https://docs.daml.com/daml/intro/4 Transformations.html# choices-as-methods Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Preliminaries II. Preliminaries III. Proposed Design: IBis III. Proposed Design: IBis IV. Detailed Construction IV. Detailed Construction V. Implementation on DAML V. Implementation on DAML VI. Evaluation VI. Evaluation VII. Conclusion and References VII. Conclusion and References V. IMPLEMENTATION ON DAML A. Blockchain Platform We implemented the proof of concept framework using Daml (Digital Asset Modeling Language) [27] atop the Canton blockchain ledger protocol [28]. We used Daml version 2.8.3 with the corresponding Daml ledger model and Canton protocol. The rationale for utilizing Daml in implementing our framework is twofold. First, in Daml, a smart contract codifies the terms of the agreement between parties, including the rights and obligations of each party. A Daml contract template describes the data schema of the contract and rules for manipulating the data. In our implementation, these contract templates align closely with the data models outlined in Sec.IV-A. contract template Second, the Daml ledger model uses smart contracts as privacy-enabled data containers, enforcing data access controls as specified in the contract. The access control permissions of a party/user are explicitly stated in contract templates by assigning the party one of the following predefined roles: • signatory : A party whose authority is required to create the contract or archive it. Every contract must have at least one signatory. Signatories are guaranteed to see actions on that contract. signatory • observer : A party that is guaranteed to see actions that create and archive the contract. observer • controller : A party that can exercise a particular choice (i.e., invoke a function) on the contract. controller • choice observer: A party that is guaranteed to see a particular choice being exercised on the contract. • choice observer: While similar approaches can be implemented with other smart contract languages, the contract-level access control typically does not extend to the ledger itself. For example, although chaincodes in Hyperledger Fabric can enforce access control, all channel members still synchronize the entire ledger [50]. In contrast, the Canton blockchain ledger protocol extends the Daml ledger model to the ledger level, where a blockchain node synchronizes only the contract data relevant to its party permissions. Therefore, by adopting Daml and Canton, we facilitate the integration of many AOs and COs onto the same IBIS platform, while ensuring that commercially sensitive license agreements between an AO and COs, dataset metadata, and model metadata remain concealed from other parties, even at the ledger level. Table III lists the resources in IBIS along with their authorizations. B. Smart Contract Implementation Listing 1 illustrates the license smart contract template, constructed based on the data schema in Table I. The copyrightOwner and modelOwner are designated as signatories of the template (Line 10). This reflects the bilateral nature of the license agreement. Consequently, authorization from both parties is required to create a license contract. Subsequently, once the contract is created, both parties can access it, while no other party has access to this contract either onchain or on-ledger. Hence, this implementation closely aligns with the access control requirements specified in Table III. In addition, the identifier attribute of each license serves as the primary key (specified by Line 12-13), which can facilitate efficient queries during the graph traversal. Similarly, dataset and model metadata are represented in smart contract templates according to their corresponding data models, except that only the modelOwner is designated as the signatory of the DatasetMeta and ModelMeta contracts. This setup enforces two access control rules: • Only modelOwner has the authority to create and access these contracts. • modelOwner is restricted to accessing DatasetMeta or ModelMeta contracts created by themselves. Full implementation details are open sourced[3]. Fig.7 illustrates the class diagram of IBIS design. C. Multi-party Signing The generation of a bilaterally signed license contract adheres to Daml’s Propose and Accept Pattern [4]. In this pattern, one party initiates a proposal contract, which the counterparty can either accept or reject. The acceptance (or rejection) is implemented as a contract choice[5], allowing the counterparty to exercise their decision. Upon exercising the accept choice, a result contract is generated, symbolizing the agreement between the two parties. Propose and Accept Pattern In our implementation, the proposal contract takes the form of the draft license agreement, and it is depicted in Listing 2. LicenseAgreement shares the same data schema as License (see Listing 1), with the copyrightOwner designated as the signatory. This ensure only the copyrightOwner has the authority to create a contract. Alternatively, modelOwner, as the controller of the Accept choice in Line 16, has the authority to exercise the choice, resulting in the creation of a License contract. This setup ensures that only the modelOwner specified in a LicenseAgreement contract has the authority to exercise the Accept choice of that contract. Authors: (1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (3) Guangsheng Yu, CSIRO Data61; (4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. Authors: Authors: (1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (3) Guangsheng Yu, CSIRO Data61; (4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv [3] Open released at https://github.com/yilin-sai/ai-copyright-framework. [4] https://docs.daml.com/daml/patterns/initaccept.html# the-propose-and-accept-pattern. [5] https://docs.daml.com/daml/intro/4 Transformations.html# choices-as-methods

Implementing Secure and Private Smart Contracts with Daml on the Canton Blockchain

The TechBeat: The New Private Cloud From the Eyes of an Architect (8/31/2024)

The New Private Cloud From the Eyes of an Architect

একজন স্থপতির চোখ থেকে নতুন ব্যক্তিগত মেঘ

Die neue Private Cloud aus der Sicht eines Architekten

La nueva nube privada desde la perspectiva de un arquitecto

Le nouveau cloud privé vu par un architecte

एक आर्किटेक्ट की नज़र से नया प्राइवेट क्लाउड

建築家の視点から見た新しいプライベートクラウド

건축가의 눈으로 본 새로운 프라이빗 클라우드

A nova nuvem privada vista por um arquiteto

Новое частное облако глазами архитектора

Bir Mimarın Gözünden Yeni Özel Bulut

Đám mây riêng mới từ góc nhìn của một kiến trúc sư

从建筑师的角度看新私有云

Zenqira is a decentralized computing platform dedicated to democratizing access to AI training resources. By connecting global computing providers with AI developers, Zenqira aims to reshape the AI landscape through collaboration, innovation, and cutting-edge technology. Zenqira, a pioneering force in decentralized AI computing, is excited to announce that the private sale of its native cryptocurrency, the Zenqira Token (ZENQ), end in just 2 days. Due to overwhelming demand, Zenqira is opening a new private sale slot starting on August 14, 2024, at invest.zenqira.ai. Zenqira Platform: Revolutionizing AI Computing Zenqira leverages cutting-edge decentralized computing technology to provide unparalleled access to AI training resources. By connecting global computing providers with AI developers, Zenqira is reshaping the AI landscape, making powerful computational resources accessible to all. Key Features Of The Zenqira Ecosystem: Decentralized AI Power: Facilitates efficient resource sharing through a collaborative ecosystem driven by token incentives, ensuring continual improvement and optimization of AI models.
Market Opportunity: Positioned at the forefront of the booming AI market, projected to reach USD 2,575.16 billion by 2032. ZENQ Token: Fueling The Future of AI The ZENQ token is the lifeblood of the Zenqira platform, rewarding participants who share their computational power and thus expanding the network and enhancing AI capabilities. Key utilizations of the ZENQ token include: Incentives: Encourages resource sharing and collaboration within the ecosystem.
Access: Unlocks premium features on the Zenqira platform.
Discounts: Offers significant savings on computing services.
Community Building: Strengthens connections within the tech ecosystem. Deflationary Model Zenqira employs a deflationary model to ensure the stability and value of the ZENQ token. Fifteen percent of net revenues from platform service fees, computing resource transactions, and data analytics services are allocated for ZENQ liquidity, with excess tokens strategically burned to manage supply. Private Sale Success And Upcoming Opportunities The initial private sale of ZENQ tokens has seen tremendous success and will end in just 2 days. Due to high demand, a new private sale slot will commence on August 14, 2024. Interested participants can join the private sale at invest.zenqira.ai. Join the Zenqira Revolution Zenqira invites AI innovators, developers, and computing resource providers to join our revolutionary platform. Connect with our expert advisors and collaborators, and be a part of the future of decentralized AI computing. Zenqira is a decentralized computing platform dedicated to democratizing access to AI training resources. By connecting global computing providers with AI developers, Zenqira aims to reshape the AI landscape through collaboration, innovation, and cutting-edge technology. For more information visit: https://zenqira.com | X | Telegram This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. Zenqira is a decentralized computing platform dedicated to democratizing access to AI training resources. By connecting global computing providers with AI developers, Zenqira aims to reshape the AI landscape through collaboration, innovation, and cutting-edge technology. Zenqira, a pioneering force in decentralized AI computing, is excited to announce that the private sale of its native cryptocurrency, the Zenqira Token (ZENQ), end in just 2 days. Due to overwhelming demand, Zenqira is opening a new private sale slot starting on August 14, 2024, at invest.zenqira.ai . invest.zenqira.ai invest.zenqira.ai Zenqira Platform: Revolutionizing AI Computing Zenqira leverages cutting-edge decentralized computing technology to provide unparalleled access to AI training resources. By connecting global computing providers with AI developers, Zenqira is reshaping the AI landscape, making powerful computational resources accessible to all. Key Features Of The Zenqira Ecosystem: Decentralized AI Power: Facilitates efficient resource sharing through a collaborative ecosystem driven by token incentives, ensuring continual improvement and optimization of AI models. Market Opportunity: Positioned at the forefront of the booming AI market, projected to reach USD 2,575.16 billion by 2032. Decentralized AI Power: Facilitates efficient resource sharing through a collaborative ecosystem driven by token incentives, ensuring continual improvement and optimization of AI models. Market Opportunity: Positioned at the forefront of the booming AI market, projected to reach USD 2,575.16 billion by 2032. ZENQ Token: Fueling The Future of AI The ZENQ token is the lifeblood of the Zenqira platform, rewarding participants who share their computational power and thus expanding the network and enhancing AI capabilities. Key utilizations of the ZENQ token include: Incentives: Encourages resource sharing and collaboration within the ecosystem. Access: Unlocks premium features on the Zenqira platform. Discounts: Offers significant savings on computing services. Community Building: Strengthens connections within the tech ecosystem. Incentives: Encourages resource sharing and collaboration within the ecosystem. Access: Unlocks premium features on the Zenqira platform. Discounts: Offers significant savings on computing services. Community Building: Strengthens connections within the tech ecosystem. Deflationary Model Zenqira employs a deflationary model to ensure the stability and value of the ZENQ token. Fifteen percent of net revenues from platform service fees, computing resource transactions, and data analytics services are allocated for ZENQ liquidity, with excess tokens strategically burned to manage supply. Private Sale Success And Upcoming Opportunities The initial private sale of ZENQ tokens has seen tremendous success and will end in just 2 days. Due to high demand, a new private sale slot will commence on August 14, 2024. Interested participants can join the private sale at invest.zenqira.ai . invest.zenqira.ai invest.zenqira.ai Join the Zenqira Revolution Zenqira invites AI innovators, developers, and computing resource providers to join our revolutionary platform. Connect with our expert advisors and collaborators, and be a part of the future of decentralized AI computing. Zenqira is a decentralized computing platform dedicated to democratizing access to AI training resources. By connecting global computing providers with AI developers, Zenqira aims to reshape the AI landscape through collaboration, innovation, and cutting-edge technology. For more information visit: https://zenqira.com | X | Telegram X Telegram This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Zenqira Announces ZENQ Token First  Private Sale End And New Sale Slot Starting August 14

জেনকিরা 14 আগস্ট থেকে ZENQ টোকেন প্রথম ব্যক্তিগত বিক্রয় শেষ এবং নতুন বিক্রয় স্লট ঘোষণা করেছে

Zenqira gibt das Ende des ersten privaten Verkaufs des ZENQ-Tokens und einen neuen Verkaufsslot ab dem 14. August bekannt

Zenqira anuncia el fin de la primera venta privada del token ZENQ y un nuevo espacio de venta a partir del 14 de agosto

Zenqira annonce la fin de la première vente privée du jeton ZENQ et un nouveau créneau de vente à partir du 14 août

ज़ेनकिरा ने ZENQ टोकन की पहली निजी बिक्री समाप्त होने और 14 अगस्त से नई बिक्री स्लॉट शुरू होने की घोषणा की

Zenqira が ZENQ トークンの最初のプライベートセールの終了と 8 月 14 日からの新しいセール開始を発表

Zenqira, ZENQ 토큰 첫 프라이빗 세일 종료 및 8월 14일부터 새로운 세일 슬롯 발표

Zenqira anuncia o fim da primeira venda privada do token ZENQ e um novo slot de venda a partir de 14 de agosto

Zenqira объявляет об окончании первой частной продажи токена ZENQ и новом слоте для продажи, начиная с 14 августа

Zenqira, ZENQ Token'ın İlk Özel Satışının Sona Erdiğini ve 14 Ağustos'ta Başlayacak Yeni Satış Slotunu Duyurdu

Zenqira công bố kết thúc đợt bán riêng tư đầu tiên của token ZENQ và thời điểm bán mới bắt đầu từ ngày 14 tháng 8

Zenqira 宣布 ZENQ 代币首次私募结束，新一轮发售将于 8 月 14 日开始

SINGAPORE, Singapore, June 21st, 2024/Chainwire/--W3GG is excited to announce the launch of an exclusive private sale of its W3GG tokens, starting on July 1, 2024. This sale offers community members a unique opportunity to purchase tokens at an initial price of $0.05 each. The private sale is structured to be inclusive, ensuring that all community members can participate. This approach reflects W3GG’s dedication to community-driven growth and rewarding early supporters. Only $250,000 worth of tokens will be available, exclusively for W3GG’s Soulbound Token (SBT) holders, with purchase options ranging from $25 to $5,000 per wallet. To participate in the sale, community members must meet the following requirements: • Have a W3GG account • Possess a W3GG SBT • Complete KYC on presale.w3gg.io • Have more than 1500 WXP “This initiative underscores W3GG’s commitment to empowering its community and fostering a robust, inclusive gaming ecosystem” said Irene Umar, Founder of W3GG. “Early supporters will benefit from the favorable pricing, reflecting the company’s dedication to rewarding loyalty. We are thrilled to launch the W3GG Community Round Private Sale. This is a pivotal moment in our vision to bringing gaming beyond play. We aim to create a vibrant, inclusive environment where gamers and developers can thrive.” The announcement comes at a time when Web3 gaming is experiencing substantial growth. According to CoinGecko, 29 of the world’s 40 biggest game studios are investing in Web3 gaming, with seven actively developing their own Web3 games in-house. Game7’s State of Web Gaming report indicated that at least $15 billion of institutional investment has recently moved into space. This influx of research, development, talent, and investment is now yielding significant outputs in the Web3 gaming space. W3GG is leading this charge, with strong backing from industry giants such as IVC, YGG, and Animoca Brands. The company has strategically invested in over 70 cutting-edge games, including Dark Times, Heroes of Mavia, APEIRON, and Nyan Heroes. These strategic investments highlight W3GG’s pioneering role in shaping the future of Web3 gaming and advancing the industry’s development. For more information on the W3GG Community Round Private Sale and to check eligibility requirements, please visit W3GG’s Litepaper. About W3GG: W3GG connects over 3 million gamers across five countries and serves as the exclusive partner of Yield Guild Games (YGG) in Southeast Asia. The company focuses on creating a social-first gaming ecosystem that promotes personal development and meaningful connections among gamers, transforming players into vital contributors and community advocates. For further details, visit W3GG’s official website. Contact CGO Josh Gier W3GG contact@w3gg.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here, SINGAPORE, Singapore, June 21st, 2024/Chainwire/--W3GG is excited to announce the launch of an exclusive private sale of its W3GG tokens, starting on July 1, 2024. This sale offers community members a unique opportunity to purchase tokens at an initial price of $0.05 each. The private sale is structured to be inclusive, ensuring that all community members can participate. This approach reflects W3GG’s dedication to community-driven growth and rewarding early supporters. Only $250,000 worth of tokens will be available, exclusively for W3GG’s Soulbound Token (SBT) holders, with purchase options ranging from $25 to $5,000 per wallet. To participate in the sale, community members must meet the following requirements: • Have a W3GG account • Possess a W3GG SBT • Complete KYC on presale.w3gg.io • Have more than 1500 WXP “This initiative underscores W3GG’s commitment to empowering its community and fostering a robust, inclusive gaming ecosystem” said Irene Umar, Founder of W3GG. “Early supporters will benefit from the favorable pricing, reflecting the company’s dedication to rewarding loyalty. We are thrilled to launch the W3GG Community Round Private Sale. This is a pivotal moment in our vision to bringing gaming beyond play. We aim to create a vibrant, inclusive environment where gamers and developers can thrive.” “This initiative underscores W3GG’s commitment to empowering its community and fostering a robust, inclusive gaming ecosystem” said Irene Umar, Founder of W3GG. “Early supporters will benefit from the favorable pricing, reflecting the company’s dedication to rewarding loyalty. We are thrilled to launch the W3GG Community Round Private Sale. This is a pivotal moment in our vision to bringing gaming beyond play. We aim to create a vibrant, inclusive environment where gamers and developers can thrive.” The announcement comes at a time when Web3 gaming is experiencing substantial growth. According to CoinGecko, 29 of the world’s 40 biggest game studios are investing in Web3 gaming, with seven actively developing their own Web3 games in-house. Game7’s State of Web Gaming report indicated that at least $15 billion of institutional investment has recently moved into space. This influx of research, development, talent, and investment is now yielding significant outputs in the Web3 gaming space. 29 of the world’s 40 biggest game studios 29 of the world’s 40 biggest game studios Game7’s State of Web Gaming report Game7’s State of Web Gaming report W3GG is leading this charge, with strong backing from industry giants such as IVC, YGG , and Animoca Brands . The company has strategically invested in over 70 cutting-edge games, including Dark Times , Heroes of Mavia , APEIRON , and Nyan Heroes . These strategic investments highlight W3GG’s pioneering role in shaping the future of Web3 gaming and advancing the industry’s development. YGG YGG Animoca Brands Animoca Brands Dark Times Dark Times Heroes of Mavia Heroes of Mavia APEIRON APEIRON Nyan Heroes Nyan Heroes For more information on the W3GG Community Round Private Sale and to check eligibility requirements, please visit W3GG’s Litepaper . Litepaper Litepaper About W3GG: W3GG connects over 3 million gamers across five countries and serves as the exclusive partner of Yield Guild Games (YGG) in Southeast Asia. The company focuses on creating a social-first gaming ecosystem that promotes personal development and meaningful connections among gamers, transforming players into vital contributors and community advocates. For further details, visit W3GG’s official website . W3GG’s official website W3GG’s official website Contact CGO Josh Gier W3GG contact@w3gg.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

W3GG To Launch Exclusive Private Token Sale For Community Members On July 1st

PrivateAI Closes Strategic Round, Commences PGPT Trading On Public Markets, And Launches AI Crowdsourcing Platform To Make Breakthrough Life Extension Discoveries PRIVATEAI (PRIVATEAI.COM), a decentralized AI protocol co-founded by the serial entrepreneur and Forbes 30 under 30 Global Angel Versetti and led by the former executives of Fantom, Filecoin, Dogecoin and Syscoin, announced successful raise of Strategic Round for its token PGPT, with lead investors including Charlie Hu of Bitlayer, DAO Maker, Gains Associates, Dextools Ventures, Connectico Capital, DOGE.ORG, Stone Block Association, 32 Ventures, Lunar Digital Assets, Pinnacle VC, Samara Asset Group, Snova Capital, Meezan Ventures. Immediately after closing the round, PrivateAI’s native token PGPT was launched on the centralized cryptocurrency exchange Bitget, where it debuted with a 500% daily gain in the first 24 hours of trading. PrivateAI has also unveiled its AI-native distributed data platform that intends to build the world’s first domain-specific Large Language Model (LLM) for the longevity industry. Powered by the unique architecture of knowledge graphs that process and structure scientific and research data into logic and framework suitable for the machine learning industry, PrivateAI creates monetization opportunities and liquid markets for owners and producers of longevity-related datasets by rewarding them with its liquid and tradeable cryptocurrency PGPT. For companies building their own Generative AI, Large Language Models, and AI-powered data analytics, PrivateAI offers access to unique data from the collective scientific hive-mind and incubation services where a mix of capital and AI tech is employed to accelerate promising early-stage scientific projects. The ultimate goal of PrivateAI is to enable breakthrough discoveries in life extension, also known as longevity. Knowledge Graphs give AI-powered systems the ability to understand the wider context of the data that is fed to the machines, enabling AI systems to develop critical thinking, context awareness, and semantic analysis. Importantly, knowledge graphs become living, perpetually updated, and expanded entities as a crowdsourcing community of scientists, researchers, and data providers expand the knowledge. The platform thus becomes the backend for such areas of longevity as AI-based drug discovery, personalized medicine, genomic research, and deep analysis of aging processes. The ultimate dream of Angel Versetti is to harness the collective scientific knowledge and research to enable people to live longer or even forever, which will be essential for humanity if it hopes to travel beyond the Solar system and colonize other planets. “PrivateAI’s 60x oversubscribed public round followed by a 500% growth of PGPT on the first day of trading indicates there is a massive appetite for the more meaningful, transhumanistic applications of AI that help radically advance science. We are proud to pioneer use cases that others deemed to be impossible.” – said Angel Versetti after the launch. PrivateAI is building collaborations with leading longevity-focused scientific research communities worldwide, having started discussions with the research communities at Oxford University, where Angel Versetti studied for an MSc in Evidence-Based Healthcare. PrivateAI also built partnerships with Lifespan.io and Aging Research Drug Discovery (ARDD) scientific consortia focused on longevity. Angel Versetti a@privateai.com CC admin@privateai.com is available for further interviews. PrivateAI Closes Strategic Round, Commences PGPT Trading On Public Markets, And Launches AI Crowdsourcing Platform To Make Breakthrough Life Extension Discoveries PrivateAI Closes Strategic Round, Commences PGPT Trading On Public Markets, And Launches AI Crowdsourcing Platform To Make Breakthrough Life Extension Discoveries PrivateAI Closes Strategic Round, Commences PGPT Trading On Public Markets, And Launches AI Crowdsourcing Platform To Make Breakthrough Life Extension Discoveries PrivateAI Closes Strategic Round, Commences PGPT Trading On Public Markets, And Launches AI Crowdsourcing Platform To Make Breakthrough Life Extension Discoveries PRIVATEAI (PRIVATEAI.COM), a decentralized AI protocol co-founded by the serial entrepreneur and Forbes 30 under 30 Global Angel Versetti and led by the former executives of Fantom, Filecoin, Dogecoin and Syscoin, announced successful raise of Strategic Round for its token PGPT, with lead investors including Charlie Hu of Bitlayer, DAO Maker, Gains Associates, Dextools Ventures, Connectico Capital, DOGE.ORG, Stone Block Association, 32 Ventures, Lunar Digital Assets, Pinnacle VC, Samara Asset Group, Snova Capital, Meezan Ventures. Immediately after closing the round, PrivateAI’s native token PGPT was launched on the centralized cryptocurrency exchange Bitget, where it debuted with a 500% daily gain in the first 24 hours of trading. serial entrepreneur serial entrepreneur Forbes 30 under 30 Global Forbes 30 under 30 Global daily gain in the first 24 hours of trading. daily gain in the first 24 hours of trading. PrivateAI has also unveiled its AI-native distributed data platform that intends to build the world’s first domain-specific Large Language Model (LLM) for the longevity industry. Powered by the unique architecture of knowledge graphs that process and structure scientific and research data into logic and framework suitable for the machine learning industry, PrivateAI creates monetization opportunities and liquid markets for owners and producers of longevity-related datasets by rewarding them with its liquid and tradeable cryptocurrency PGPT. For companies building their own Generative AI, Large Language Models, and AI-powered data analytics, PrivateAI offers access to unique data from the collective scientific hive-mind and incubation services where a mix of capital and AI tech is employed to accelerate promising early-stage scientific projects. The ultimate goal of PrivateAI is to enable breakthrough discoveries in life extension, also known as longevity. AI-native distributed data platform AI-native distributed data platform Knowledge Graphs give AI-powered systems the ability to understand the wider context of the data that is fed to the machines, enabling AI systems to develop critical thinking, context awareness, and semantic analysis. Importantly, knowledge graphs become living, perpetually updated, and expanded entities as a crowdsourcing community of scientists, researchers, and data providers expand the knowledge. The platform thus becomes the backend for such areas of longevity as AI-based drug discovery, personalized medicine, genomic research, and deep analysis of aging processes. The ultimate dream of Angel Versetti is to harness the collective scientific knowledge and research to enable people to live longer or even forever, which will be essential for humanity if it hopes to travel beyond the Solar system and colonize other planets. “PrivateAI’s 60x oversubscribed public round followed by a 500% growth of PGPT on the first day of trading indicates there is a massive appetite for the more meaningful, transhumanistic applications of AI that help radically advance science. We are proud to pioneer use cases that others deemed to be impossible.” – said Angel Versetti after the launch. PrivateAI is building collaborations with leading longevity-focused scientific research communities worldwide, having started discussions with the research communities at Oxford University, where Angel Versetti studied for an MSc in Evidence-Based Healthcare. PrivateAI also built partnerships with Lifespan.io and Aging Research Drug Discovery (ARDD) scientific consortia focused on longevity. Angel Versetti a@privateai.com CC admin@privateai.com is available for further interviews. a@privateai.com a@privateai.com admin@privateai.com admin@privateai.com

PGPT Token Now Public: PrivateAI Completes Funding Round

PGPT টোকেন এখন পাবলিক: PrivateAI ফান্ডিং রাউন্ড সম্পূর্ণ করেছে

PGPT-Token jetzt öffentlich: PrivateAI schließt Finanzierungsrunde ab

El token PGPT ahora es público: PrivateAI completa la ronda de financiación

Le jeton PGPT est désormais public : PrivateAI termine le cycle de financement

पीजीपीटी टोकन अब सार्वजनिक: प्राइवेटएआई ने फंडिंग राउंड पूरा किया

PGPTトークンが一般公開：PrivateAIが資金調達ラウンドを完了

PGPT 토큰 공개: PrivateAI, 자금 조달 라운드 완료

Token PGPT agora público: PrivateAI conclui rodada de financiamento

Токен PGPT теперь общедоступен: PrivateAI завершает раунд финансирования

PGPT Tokeni Artık Halka Açık: PrivateAI Finansman Turunu Tamamladı

Mã thông báo PGPT hiện đã được công khai: PrivateAI hoàn thành vòng cấp vốn

PGPT 代币现已公开：PrivateAI 完成融资

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