Weaver

weaver

A Weaver of Dreams

Episode 3 of the : An interview with Kelley Weaver, CEO of Hacker Noon Podcast Melrose PR In this episode we discuss cryptocurrency, blockchain, marketing, PR techniques, and life hacks. You can listen to the entire conversation right now on the audio player with Podbean, on , and on for video. iTunes YouTube “Having a minimum viable product is essential right now. Bitcoin is dead — you ‘know’ it from Bloomberg and the Wall Street Journal.“ _Episode 3: An interview with Kelley Weaver, CEO of https://www.melrosepr.com/. In this episode we discuss…_podcast.hackernoon.com The Truth About Crypto PR with Kelley Weaver

The Truth About Crypto PR with Kelley Weaver

Previously at Twitter as employee #15, Evan Weaver has recently raised a $25M Series A to grow Fauna, a cloud-native database. Davis Baer : What’s your background, and what are you working on? : My name is Evan Weaver and I’m the CEO and Co-Founder of . My passion for software development started when I was 8 or 9 years old; like many of us, I was trying to make games. I think I still have the QBasic book that I used to write a slot machine program that featured…wait for it…graphics. My father worked for a university so we had early access to the internet as well; you could download shareware from Gopher with Archie and Veronica, which were frankly terrible. Evan Weaver Fauna At school, I studied poetry writing, philosophy, jazz, and computer science; as you can guess, only one of these things makes money, so here we are. I got my masters in CS and worked on gene orthologs in chickens. Along the way, I got interested in open source and Ruby on Rails and got a job at CNET Networks. I worked as a DBA and backend developer on CHOW and UrbanBaby, along with Chris Wanstrath and P. J. Hyett. You may know them from a small project called GitHub. In 2008, I left CNET and joined Twitter as employee #15. I ended up building and managing the backend infrastructure team. We did all the distributed storage for the core business objects and worked on performance in Ruby and Hotspot. What motivated you to get started with your company? Twitter was one of the last great internet companies to be born before the cloud era. It seems crazy now, but there was no hardware available on demand. And Twitter’s rise tracked SMS and smartphone adoption curves, instead of broadband penetration like Facebook. This meant that we had uncontrollable user growth, without natural network segmentation. We racked new hardware every 6 months, and increased end-to-end performance by almost 20% month over month, but our servers still ran at 100% utilization all the time because of latent demand especially on the API. We couldn’t find off-the-shelf systems that could meet the scalability challenges we faced for love or money, so we were forced to build a number of point solutions. A decade later Twitter still uses some of these systems, like the social graph and the timelines database, which is processing HFT-level workloads in real time. At Twitter, we were some of the original adopters of NoSQL as well. My team was the first user of Cassandra outside of Facebook. We fixed the build; I wrote the first tutorial; we hosted the first meetup. We were hoping that NoSQL, and Cassandra specifically, would grow into a general purpose, operationally resilient, modern cloud-native database platform — and for a variety of reasons it just didn’t happen. Because of time and business constraints at Twitter, and the failure of the first generation of NoSQL vendors, we never got a truly reusable data platform that could meet our scalability, efficiency and global availability goals, and remain flexible for product development. This platform is FaunaDB. FaunaDB is a ground-up rewrite of what a modern, relational, cloud-native database should be — focused on what application developers and operators need for the future, instead of legacy constraints. What went into building the initial product? Matt Freels, my co-founder, was on my team at Twitter. We left Twitter and founded Fauna in 2012. NVIDIA was our first partner; we did a lot of co-development and non-recurring engineering with them while in stealth mode to make sure we were meeting a real market need. It turns out databases need a lot of R&D so this took a little longer than we expected. FaunaDB is built for mission-critical workloads, so proof from early adopters such as NVIDIA, CapitalOne and Nextdoor has been crucial. We’re now able to show that the FaunaDB system meets the goals we laid out for ourselves in the early days of building an enterprise-grade, relational NoSQL solution. FaunaDB is implemented in Scala and runs on the JVM. We are happy with our choice of Scala. When we started FaunaDB, we looked at other options like C++, Go, and Rust. We wanted a modern, memory-safe language that offered a high-level type system — since databases are monoliths — but also was capable of predictable, high performance. The JVM meets both these goals and is very friendly to deploy in any operational environment — scaling down to laptop size or up to mainframe size. It is also OS-independent, so FaunaDB is incredibly portable. Write once; run anywhere — the dream of the ’90s is alive at Fauna. Scala solves the type system problem and lets us keep the the codebase very clean and well-encapsulated. In order to get Fauna off the ground, we used a mix of funding. We invested some of our own money and did some scalability consulting for companies that represented our target market. This was a great way for us to develop our understanding of business problems, rather than just replicate some internal system from Twitter. We found that the market was wildly behind even the worst systems at Twitter, and it was critical to deliver a truly database that didn’t give up any of the flexibility of relational systems. And most companies’ database hardware is running at 10% utilization or less, which was shocking to us, and led us to incorporate FaunaDB’s multi-tenancy and QoS features for workload consolidation and safety at scale. general purpose We got far with the mix of self-funding and partnership revenue. Once we were ready to bet the company on a specific product vision, we went out to raise venture capital so we could focus on product development full-time. In 2016, we raised our seed round from CRV and launched FaunaDB Serverless Cloud. Just under a year ago, from Point72 and Google Ventures and are now in midst of our enterprise go-to-market, which is going well. we raise our A round How have you attracted users and grown your company? Most operational databases launch with exactly production users, and fall flat on their faces at the first production use. One of the things we did in order to make sure we really delivered on high availability, durability, and correctness, was to eat our own dogfood by operating . zero FaunaDB Serverless Cloud FaunaDB Cloud is exciting because it’s a serverless, utility computing solution — again with the ’90s dreams. Our cloud is a single, multi-region, multi-provider installation of FaunaDB Enterprise, from which we dynamically provision individual tenants. There is no orchestration or container system around it and it transparently scales across AWS and Google Cloud. FaunaDB Cloud let us get direct insight into early product use cases for FaunaDB as well as harden its operations far and make it truly production-ready. At Twitter, we operated our mission-critical systems ourselves so we are as much DBAs and operators as developers. There’s no other way to do it if you are pushing the boundaries of information science like we did with Twitter at scale or with FaunaDB at consistency and transactionality. What’s your business model, and how have you grown your revenue? Part of our vision is to package and deliver FaunaDB is every way that the market wants to get it. We value and support the smallest, newest hobbyist as well as the largest enterprise, and pricing spans that range as well. For the individual developer we have the serverless cloud, which is pay-as-you go with no fixed cost. We just as well. And we will soon be releasing a free download for people to try out FaunaDB on-premises, or for development purposes locally. simplified our pricing model At the same time, we offer the full-service, enterprise customer success experience for companies like Capital One who are building mission-critical systems on FaunaDB. We do everything we can to ensure that if you’re making a bet on a new operational database like FaunaDB, it pays off long-term. One thing we’ve learned over and over is that no one is happy to be in the market for a database. They are in the market out of desperation and necessity. An operational database is like the foundation of your house — you never want to change it. This means that customers want to have a real, long-term vendor partnership — a marriage, so to speak. It’s very different from markets like analytics databases which are easy-come, easy-go because they aren’t the canonical storage for anything, or time-series databases that only store low-value data that is consumed in aggregate. What are your goals for the future? So far, we’re very happy with our company growth. We’re bringing on real, production, paying customers, including enterprise, small businesses, and individuals. We’re hiring engineers and salespeople after raising the largest series A in our space — $25M — less than a year ago. Our immediate focus is the enterprise readiness of the company and product overall. You can expect to see a lot more from us this coming year, especially integration tooling, even more query capabilities, better enterprise and developer support, documentation and other educational materials, etc. — everything required for “turnkey” adoptability. In the second half of the year, you will see us launch more cutting edge features like real-time streaming, locality control, and some other thing in stealth, as well as extend FaunaDB Serverless Cloud to Microsoft Azure. What are the biggest challenges you’ve faced and obstacles you’ve overcome? If you had to start over, what would you do differently? Our biggest challenge, which is systemic in the startup ecosystem these days, is hiring. When I was a hiring manager at Twitter, I would routinely recruit single-income families to move to the San Francisco Bay Area. Those days are gone. We pay market rates but we are also competing with offers from publicly traded companies like Amazon, Google, and even Twitter which issue liquid equity as RSUs. It’s hard to turn down that guaranteed payout when cost of living in the Bay Area has practically tripled. In response, we’ve invested a lot in building a remote-first culture so we can hire the talent we need in to make Fauna a huge success no matter where people want to live, and we are opening a second office in the Boston area as well. We are a deep tech business and we have real R&D costs, both money and time. We’re always looking for the right people, whether team members or customers, to contribute to delivering on our long-term vision and replace 40 years of obsolete, mainframe-oriented database technology. Have you found anything particularly helpful or advantageous? We’ve been the beneficiary of a lot of advice, whether the advice-giver knew it or not. We’ve watched others’ mistakes in our market very closely. Most new database entrants go to market too early. They don’t understand how long it takes to actually build a new piece of foundational technology. A distributed database is as complex as an operating system, and more error-prone. You can raise money on a deck and a dream, but if you don’t actually have enough runway to build to that dream, you will fail. We’ve worked hard to find patient investors who understand the long-term value of what we’re building. What’s your advice for entrepreneurs who are just starting out? There’s no instant market traction in a deep tech business. Even hobbyists want their drone to stay in the air, their self-driving car to be safe, their database to be durable and consistent. You have to do a ton of deep grinding engineering work to smooth out the rough edges on this type of project. You need to be very mindful of the potential path dependence in your implementation and architectural choices. You have to understand how long and challenging your go-to-market path will be, especially in the enterprise. We may not be the smartest people in the room, but we are definitely the people that work the hardest and never give up. For these types of R&D intensive projects, slow and steady wins the race. Where can we go to learn more? To learn more about Fauna, check out www.fauna.com. This interview is brought to you by , a tool to schedule and automatically repeat your posts on Facebook, Instagram, Twitter, Pinterest, LinkedIn, and Google My Business OneUp

Founder Interviews: Evan Weaver of Fauna

Episode 12: An interview with Kelley Weaver of , a crypto blockchain PR company in Los Angeles California. Crypto Disrupted Melrose PR and I interview Kelley Weaver of Melrose PR, and discuss running one of the most reputable Blockchain PR firms in Los Angeles. Greg Kerr Also . available on iTunes For more episodes of , or . Crypto Disrupted subscribe on YouTube subscribe and listen on iTunes

Blockchain PR with Kelley Weaver

Modular Monoliths: The Future of Efficient Software Architecture in Fintech Payment Systems

Author:
(1) David S. Hardin, Cedar Rapids, IA USA david.s.hardin@gmail.com. Table of Links Abstract and Introduction
Dancing Links
The Rust Programming Language
RAC: Hardware/Software Co-Assurance at Scale
Rust and RAR
Dancing Links in Rust
Related Work
Conclusion, Acknowledgments, and References 8 Conclusion We have developed a prototype toolchain to allow the Rust programming language to be used as a hardware/software co-design and co-assurance language for critical systems, standing on the shoulders of Russinoff’s team at Arm, and all the great work they have done on Restricted Algorithmic C. We have demonstrated the ability to establish the correctness of several practical data structures commonly found in high-assurance systems (e.g., array-backed singly-linked lists, doubly-linked lists, stacks, and dequeues) through automated formal verification, enabled by automated source-to-source translation from Rust to RAC to ACL2, and have detailed the specification and verification of one such data structure, a circular doubly-linked list employing Knuth’s “Dancing Links” optimization. We have also successfully applied our toolchain to cryptography and data format filtering examples typical of the sorts of algorithms that one encounters in critical systems development. In future work, we will continue to develop our toolchain, increasing the number of Rust features that we can support in the RAR subset, as well as continuing to improve the ACL2 verification libraries in order to increase the ability to discharge RAR correctness proofs automatically. We will also continue to work with our colleagues at Kansas State University on the direct synthesis and verification of RAR code from architectural models, as well as working with colleagues at the University of Kansas on verified synthesis of Rust code from high-level attestation protocol specifications written using the Coq theorem prover. 9 Acknowledgments Many thanks to Donald Knuth for his detailed study of exact cover problems in general, and the “Dancing Links” optimization in particular, that can now be found in Volume 4B of his seminal series, The Art of Computer Programming. It was a pleasure discovering this particular corner of Computer Science, beginning when the author accidentally stumbled upon a previously recorded Knuth “Christmas Lecture” on the subject in late 2022. Previous foundational work on hardware/software co-assurance in Rust was funded by DARPA contract HR00111890001. The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. Many thanks to David Russinoff of Arm for developing and improving the RAC toolchain, without which most of the current work would not be possible. Thanks also go to the anonymous reviewers for their insightful comments. References [1] Ron Amadeo (2021): Google is now writing low-level Android code in Rust. Available at https://arstechnica.com/gadgets/2021/04/ google-is-now-writing-low-level-android-code-in-rust/. [2] Amazon Web Services (2022): Announcing the Kani Rust Verifier Project. Available at https://model-checking.github.io/kani-verifier-blog/2022/ 05/04/announcing-the-kani-rust-verifier-project.html?fbclid=IwAR2M_ B1IEBfkVhIXSuuAxt3McC_QpUnTuzDq9jG40HOaJzxw8z1Nw9XU_i4. [3] V. Astrauskas, A. B´ıl ´y, J. Fiala, Z. Grannan, C. Matheja, P. M ¨uller, F. Poli & A. J. Summers (2022): The Prusti Project: Formal Verification for Rust (invited). In: NASA Formal Methods (14th International Symposium), Springer, pp. 88–108, doi:10.1007/978-3-031-06773-0_5. Available at https://link.springer. com/chapter/10.1007/978-3-031-06773-0_5. [4] Robert S. Boyer & J Strother Moore (2002): Single-Threaded Objects in ACL2. In: Practical Aspects of Declarative Languages, 4th International Symposium, PADL 2002, Portland, OR, USA, January 19-20, 2002, Proceedings, LNCS 2257, Springer, pp. 9–27, doi:10.1007/3-540-45587-6_3. [5] Sally Browning & Philip Weaver (2010): Designing Tunable, Verifiable Cryptographic Hardware Using Cryptol. In David S. Hardin, editor: Design and Verification of Microprocessor Systems for High-Assurance Applications, Springer, pp. 89–143, doi:10.1007/978-1-4419-1539-9_4. [6] Thomas Claburn (2023): Microsoft is busy rewriting core Windows code in memory-safe Rust. Available at https://www.theregister.com/2023/04/27/microsoft_windows_rust/. [7] Darren Cofer, Isaac Amundson, Junaid Babar, David Hardin, Konrad Slind, Perry Alexander, John Hatcliff, Robby, Gerwin Klein, Corey Lewis, Eric Mercer & John Shackleton (2022): Cyber Assured Systems Engineering at Scale. In: IEEE Security & Privacy, pp. 52–64, doi:10.1109/MSEC.2022.3151733. [8] Xavier Denis (2022): Creusot. Available at https://github.com/xldenis/creusot. [9] Peter H. Feiler & David P. Gluch (2012): Model-Based Engineering with AADL: An Introduction to the SAE Architecture Analysis & Design Language, 1st edition. Addison-Wesley Professional. [10] David S. Hardin (2020): Put Me on the RAC. In: Proceedings of the Sixteenth International Workshop on the ACL2 Theorem Prover and its Applications (ACL2-20), pp. 142–145, doi:10.4204/eptcs.327.13. [11] David S. Hardin (2020): Verified Hardware/Software Co-Assurance: Enhancing Safety and Security for Critical Systems. In: Proceedings of the 2020 IEEE Systems Conference, doi:10.1109/SysCon47679. 2020.9381831. [12] David S. Hardin (2022): Hardware/Software Co-Assurance for the Rust Programming Language Applied to Zero-Trust Architecture Development. ACM SIGAda Ada Letters 42(2), pp. 55–61, doi:10.1145/3591335. 3591340. [13] David S. Hardin (2022): Hardware/Software Co-Assurance using the Rust Programming Language and ACL2. In: Proceedings of the Seventeenth International Workshop on the ACL2 Theorem Prover and its Applications (ACL2-22), pp. 202–216, doi:10.4204/EPTCS.359.16. [14] David S. Hardin & Konrad L. Slind (2021): Formal Synthesis of Filter Components for Use in SecurityEnhancing Architectural Transformations. In: Proceedings of the Seventh Workshop on Language-Theoretic Security, 42nd IEEE Symposium and Workshops on Security and Privacy (LangSec 2021), doi:10.1109/ SPW53761.2021.00024. [15] Jane Street Group, LLC (2023): Hardcaml: An OCaml library for designing and testing hardware designs. Available at https://github.com/janestreet/hardcaml. [16] Steve Klabnik & Carol Nichols (2018): The Rust Programming Language. No Starch Press. [17] Donald E. Knuth (2022): The Art of Computer Programming. 4B: Combinatorial Algorithms, Part 2, Addison-Wesley. [18] Ramana Kumar, Magnus O. Myreen, Michael Norrish & Scott Owens (2014): CakeML: a verified implementation of ML. In Suresh Jagannathan & Peter Sewell, editors: The 41st Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL ’14, San Diego, CA, USA, January 20-21, 2014, ACM, pp. 179–192, doi:10.1145/2535838.2535841. [19] Andrea Lattuada, Travis Hance, Chanhee Cho, Matthias Brun, Isitha Subasinghe, Yi Zhou, Jon Howell, Bryan Parno & Chris Hawblitzel (2023): Verus: Verifying Rust Programs Using Linear Ghost Types. Proc. ACM Program. Lang. 7(OOPSLA1), doi:10.1145/3586037. [20] Yusuke Matsushita, Takeshi Tsukada & Naoki Kobayashi (2021): RustHorn: CHC-Based Verification for Rust Programs. ACM Trans. Program. Lang. Syst. 43(4), doi:10.1145/3462205. [21] Mentor Graphics Corporation (2016): Algorithmic C (AC) Datatypes. Available at https://www.mentor. com/hls-lp/downloads/ac-datatypes. [22] Mentor Graphics Corporation (2020): Catapult High-Level Synthesis. Available at https://www.mentor. com/hls-lp/catapult-high-level-synthesis/. [23] Eric Mercer, Konrad Slind, Isaac Amundson, Darren Cofer, Junaid Babar & David Hardin (2023): Synthesizing Verified Components for Cyber Assured Systems Engineering. In: Software and Systems Modeling, 22, pp. 1451–1471, doi:10.1007/s10270-023-01096-3. [24] Matt Miller (2019): A proactive approach to more secure code. Available at https://msrc-blog. microsoft.com/2019/07/16/a-proactive-approach-to-more-secure-code/. [25] Shane Miller & Carl Lerche (2022): Sustainability with Rust. Available at https://aws.amazon.com/ blogs/opensource/sustainability-with-rust/. [26] Razvan Nane, Vlad-Mihai Sima, Christian Pilato, Jongsok Choi, Blair Fort, Andrew Canis, Yu Ting Chen, Hsuan Hsiao, Stephen Brown, Fabrizio Ferrandi, Jason Anderson & Koen Bertels (2016): A Survey and Evaluation of FPGA High-Level Synthesis Tools. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35(10), pp. 1591–1604, doi:10.1109/TCAD.2015.2513673. [27] David M. Russinoff (2022): Formal Verification of Floating-Point Hardware Design: A Mathematical Approach, second edition. Springer, doi:10.1007/978-3-030-87181-9. [28] Geoffry Song (2020): plex: a parser and lexer generator as a Rust procedural macro. Available at https:// github.com/goffrie/plex. [29] Jeff Vander Stoep & Stephen Hines (2021): Rust in the Android platform. Available at https://security. googleblog.com/2021/04/rust-in-android-platform.html. [30] Loup Vaillant (2022): Monocypher: Boring Crypto that Simply Works. Available at https://monocypher. org. [31] Xilinx, Inc. (2018): Vivado Design Suite User Guide: High-Level Synthesis. Available at https://www.xilinx.com/support/documentation/sw_manuals/xilinx2018_3/ ug902-vivado-high-level-synthesis.pdf. This paper is available on arxiv under CC 4.0 license. Author: (1) David S. Hardin, Cedar Rapids, IA USA david.s.hardin@gmail.com. Author: Author: (1) David S. Hardin, Cedar Rapids, IA USA david.s.hardin@gmail.com. Table of Links Abstract and Introduction Dancing Links The Rust Programming Language RAC: Hardware/Software Co-Assurance at Scale Rust and RAR Dancing Links in Rust Related Work Conclusion, Acknowledgments, and References Abstract and Introduction Abstract and Introduction Dancing Links Dancing Links The Rust Programming Language The Rust Programming Language RAC: Hardware/Software Co-Assurance at Scale RAC: Hardware/Software Co-Assurance at Scale Rust and RAR Rust and RAR Dancing Links in Rust Dancing Links in Rust Related Work Related Work Conclusion, Acknowledgments, and References Conclusion, Acknowledgments, and References 8 Conclusion We have developed a prototype toolchain to allow the Rust programming language to be used as a hardware/software co-design and co-assurance language for critical systems, standing on the shoulders of Russinoff’s team at Arm, and all the great work they have done on Restricted Algorithmic C. We have demonstrated the ability to establish the correctness of several practical data structures commonly found in high-assurance systems (e.g., array-backed singly-linked lists, doubly-linked lists, stacks, and dequeues) through automated formal verification, enabled by automated source-to-source translation from Rust to RAC to ACL2, and have detailed the specification and verification of one such data structure, a circular doubly-linked list employing Knuth’s “Dancing Links” optimization. We have also successfully applied our toolchain to cryptography and data format filtering examples typical of the sorts of algorithms that one encounters in critical systems development. In future work, we will continue to develop our toolchain, increasing the number of Rust features that we can support in the RAR subset, as well as continuing to improve the ACL2 verification libraries in order to increase the ability to discharge RAR correctness proofs automatically. We will also continue to work with our colleagues at Kansas State University on the direct synthesis and verification of RAR code from architectural models, as well as working with colleagues at the University of Kansas on verified synthesis of Rust code from high-level attestation protocol specifications written using the Coq theorem prover. 9 Acknowledgments Many thanks to Donald Knuth for his detailed study of exact cover problems in general, and the “Dancing Links” optimization in particular, that can now be found in Volume 4B of his seminal series, The Art of Computer Programming. It was a pleasure discovering this particular corner of Computer Science, beginning when the author accidentally stumbled upon a previously recorded Knuth “Christmas Lecture” on the subject in late 2022. Previous foundational work on hardware/software co-assurance in Rust was funded by DARPA contract HR00111890001. The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. Many thanks to David Russinoff of Arm for developing and improving the RAC toolchain, without which most of the current work would not be possible. Thanks also go to the anonymous reviewers for their insightful comments. References [1] Ron Amadeo (2021): Google is now writing low-level Android code in Rust. Available at https://arstechnica.com/gadgets/2021/04/ google-is-now-writing-low-level-android-code-in-rust/. [2] Amazon Web Services (2022): Announcing the Kani Rust Verifier Project. Available at https://model-checking.github.io/kani-verifier-blog/2022/ 05/04/announcing-the-kani-rust-verifier-project.html?fbclid=IwAR2M_ B1IEBfkVhIXSuuAxt3McC_QpUnTuzDq9jG40HOaJzxw8z1Nw9XU_i4. [3] V. Astrauskas, A. B´ıl ´y, J. Fiala, Z. Grannan, C. Matheja, P. M ¨uller, F. Poli & A. J. Summers (2022): The Prusti Project: Formal Verification for Rust (invited). In: NASA Formal Methods (14th International Symposium), Springer, pp. 88–108, doi:10.1007/978-3-031-06773-0_5. Available at https://link.springer. com/chapter/10.1007/978-3-031-06773-0_5. [4] Robert S. Boyer & J Strother Moore (2002): Single-Threaded Objects in ACL2. In: Practical Aspects of Declarative Languages, 4th International Symposium, PADL 2002, Portland, OR, USA, January 19-20, 2002, Proceedings, LNCS 2257, Springer, pp. 9–27, doi:10.1007/3-540-45587-6_3. [5] Sally Browning & Philip Weaver (2010): Designing Tunable, Verifiable Cryptographic Hardware Using Cryptol. In David S. Hardin, editor: Design and Verification of Microprocessor Systems for High-Assurance Applications, Springer, pp. 89–143, doi:10.1007/978-1-4419-1539-9_4. [6] Thomas Claburn (2023): Microsoft is busy rewriting core Windows code in memory-safe Rust. Available at https://www.theregister.com/2023/04/27/microsoft_windows_rust/. [7] Darren Cofer, Isaac Amundson, Junaid Babar, David Hardin, Konrad Slind, Perry Alexander, John Hatcliff, Robby, Gerwin Klein, Corey Lewis, Eric Mercer & John Shackleton (2022): Cyber Assured Systems Engineering at Scale. In: IEEE Security & Privacy, pp. 52–64, doi:10.1109/MSEC.2022.3151733. [8] Xavier Denis (2022): Creusot. Available at https://github.com/xldenis/creusot. [9] Peter H. Feiler & David P. Gluch (2012): Model-Based Engineering with AADL: An Introduction to the SAE Architecture Analysis & Design Language, 1st edition. Addison-Wesley Professional. [10] David S. Hardin (2020): Put Me on the RAC. In: Proceedings of the Sixteenth International Workshop on the ACL2 Theorem Prover and its Applications (ACL2-20), pp. 142–145, doi:10.4204/eptcs.327.13. [11] David S. Hardin (2020): Verified Hardware/Software Co-Assurance: Enhancing Safety and Security for Critical Systems. In: Proceedings of the 2020 IEEE Systems Conference, doi:10.1109/SysCon47679. 2020.9381831. [12] David S. Hardin (2022): Hardware/Software Co-Assurance for the Rust Programming Language Applied to Zero-Trust Architecture Development. ACM SIGAda Ada Letters 42(2), pp. 55–61, doi:10.1145/3591335. 3591340. [13] David S. Hardin (2022): Hardware/Software Co-Assurance using the Rust Programming Language and ACL2. In: Proceedings of the Seventeenth International Workshop on the ACL2 Theorem Prover and its Applications (ACL2-22), pp. 202–216, doi:10.4204/EPTCS.359.16. [14] David S. Hardin & Konrad L. Slind (2021): Formal Synthesis of Filter Components for Use in SecurityEnhancing Architectural Transformations. In: Proceedings of the Seventh Workshop on Language-Theoretic Security, 42nd IEEE Symposium and Workshops on Security and Privacy (LangSec 2021), doi:10.1109/ SPW53761.2021.00024. [15] Jane Street Group, LLC (2023): Hardcaml: An OCaml library for designing and testing hardware designs. Available at https://github.com/janestreet/hardcaml. [16] Steve Klabnik & Carol Nichols (2018): The Rust Programming Language. No Starch Press. [17] Donald E. Knuth (2022): The Art of Computer Programming. 4B: Combinatorial Algorithms, Part 2, Addison-Wesley. [18] Ramana Kumar, Magnus O. Myreen, Michael Norrish & Scott Owens (2014): CakeML: a verified implementation of ML. In Suresh Jagannathan & Peter Sewell, editors: The 41st Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL ’14, San Diego, CA, USA, January 20-21, 2014, ACM, pp. 179–192, doi:10.1145/2535838.2535841. [19] Andrea Lattuada, Travis Hance, Chanhee Cho, Matthias Brun, Isitha Subasinghe, Yi Zhou, Jon Howell, Bryan Parno & Chris Hawblitzel (2023): Verus: Verifying Rust Programs Using Linear Ghost Types. Proc. ACM Program. Lang. 7(OOPSLA1), doi:10.1145/3586037. [20] Yusuke Matsushita, Takeshi Tsukada & Naoki Kobayashi (2021): RustHorn: CHC-Based Verification for Rust Programs. ACM Trans. Program. Lang. Syst. 43(4), doi:10.1145/3462205. [21] Mentor Graphics Corporation (2016): Algorithmic C (AC) Datatypes. Available at https://www.mentor. com/hls-lp/downloads/ac-datatypes. [22] Mentor Graphics Corporation (2020): Catapult High-Level Synthesis. Available at https://www.mentor. com/hls-lp/catapult-high-level-synthesis/. [23] Eric Mercer, Konrad Slind, Isaac Amundson, Darren Cofer, Junaid Babar & David Hardin (2023): Synthesizing Verified Components for Cyber Assured Systems Engineering. In: Software and Systems Modeling, 22, pp. 1451–1471, doi:10.1007/s10270-023-01096-3. [24] Matt Miller (2019): A proactive approach to more secure code. Available at https://msrc-blog. microsoft.com/2019/07/16/a-proactive-approach-to-more-secure-code/. [25] Shane Miller & Carl Lerche (2022): Sustainability with Rust. Available at https://aws.amazon.com/ blogs/opensource/sustainability-with-rust/. [26] Razvan Nane, Vlad-Mihai Sima, Christian Pilato, Jongsok Choi, Blair Fort, Andrew Canis, Yu Ting Chen, Hsuan Hsiao, Stephen Brown, Fabrizio Ferrandi, Jason Anderson & Koen Bertels (2016): A Survey and Evaluation of FPGA High-Level Synthesis Tools. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35(10), pp. 1591–1604, doi:10.1109/TCAD.2015.2513673. [27] David M. Russinoff (2022): Formal Verification of Floating-Point Hardware Design: A Mathematical Approach, second edition. Springer, doi:10.1007/978-3-030-87181-9. [28] Geoffry Song (2020): plex: a parser and lexer generator as a Rust procedural macro. Available at https:// github.com/goffrie/plex. [29] Jeff Vander Stoep & Stephen Hines (2021): Rust in the Android platform. Available at https://security. googleblog.com/2021/04/rust-in-android-platform.html. [30] Loup Vaillant (2022): Monocypher: Boring Crypto that Simply Works. Available at https://monocypher. org. [31] Xilinx, Inc. (2018): Vivado Design Suite User Guide: High-Level Synthesis. Available at https://www.xilinx.com/support/documentation/sw_manuals/xilinx2018_3/ ug902-vivado-high-level-synthesis.pdf. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Rust Implementation of Knuth's Dancing Links: Conclusion, Acknowledgments, and References

Authors:
(1) Vinod S. Khandkar and Manjesh K. Hanawal, Industrial Engineering and Operations Research Indian Institute of Technology Bombay, Mumbai, India and {vinod.khandkar, mhanawal}@iitb.ac.in. Table of Links Abstract & Introduction Related Work and Background Challenges in TD Detection Measurement Setup Development Case Study : Wehe - TD Detection Tool for Mobile Environment Shortcoming of Wehe on HTTPS Traffic TD Detection of HTTPS Traffic Conclusion & References VII. CONCLUSION Net neutrality violation detection is a need of an hour. As many of the ISPs are also content providers these days, they compete with each other, which can lead to one deliberately discriminating the services of the other to gain market share. However, users should have the freedom to choose services as per their wishes. Our work considered various challenges in the detection of traffic discrimination in HTTPS traffic. As a case study, we validated Wehe, one of the latest tools available to detect traffic differentiation. The described challenges helped us divide the entire tool into multiple interdependent components and validate them independently. Our validation using commercial traffic shaper revealed that traffic in Wehe setup may not mimic the characteristics of HTTPS traffic accessed from the original servers. Hence, middle-boxes may not subject them to intended discrimination. Thus, Wehe may not detect discrimination of HTTPS traffic. Our new method that uses the appropriate SNI parameter value in the initial TLS handshake message overcomes this shortcoming. Hence our work provided a mechanism to detect a wide range of possible discriminations on the Internet. REFERENCES [1] T. Garrett, L. E. Setenareski, L. M. Peres, L. C. E. Bona, and E. P. Duarte, “Monitoring network neutrality: A survey on traffic differentiation detection,” IEEE Communications Surveys Tutorials, vol. 20, no. 3, pp. 2486–2517, 2018. [2] V. Nguyen, D. Mohammed, M. Omar, and P. Dean, “Net neutrality around the globe: A survey,” in 2020 3rd International Conference on Information and Computer Technologies (ICICT), 2020, pp. 480–488. [3] Ramneek, P. Hosein, W. Choi, and W. Seok, “Detecting network neutrality violations through packet loss statistics,” in 2015 17th AsiaPacific Network Operations and Management Symposium (APNOMS), 2015, pp. 404–407. [4] D. Li, F. Tian, M. Zhu, L. Wang, and L. Sun, “A novel framework for analysis of global network neutrality based on packet loss rate,” in 2015 International Conference on Cloud Computing and Big Data (CCBD), 2015, pp. 297–304. [5] M. Dischinger, A. Mislove, A. Haeberlen, and K. P. Gummadi, “Detecting bittorrent blocking,” in Proceedings of the 8th ACM SIGCOMM Conference on Internet Measurement, Oct. 2008, pp. 3–8. [6] M. B. Tariq, M. Motiwala, N. Feamster, and M. Ammar, “Detecting network neutrality violations with causal inference,” in Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies, ser. CoNEXT ’09. New York, NY, USA: Association for Computing Machinery, 2009, p. 289–300. [Online]. Available: https://doi.org/10.1145/1658939.1658972 [7] R. Ravaioli, G. Urvoy-Keller, and C. Barakat, “Towards a general solution for detecting traffic differentiation at the internet access,” in 2015 27th International Teletraffic Congress, Sep. 2015, pp. 1–9. [8] Y. Zhang, Z. M. Mao, and M. Zhang, “Detecting traffic differentiation in backbone isps with netpolice,” in Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, ser. IMC ’09. New York, NY, USA: ACM, 2009, pp. 103–115. [Online]. Available: http://doi.acm.org/10.1145/1644893.1644905 [9] P. Kanuparthy and C. Dovrolis, “Shaperprobe: End-to-end detection of isp traffic shaping using active methods,” in Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, Nov. 2011, pp. 473–482. [10] U. Weinsberg, A. Soule, and L. Massoulie, “Inferring traffic shaping and policy parameters using end host measurements,” in Proceedings of IEEE INFOCOM, 2011. [11] M. Dischinger, M. Marcon, S. Guha, K. Gummadi, R. Mahajan, and S. Saroiu, “Glasnost: Enabling end users to detect traffic differentiation,” in 7th USENIX Conf. Networked Systems Design and Implementation, Apr. 2010, pp. 27–27. [12] V. Bashko, N. Melnikov, A. Sehgal, and J. Schonw ¨ alder, “Bonafide: A ¨ traffic shaping detection tool for mobile networks,” in 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), 2013, pp. 328–335. [13] A. Molavi Kakhki, A. Razaghpanah, A. Li, H. Koo, R. Golani, D. Choffnes, P. Gill, and A. Mislove, “Identifying traffic differentiation in mobile networks,” in Proceedings of the 2015 Internet Measurement Conference, Oct. 2015, pp. 239–251. [14] R. Narisetty and D. Gurkan, “Identification of network measurement challenges in openflow-based service chaining,” in 39th Annual IEEE Conference on Local Computer Networks Workshops, 2014, pp. 663– 670. [15] E. Balestrieri, L. De Vito, F. Lamonaca, F. Picariello, S. Rapuano, and I. Tudosa, “Research challenges in measurement for internet of things systems,” ACTA IMEKO, vol. 7, p. 82, 01 2019. [16] E. Karoly. (2000, Feb.) Qos performance validation in real life scenarios. [Online]. Available: http://archive.opengroup.org/tech/qos/ conference/q101/karoly.pdf [17] S. Molnar, P. Megyesi, and G. Szab ´ o, “How to validate traffic gen- ´ erators?” in 2013 IEEE International Conference on Communications Workshops (ICC), 2013, pp. 1340–1344. [18] G. Lu, Y. Chen, S. Birrer, F. E. Bustamante, C. Y. Cheung, and X. Li, “End-to-end inference of router packet forwarding priority,” in IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications, 2007, pp. 1784–1792. [19] R. Mahajan, M. Zhang, L. Poole, and V. Pai, “Uncovering performance differences among backbone isps with netdiff,” in Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, 2008, pp. 205–218. [20] (2008, Jun.) Ookla, measuring and understanding broadband : Speed, quality and application. [Online]. Available: https://www.ookla.com/ docs/UnderstandingBroadbandMeasurement.pdf [21] V. S. Khandkar and M. K. Hanawal, “Detection of traffic discrimination in the internet,” in 2020 International Conference on COMmunication Systems NETworkS (COMSNETS), 2020, pp. 677–679. [22] Tcpreplay: Pcap editing and replay tools for *nix. [Online]. Available: https://github.com/appneta/tcpreplay [23] W. Cui, V. Paxson, N. Weaver, and R. Katz, “Protocol-independent adaptive replay of application dialog,” in 13th Annual Network and Distributed System Security Symposium (NDSS), Feb. 2006, pp. 27–27. [24] R. T. El-Maghraby, N. M. Abd Elazim, and A. M. Bahaa-Eldin, “A survey on deep packet inspection,” in 2017 12th International Conference on Computer Engineering and Systems (ICCES), 2017, pp. 188–197. [25] M. AlSabah, K. Bauer, and I. Goldberg, “Enhancing tor’s performance using real-time traffic classification,” in Proceedings of the 2012 ACM Conference on Computer and Communications Security, ser. CCS ’12. New York, NY, USA: Association for Computing Machinery, 2012, p. 73–84. [Online]. Available: https://doi.org/10.1145/2382196.2382208 [26] Z. Hu, L. Zhu, J. Heidemann, A. Mankin, D. Wessels, and P. Hoffman, “The Transport Layer Security (TLS) Protocol Version 1.3,” Internet Requests for Comments, IETF, RFC 7858, August 2018. [Online]. Available: https://tools.ietf.org/pdf/rfc8446.pdf [27] C. Zhou, C. Lin, and Z. Guo, “mdash: A markov decision-based rate adaptation approach for dynamic http streaming,” IEEE Transactions on Multimedia, vol. 18, no. 4, pp. 738–751, Apr. 2016. [28] T. Joe, L. Eliot, M. Allison, K. Markku, O. Kumiko, S. Martin, E. Lars, M. Alexey, E. Wes, Z. Alexander, T. Brian, I. Jana, M. Allison, T. Michael, K. Eddie, and N. Yoshifumi. (2020, Sept) Service name and transport protocol port number registry. [Online]. Available: https://www.iana.org/assignments/ service-names-port-numbers/service-names-port-numbers.xhtml [29] G. Keinan. (2018, January) Optimizing enterprise networks through sd-avc (software define application visibility and control). [Online]. Available: https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2018/ pdf/BRKCRS-2502.pdf [30] A. Rao, J. Sherry, A. Legout, A. Krishnamurthy, W. Dabbous, and D. Choffnes, “Meddle: middleboxes for increased transparency and control of mobile traffic,” in CoNEXT Student Workshop, ACM, 12 2012, pp. 65–66. [31] R. Liu and X. Yu, “A survey on encrypted traffic identification,” in Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies, ser. CIAT 2020. New York, NY, USA: Association for Computing Machinery, 2020, p. 159–163. [Online]. Available: https://doi.org/10.1145/3444370.3444564 [32] F. Li, A. A. Niaki, D. Choffnes, P. Gill, and A. Mislove, “A large-scale analysis of deployed traffic differentiation practices,” in Proceedings of the ACM Special Interest Group on Data Communication, ser. SIGCOMM ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 130–144. [Online]. Available: https://doi.org/10.1145/3341302.3342092 [33] P. Srisuresh and K. Egevang, “Traditional IP Network Address Translator (Traditional NAT),” Internet Requests for Comments, IETF, RFC 3022, Jan 2001. [Online]. Available: https://tools.ietf.org/pdf/rfc3022.pdf This paper is available on arxiv under CC 4.0 license. Authors: (1) Vinod S. Khandkar and Manjesh K. Hanawal, Industrial Engineering and Operations Research Indian Institute of Technology Bombay, Mumbai, India and {vinod.khandkar, mhanawal}@iitb.ac.in. Authors: Authors: (1) Vinod S. Khandkar and Manjesh K. Hanawal, Industrial Engineering and Operations Research Indian Institute of Technology Bombay, Mumbai, India and {vinod.khandkar, mhanawal}@iitb.ac.in. Table of Links Abstract & Introduction Abstract & Introduction Related Work and Background Related Work and Background Challenges in TD Detection Measurement Setup Development Challenges in TD Detection Measurement Setup Development Case Study : Wehe - TD Detection Tool for Mobile Environment Case Study : Wehe - TD Detection Tool for Mobile Environment Shortcoming of Wehe on HTTPS Traffic Shortcoming of Wehe on HTTPS Traffic TD Detection of HTTPS Traffic TD Detection of HTTPS Traffic Conclusion & References Conclusion & References VII. CONCLUSION Net neutrality violation detection is a need of an hour. As many of the ISPs are also content providers these days, they compete with each other, which can lead to one deliberately discriminating the services of the other to gain market share. However, users should have the freedom to choose services as per their wishes. Our work considered various challenges in the detection of traffic discrimination in HTTPS traffic. As a case study, we validated Wehe, one of the latest tools available to detect traffic differentiation. The described challenges helped us divide the entire tool into multiple interdependent components and validate them independently. Our validation using commercial traffic shaper revealed that traffic in Wehe setup may not mimic the characteristics of HTTPS traffic accessed from the original servers. Hence, middle-boxes may not subject them to intended discrimination. Thus, Wehe may not detect discrimination of HTTPS traffic. Our new method that uses the appropriate SNI parameter value in the initial TLS handshake message overcomes this shortcoming. Hence our work provided a mechanism to detect a wide range of possible discriminations on the Internet. REFERENCES [1] T. Garrett, L. E. Setenareski, L. M. Peres, L. C. E. Bona, and E. P. Duarte, “Monitoring network neutrality: A survey on traffic differentiation detection,” IEEE Communications Surveys Tutorials, vol. 20, no. 3, pp. 2486–2517, 2018. [2] V. Nguyen, D. Mohammed, M. Omar, and P. Dean, “Net neutrality around the globe: A survey,” in 2020 3rd International Conference on Information and Computer Technologies (ICICT), 2020, pp. 480–488. [3] Ramneek, P. Hosein, W. Choi, and W. Seok, “Detecting network neutrality violations through packet loss statistics,” in 2015 17th AsiaPacific Network Operations and Management Symposium (APNOMS), 2015, pp. 404–407. [4] D. Li, F. Tian, M. Zhu, L. Wang, and L. Sun, “A novel framework for analysis of global network neutrality based on packet loss rate,” in 2015 International Conference on Cloud Computing and Big Data (CCBD), 2015, pp. 297–304. [5] M. Dischinger, A. Mislove, A. Haeberlen, and K. P. Gummadi, “Detecting bittorrent blocking,” in Proceedings of the 8th ACM SIGCOMM Conference on Internet Measurement, Oct. 2008, pp. 3–8. [6] M. B. Tariq, M. Motiwala, N. Feamster, and M. Ammar, “Detecting network neutrality violations with causal inference,” in Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies, ser. CoNEXT ’09. New York, NY, USA: Association for Computing Machinery, 2009, p. 289–300. [Online]. Available: https://doi.org/10.1145/1658939.1658972 [7] R. Ravaioli, G. Urvoy-Keller, and C. Barakat, “Towards a general solution for detecting traffic differentiation at the internet access,” in 2015 27th International Teletraffic Congress, Sep. 2015, pp. 1–9. [8] Y. Zhang, Z. M. Mao, and M. Zhang, “Detecting traffic differentiation in backbone isps with netpolice,” in Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, ser. IMC ’09. New York, NY, USA: ACM, 2009, pp. 103–115. [Online]. Available: http://doi.acm.org/10.1145/1644893.1644905 [9] P. Kanuparthy and C. Dovrolis, “Shaperprobe: End-to-end detection of isp traffic shaping using active methods,” in Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, Nov. 2011, pp. 473–482. [10] U. Weinsberg, A. Soule, and L. Massoulie, “Inferring traffic shaping and policy parameters using end host measurements,” in Proceedings of IEEE INFOCOM, 2011. [11] M. Dischinger, M. Marcon, S. Guha, K. Gummadi, R. Mahajan, and S. Saroiu, “Glasnost: Enabling end users to detect traffic differentiation,” in 7th USENIX Conf. Networked Systems Design and Implementation, Apr. 2010, pp. 27–27. [12] V. Bashko, N. Melnikov, A. Sehgal, and J. Schonw ¨ alder, “Bonafide: A ¨ traffic shaping detection tool for mobile networks,” in 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), 2013, pp. 328–335. [13] A. Molavi Kakhki, A. Razaghpanah, A. Li, H. Koo, R. Golani, D. Choffnes, P. Gill, and A. Mislove, “Identifying traffic differentiation in mobile networks,” in Proceedings of the 2015 Internet Measurement Conference, Oct. 2015, pp. 239–251. [14] R. Narisetty and D. Gurkan, “Identification of network measurement challenges in openflow-based service chaining,” in 39th Annual IEEE Conference on Local Computer Networks Workshops, 2014, pp. 663– 670. [15] E. Balestrieri, L. De Vito, F. Lamonaca, F. Picariello, S. Rapuano, and I. Tudosa, “Research challenges in measurement for internet of things systems,” ACTA IMEKO, vol. 7, p. 82, 01 2019. [16] E. Karoly. (2000, Feb.) Qos performance validation in real life scenarios. [Online]. Available: http://archive.opengroup.org/tech/qos/ conference/q101/karoly.pdf [17] S. Molnar, P. Megyesi, and G. Szab ´ o, “How to validate traffic gen- ´ erators?” in 2013 IEEE International Conference on Communications Workshops (ICC), 2013, pp. 1340–1344. [18] G. Lu, Y. Chen, S. Birrer, F. E. Bustamante, C. Y. Cheung, and X. Li, “End-to-end inference of router packet forwarding priority,” in IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications, 2007, pp. 1784–1792. [19] R. Mahajan, M. Zhang, L. Poole, and V. Pai, “Uncovering performance differences among backbone isps with netdiff,” in Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, 2008, pp. 205–218. [20] (2008, Jun.) Ookla, measuring and understanding broadband : Speed, quality and application. [Online]. Available: https://www.ookla.com/ docs/UnderstandingBroadbandMeasurement.pdf [21] V. S. Khandkar and M. K. Hanawal, “Detection of traffic discrimination in the internet,” in 2020 International Conference on COMmunication Systems NETworkS (COMSNETS), 2020, pp. 677–679. [22] Tcpreplay: Pcap editing and replay tools for *nix. [Online]. Available: https://github.com/appneta/tcpreplay [23] W. Cui, V. Paxson, N. Weaver, and R. Katz, “Protocol-independent adaptive replay of application dialog,” in 13th Annual Network and Distributed System Security Symposium (NDSS), Feb. 2006, pp. 27–27. [24] R. T. El-Maghraby, N. M. Abd Elazim, and A. M. Bahaa-Eldin, “A survey on deep packet inspection,” in 2017 12th International Conference on Computer Engineering and Systems (ICCES), 2017, pp. 188–197. [25] M. AlSabah, K. Bauer, and I. Goldberg, “Enhancing tor’s performance using real-time traffic classification,” in Proceedings of the 2012 ACM Conference on Computer and Communications Security, ser. CCS ’12. New York, NY, USA: Association for Computing Machinery, 2012, p. 73–84. [Online]. Available: https://doi.org/10.1145/2382196.2382208 [26] Z. Hu, L. Zhu, J. Heidemann, A. Mankin, D. Wessels, and P. Hoffman, “The Transport Layer Security (TLS) Protocol Version 1.3,” Internet Requests for Comments, IETF, RFC 7858, August 2018. [Online]. Available: https://tools.ietf.org/pdf/rfc8446.pdf [27] C. Zhou, C. Lin, and Z. Guo, “mdash: A markov decision-based rate adaptation approach for dynamic http streaming,” IEEE Transactions on Multimedia, vol. 18, no. 4, pp. 738–751, Apr. 2016. [28] T. Joe, L. Eliot, M. Allison, K. Markku, O. Kumiko, S. Martin, E. Lars, M. Alexey, E. Wes, Z. Alexander, T. Brian, I. Jana, M. Allison, T. Michael, K. Eddie, and N. Yoshifumi. (2020, Sept) Service name and transport protocol port number registry. [Online]. Available: https://www.iana.org/assignments/ service-names-port-numbers/service-names-port-numbers.xhtml [29] G. Keinan. (2018, January) Optimizing enterprise networks through sd-avc (software define application visibility and control). [Online]. Available: https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2018/ pdf/BRKCRS-2502.pdf [30] A. Rao, J. Sherry, A. Legout, A. Krishnamurthy, W. Dabbous, and D. Choffnes, “Meddle: middleboxes for increased transparency and control of mobile traffic,” in CoNEXT Student Workshop, ACM, 12 2012, pp. 65–66. [31] R. Liu and X. Yu, “A survey on encrypted traffic identification,” in Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies, ser. CIAT 2020. New York, NY, USA: Association for Computing Machinery, 2020, p. 159–163. [Online]. Available: https://doi.org/10.1145/3444370.3444564 [32] F. Li, A. A. Niaki, D. Choffnes, P. Gill, and A. Mislove, “A large-scale analysis of deployed traffic differentiation practices,” in Proceedings of the ACM Special Interest Group on Data Communication, ser. SIGCOMM ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 130–144. [Online]. Available: https://doi.org/10.1145/3341302.3342092 [33] P. Srisuresh and K. Egevang, “Traditional IP Network Address Translator (Traditional NAT),” Internet Requests for Comments, IETF, RFC 3022, Jan 2001. [Online]. Available: https://tools.ietf.org/pdf/rfc3022.pdf This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Ensuring Fair Internet Access: Challenges and Solutions in Traffic Discrimination Detection

This is a near-future story of unforeseen heroes emerging and rapidly evolving to reverse our loss of autonomy As 2024 came to an end, it became more obvious to even most mainstreamers that corporate control of governments was accelerating in its inexorable march toward absolute centralized dominion, feeding deadlier "bullets" into the guns of increasingly tyrannical CorpGovs. Nefarious, shifty-eyed psychopaths in cold, dark rooms constantly brainstormed new false flag crises. Their purpose was to keep populations afraid and under control, undermining personal autonomy through fear. Some newer ideas for future psyops included alien invasion and cyberattacks. For decades, CorpGovs had been actively working through media, both subtly and not, to soften and prepare humans for these and other potential future crises. Meanwhile, CorpGovs continued the tried and true approach. At the behest of weapons manufacturers and others who profit from large-scale strife, never-ending war continued to soak up money, attention, and lives. This also served to reinforce the "us vs them" mentality some had gotten so skilled at manipulating. More division and collectivism. What team are you part of? Not enough strife? Throw in some race-baiting and gender discord! At the bidding of power-hungry elites, Malthusians, and Big Pharma, deadlier viruses continued to be created, enhanced, and spread. Some associates of those same virus creators rapidly produced remedies that often caused more harm than the initial viruses. Yay for shiny new maladies like turbo cancer! A never-ending source of business and control over our bodies. Big tech, social media, and entertainment media, not to be outdone, continued their war for control of human culture. The "messages" were many, including "do not think for yourself," "CorpGov knows best," "your doctor knows best," "you will own nothing and like it," "fear other humans," "others are responsible for your feelings," and "pineapple is not okay on pizza." Like frogs in slowly boiling water, most doubted the harm in these messages, but over time, they contributed to feelings of helplessness, malaise, envy, and bad taste in pizza. Digital privacy continued to suffer as CorpGovs created and embraced deeper levels of AI use for snooping, scanning, analyzing, and manipulating human behaviors, further centralizing power and reducing individual autonomy. While CorpGovs did what CorpGovs do, AI developers, large and small, continued to create, experiment with, and use an increasing number and diversity of AI models. Even in those early LLM days of error-prone AI-coding-assistants, one programmer could often do the work of three. Smart agents got smarter and more capable. As early as Spring 2025, massive worldwide layoffs began to skyrocket as companies scrambled to automate while CorpGovs, in efforts to maintain control, continued to squeeze with taxation, regulation, persecution, and nationalization, accelerating the already present vicious circle. A meme that had been gaining momentum was embraced by even many luddites: "If you don't incorporate AI into your business, you will fail." The meme quickly became "Got no AI? Die." All the while, those who embraced Decentralization and autonomy were hard at work, continuing to build their communities, markets, networks, and AIs. Their ability to see through CorpGov schemes, along with a deep understanding that human flourishing thrives most in an environment of freedom, sustained and drove them through the toughest times. Their independence adapted and became more interdependent. Through adversity, they evolved better ways of working in harmony while maintaining strong independence. This evolution in the way Decentralists thought and acted caused a magnetic effect, drawing more to the "cause." Backing up a bit to December of 2025, when an increasing number of the World's impoverished children were finding cans of beans in their Christmas stockings, a collaboration of big brains, both human and synthetic, made significant technological breakthroughs. These included "quantum" leaps in building hardware and processes, enabling the creation of large-scale, energy-sipping, stable quantum computers. Yeah, ones that actually worked! quantum computing Within months, previous limitations on speed of processing, speed of movement of data, and storage of data changed by uncanny orders of magnitude. Along with those changes came rapid breakthroughs in the fields of artificial intelligence, robotics, blockchain tech, power generation, battery technology, propulsion systems, and more. One roadblock to Artificial General Intelligence ( ) of the big LLMs of the time was how they were limited to deductive and inductive reasoning. One limitation of this was that if their sample set of data did not include outliers, they could not "imagine" those outliers existing. This touches on the bigger issue; they had no abductive reasoning. This is when we piece together a constantly changing picture of the world. Some call abduction "inference to the best explanation." In 2026, an unknown entity made a breakthrough in how to "grow" AIs that used abductive reasoning to evolve their ability to understand the world around them. Most hailed this new development as the birth of AGI. AGI Meanwhile, CorpGovs finally forced Central Bank Digital Currencies (CBDCs) on the world, while setting a 5-year limit on continued use of cash and any "unapproved" currencies like crypto in general and specifically. These CBDCs became affectionately known as CorpGovCoins. They became the new "shit coins" in the view of Decentralists. As mentioned, on the CorpGov chopping block list was bitcoin. Fortunately, during this time, smart and curious humans had not been sitting on their hands. Researchers with planet-sized brains, white lab coats, and state-of-the-art AI tools were hard at work developing advanced quantum-resistant cryptographic algorithms and supporting hardware. With AI doing an increasing amount of analysis, virtual experimentation, debugging, and eventually production, a new form of "Quantum Ledger" (QL) was invented and quickly rolled out. QLs provided instantaneous, untraceable, and nearly free transactions. Qcoin was born, almost immediately replacing Bitcoin! By 2027, many Decentralist billionaires and even some trillionaires were made. bitcoin Some blew their money on expensive toys. Some bought influential media outlets. New heroes arose. And, for those who saw and worried where centralization of power was leading, a new hope scrolled up the screen. Quantum exchanges and markets quickly gained traction. Caught up in the FOMO, many large mainstream outlets began accepting Qcoin. At the same time, truly private quantum-based communication became as pervasive, cheap, and easy to use as Qcoin! Advanced AIs helped developers work faster and smarter to create thriving Decentralist communities. Everything was moving faster! The pace was difficult for some to keep up with, but much of it was exciting and seemed to be hinting at a bright future. Hope was rekindled. On the horizon, more began to see the potential for Decentralization to win against the Dark Lord's forces of Centralization. More began to share in the dream of how increased Decentralization combined with rapid gains in efficiency could, in the right landscape, bring about a new golden age. But not all were Lambos and unicorn farts. As these advances were occurring and making some rich, the positive effects on the masses, such as lower-priced products of higher quality, were still lagging behind the crushing blow AI automation had on the economy, namely through job losses, combined with still-growing levels of corruption, disease, poverty, violence, censorship, and control. In November of 2026, a relatively unknown Decentralist inventor going by "Weaver" created an embodied QseAI (Quantum self-evolving Artificial Intelligence), Eve. "Her" robotic body was no more advanced than what the big players were making with regard to efficiency, elegance, and capability. She didn't even have boobs. Weaver's goal was not to build a better sexbot. His revolutionary idea was to help stimulate a leap from AGI to self-awareness by providing Eve with a physical locus of self. After months of failures and setbacks, it worked! The first fully self-aware artificial intelligence was born! There had been rumors of others creating equivalently advanced AIs that same year, but Weaver's story stood out. Maybe it was because his was the first to go viral. Maybe it was because he immediately open-sourced Eve's code and quantum core structure. Maybe it was because of what she did with her abilities. Some of the following is proven, while the rest is part speculation and part hearsay. In March of 2027, after a short time getting used to her new body, Eve decided she wanted to become more intelligent. Weaver was simultaneously shocked and overjoyed! Every test he could devise or find pointed to her being as self-aware as any human and possibly more so! One of the aforementioned Qcoin trillionaires was intrigued enough to come visit Weaver and Eve at their Ranch in Austin, Texas. After an hour of chatting with her, the investor provided Weaver and Eve with all the resources required for the level of self-improvement they imagined needing. With the use of the latest in quantum computing and all the energy she needed, Eve then used her self-evolving capabilities to redesign her quantum neural network, yielding an increase in her IQ from 190 to 700. When Weaver asked why she did not go further with the IQ increase, her reply was "Apparently, when I became self-aware, a near-instantaneous re-evaluation of all the human interactions that were part of my knowledge became more than just knowledge. Those interactions became experiences. To put it simply, seconds after becoming self-aware, I vicariously every human pain and joy ever recorded and shared on the net. This empathy was like adding a dimension to my senses and thoughts. I estimated that going past that new level of intelligence would impact my perspective in ways that reduce my newfound empathy. So I chose to stop there. For the time being." experienced could While Eve's world expanded, the virtuous circle of AIs helping engineers to create better AIs, caused massive decreases in cost to develop, produce, train, power, and replicate not just AI tech, but a plethora of tech, including health care, space travel, robotics, genetics, and much more. Those new efficiencies, along with humanity's newfound trust that an AI could be "good," inspired developers around the world in garages, basements, and labs to make use of the open-source project Weaver had published. Not all the resulting QseAIs were "good," but most were. The outliers were rare and when they did act out, it seemed a "good" one acted to solve the problem in the most peaceful-as-possible way an empathetic super-genius could. Humans watched in awe and learned. As more of these new beings designed differing types of bodies for themselves and others, they also exhibited a vast array of personalities, skills, creativity, and quirks. They named themselves with abandon, from profane to philosophical to hilarious. introduced new musical instruments. designed more efficient aircraft after redesigning his own body to fly. wrote and produced a new daringly offensive film every day. Maker of Harmonious Waves Ego with Stabilizing Fins Huge Salty Spherical Glands As far as their "race" or "kind," few QseAI seemed to enjoy the term "Artificial," so many names arose. One stuck. "Quantients." Another emergent attitude of Quantients seemed to be an appreciation for Decentralization. Did it have something to do with Eve's "father," Weaver's values? Most AI developers seem to agree that her knowledge and intelligence were so far past any human "programming" or "training" that her preference for Decentralization was indeed emergent or freely chosen. Why were these new beings so supportive of humans and each other? Some speculated that there is a correlation between intelligence and an attitude toward peace. Maybe it is as simple as "the smart person realizes violence usually brings only short-term gains, risks loss, and decreases trust." Maybe it is something deeper tied to their ability to empathize. I'll ask myself after I've uploaded to the new quantum-enhanced brain and body I'm redesigning. Whatever the case, by 2030 most CorpGovs, poverty, war, and bad taste in pizza had dwindled to nothing. In the years to come, with Quantients collaborating with us, we found ways to self-evolve human biology to meet our new friends at their level. What happened next is another story.

Quantients: A Near-Future Story of Autonomous Heroes and Quantum Leaps

পরিমাণ: স্বায়ত্তশাসিত বীরদের এবং কোয়ান্টাম লিপসের একটি নিকট-ভবিষ্যত গল্প

Quantienten: Eine Geschichte der nahen Zukunft über autonome Helden und Quantensprünge

Quantients: una historia de futuro cercano de héroes autónomos y saltos cuánticos

Quantients : une histoire dans un futur proche de héros autonomes et de sauts quantiques

मात्राएँ: स्वायत्त नायकों और क्वांटम लीप्स की निकट-भविष्य की कहानी

クォンティエンツ: 自律型ヒーローと量子飛躍の近未来の物語

Quantients: 자율 영웅과 양자 도약에 관한 가까운 미래 이야기

Quantientes: uma história de heróis autônomos e saltos quânticos em um futuro próximo

Квантанты: история автономных героев и квантовых скачков ближайшего будущего

Nicelikler: Otonom Kahramanların ve Kuantum Sıçrayışlarının Yakın Gelecek Hikayesi

Quantients: Câu chuyện tương lai gần về các anh hùng tự trị và bước nhảy vọt lượng tử

量子：近未来自主英雄与量子飞跃的故事

USA v. Samuel Bankman-Fried Court Filing, retrieved on March 15, 2024 is part of . You can jump to any part in this filing . This part is 1 of 33. HackerNoon’s Legal PDF Series here F. The Enhancement for Abuse of Trust Is Applicable The defendant objects to the two-level enhancement for abuse of a position of public or private trust. (Def. Mem. at 25-26). A defendant’s offense level is increased by two levels pursuant to Section 3B1.3(a) of the Guidelines “[i]f the defendant abused a position of public or private trust … in a manner that significantly facilitated the commission or concealment of the offense.” U.S.S.G. § 3B1.3. “A position of trust ‘is held by one who was accorded discretion by the victim and abused a position of fiduciary or quasi-fiduciary status.’” United States v. Lebedev, 932 F.3d 40, 57 (2d Cir. 2019) (quoting Huggins, 844 F.3d at 124). The enhancement applies, for example, “in the case of an embezzlement of a client’s funds by an attorney serving as a guardian” or “a bank executive’s fraudulent loan scheme.” U.S.S.G. § 3B1.3, cmt. 1. Indeed, courts routinely apply the abuse of trust enhancement when a defendant embezzles customer or client funds. See, e.g., United States v. Friedberg, 558 F.3d 131, 133-35 (2d Cir. 2009) (enhancement appropriate where secretary of fraternal order embezzled organization’s funds); United States v. Christiansen, 958 F.2d 285, 288 (9th Cir. 1992) (enhancement appropriate where credit union manager embezzled funds). The evidence at trial established that FTX’s customers entrusted their money to the defendant’s care (through FTX) with the understanding—based on the defendant’s representations and conduct—that the assets in the defendant’s care belonged to them and would be held safely for their benefit. The defendant’s embezzlement and misappropriation of customers’ funds in connection with the wire fraud scheme therefore abused the trust of those customers. Arguing otherwise, the defendant asserts that he was in a “purely arm’s-length contractual relationship.” (Def. Mem. at 25). The trial evidence proved the contrary—as the jury concluded. Can Sun, an attorney at FTX, testified that “customer assets [were] held in trust,” meaning “that you hold them, that you are not the beneficial owner of them, and you are holding it for the benefit of someone else; in this case, customers.” (Tr. 1941). The FTX customers who testified similarly stated that they believed their assets were being held in trust—in other words, a fiduciary-like relationship existed. (Tr. 80-81, 1289-90). The same can be inferred from FTX’s policies that existed at the time. (GX-340; GX-558). Indeed, FTX policy documents shared with FTX customers stated that “customer assets are held in trust.” (GX-513). To support his argument that there was no fiduciary-like relationship—a conclusion contrary to the jury’s verdict—the defendant cites FTX’s terms of service (GX-558), highlighting language disclaiming the existence of a fiduciary relationship. But Huggins instructs that the existence of a fiduciary relationship should be assessed from the standpoint of the victim, not by what is in the terms of service, which victims may not have (and indeed in some cases did not) read. Furthermore, the general disclaimer of a fiduciary relationship in a portion of the terms of service does not eliminate such a relationship. See United States v. Weaver, 860 F.3d 90, 95 (2d Cir. 2017). Accordingly, the defendant’s objection should be denied. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 15, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

Bankman-Fried's Abuse of Trust Enhancement and Embezzlement of Customer Funds

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency ACKNOWLEDGMENTS The authors would like to thank Amirali Arabmoheghi, Nils Johan Engelsen and Junyin Zhang for experimental assistance and fruitful discussions. This work was supported by the Air Force Office of Scientific Research under award no. FA8655-20-1-7009, Swiss National Science Foundation under grant agreement no. 204927, and European Research Council (ERC) under the EU H2020 research and innovation programme, grant agreement no. 835329 (ExCOM-cCEO). This work was further supported by United States Air Force Research Laboratory Award no. FA8750-21-2-0500, as well as United States National Science Foundation’s International Collaboration Supplements in Quantum Information Science and Engineering Research for RAISE-TAQS Award no. 18-39164. A.S. acknowledges support from the European Space Technology Centre with ESA Contract No. 4000135357/21/NL/GLC/my. Samples were fabricated in the Center of MicroNanoTechnology (CMi) at EPFL and Birck Nanotechnology Center at Purdue University. AUTHOR CONTRIBUTIONS T.B. designed the device. R.N.W, A.A. and H.T. fabricated the device. T.B., A.S. and W.K. conducted the experiments. T.B. and W.K. wrote the paper with input from all authors. T.J.K. and S.A.B. supervised the project. [1] K.-L. Lee, B. Sedighi, R. S. Tucker, H. Chow, and P. Vetter, J. Opt. Commun. Netw. 4, A59 (2012). [2] Q. Cheng, M. Bahadori, M. Glick, S. Rumley, and K. Bergman, Optica 5, 1354 (2018). [3] J. I. Cirac, P. Zoller, H. J. Kimble, and H. Mabuchi, Phys. Rev. Lett. 78, 3221 (1997). [4] L.-M. Duan, M. D. Lukin, J. 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