Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
awesome-zkml
awesome-zkml repository
https://github.com/worldcoin/awesome-zkml
Last synced: 4 days ago
JSON representation
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Content
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Articles and podcasts
- An introduction to zero-knowledge machine learning - Worldcoin
- Zero Gravity (The Weight is Over) - ZKHack Lisbon
- Zero-Knowledge Decision Tree Prediction (ZK-DTP) - ZKHack Lisbon
- Open-sourcing zkml: Trustless Machine Learning for All - [Daniel Kang](https://twitter.com/daniel_d_kang)
- Checks and balances: Machine learning and zero-knowledge proofs - [Elena Burger @ a16z](https://twitter.com/virtualelena/)
- ZKML: Bridging AI/ML and Web3 with Zero-Knowledge Proofs - [Cathie So](https://twitter.com/drCathieSo_eth)
- Balancing the Power of AI/ML: The Role of ZK and Blockchain - SevenX Ventures
- Verified Execution of GPT, Bert, CLIP, and more - [Daniel Kang](https://twitter.com/daniel_d_kang)
- Zero Knowledge Machine Learning - [Remco Bloemen](https://twitter.com/recmo)
- Zero-Knowledge Proofs and Their Applications to Machine Learning (video)
- ZK for ML
- Zero-Knowledge Machine Learning
- Chapter 2: Why Put Your AI On-Chain?
- Chapter 3: The World’s First On-Chain AI Trading Bot
- Chapter 4: Blockchains that Self-Improve
- Chapter 5: The Cost of Intelligence
- Trustless Verification of Machine Learning
- ZK Podcast - episode 265: Where ZK and ML intersect with Yi Sun and Daniel Kang
- zkML: Evolving the Intelligence of Smart Contracts Through Zero-Knowledge Cryptography - 1kx
- Dcbuilder - Zero-Knowledge Machine Learning and its use cases
- ZK10: ZKML with EZKL: Where we are and the future - [Jason Morton (ZKonduit)](https://twitter.com/jasonmorton)
- ZK10: ZKML Endgame: Specialized ZK Proving with GKR - [Ryan Cao (Modulus)](https://twitter.com/nayr_oac)
- Do language models possess knowledge (soundness)? - [Tarun Chitra](https://twitter.com/tarunchitra)
- Zero Knowledge Machine Learning - [Remco Bloemen](https://twitter.com/recmo)
- Zero-Knowledge Proofs and Their Applications to Machine Learning (video)
- ZK for ML
- Zero Knowledge Podcast - 2/)
- Zero-Knowledge Machine Learning
- Chapter 1: How to Put Your AI On-Chain
- Chapter 2: Why Put Your AI On-Chain?
- Chapter 3: The World’s First On-Chain AI Trading Bot
- Chapter 4: Blockchains that Self-Improve
- Chapter 5: The Cost of Intelligence
- Trustless Verification of Machine Learning
- ZK Podcast - episode 265: Where ZK and ML intersect with Yi Sun and Daniel Kang
- An introduction to zero-knowledge machine learning - Worldcoin
- Zero Gravity (The Weight is Over) - ZKHack Lisbon
- Zero-Knowledge Decision Tree Prediction (ZK-DTP) - ZKHack Lisbon
- Open-sourcing zkml: Trustless Machine Learning for All - [Daniel Kang](https://twitter.com/daniel_d_kang)
- Checks and balances: Machine learning and zero-knowledge proofs - [Elena Burger @ a16z](https://twitter.com/virtualelena/)
- ZKML: Bridging AI/ML and Web3 with Zero-Knowledge Proofs - [Cathie So](https://twitter.com/drCathieSo_eth)
- Balancing the Power of AI/ML: The Role of ZK and Blockchain - SevenX Ventures
- The Ultimate Guide to the ZKML ecosystem (Twitter thread) - [SevenX ventures](https://twitter.com/SevenXVentures)
- Verified Execution of GPT, Bert, CLIP, and more - [Daniel Kang](https://twitter.com/daniel_d_kang)
- zkML: Evolving the Intelligence of Smart Contracts Through Zero-Knowledge Cryptography - 1kx
- Dcbuilder - Zero-Knowledge Machine Learning and its use cases
- ZK10: ZKML with EZKL: Where we are and the future - [Jason Morton (ZKonduit)](https://twitter.com/jasonmorton)
- ZK10: ZKML Endgame: Specialized ZK Proving with GKR - [Ryan Cao (Modulus)](https://twitter.com/nayr_oac)
- Zero-Knowledge Machine Learning
- Zero Knowledge Machine Learning - [Remco Bloemen](https://twitter.com/recmo)
- Zero-Knowledge Proofs and Their Applications to Machine Learning (video)
- Chapter 2: Why Put Your AI On-Chain?
- Chapter 4: Blockchains that Self-Improve
- Chapter 5: The Cost of Intelligence
- Trustless Verification of Machine Learning
- Zero Gravity (The Weight is Over) - ZKHack Lisbon
- Zero-Knowledge Decision Tree Prediction (ZK-DTP) - ZKHack Lisbon
- Open-sourcing zkml: Trustless Machine Learning for All - [Daniel Kang](https://twitter.com/daniel_d_kang)
- ZKML: Bridging AI/ML and Web3 with Zero-Knowledge Proofs - [Cathie So](https://twitter.com/drCathieSo_eth)
- Balancing the Power of AI/ML: The Role of ZK and Blockchain - SevenX Ventures
- ZK10: ZKML Endgame: Specialized ZK Proving with GKR - [Ryan Cao (Modulus)](https://twitter.com/nayr_oac)
- Linear A Research
- ezkl blog
- Modulus blog
- Giza blog
- The promise and challenges of crypto + AI applications - Vitalik Buterin
- Chapter 3: The World’s First On-Chain AI Trading Bot
- TensorPlonk: A “GPU” for ZKML, Delivering 1,000x Speedups - [Daniel Kang](https://twitter.com/daniel_d_kang)
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ZKML community calls
- Telegram channel - community-calls).
- ZKML community call #0
- Telegram channel - community-calls).
- ZKML community call #0
- ZKML community call #0
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Learn ZK
- Curated list of ZKP implementations
- ZK research 0x
- ZK canon
- Proofs, Args and ZK - Justin Thaler
- zkProof Standards - Resource
- ZK Mesh - resource
- Curated list of ZKP implementations
- ZK research 0x
- ZK canon
- Proofs, Args and ZK - Justin Thaler
- ZK research 0x
- Ingopedia
- Awesome - Matter labs - ZK proofs
- Awesome - Mikerah - Privacy on Blockchains
- Resource: Awesome_Plonk
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Learn ML
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Use cases
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Articles and podcasts
- Modulus Labs
- RockyBot
- Lyra finance
- Lyra finance
- Astraly
- Aztec Protocol - rollup with privacy features)
- link
- Aztec Protocol - rollup with privacy features)
- vCNN paper, page 2/16
- Worldcoin
- exploitability
- vCNN paper, page 2/16
- WorldID - hosted biometric data and is calling [\_addMember(uint256 groupId, uint256 identityCommitment)](https://github.com/semaphore-protocol/semaphore/blob/4e6be04729ed2d7e29461a3915877a66a2c9c4d2/contracts/base/SemaphoreGroups.sol#L43) function on the WorldID Semaphore identity group with a valid identityCommitment. -> Makes protocol more permissionless
- Lyra finance
- RockyBot
- Lyra finance
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Codebases
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Articles and podcasts
- zk-mnist - [@hopanml](https://twitter.com/hopanml) [@sunfishstanford](https://twitter.com/fho888) [@henripal](https://twitter.com/henripal) (2022)
- zk-ml/demo - [@liaopeiyuan](https://twitter.com/LiaoPeiyuan) (2021)
- circomlib-ml - [@socathie](https://twitter.com/drCathieSo_eth) (2022)
- RockyBot - [@ModulusLabs](https://twitter.com/ModulusLabs) (2022)
- ezkl
- keras2circom
- Otti
- zk-mnist - [@hopanml](https://twitter.com/hopanml) [@sunfishstanford](https://twitter.com/fho888) [@henripal](https://twitter.com/henripal) (2022)
- circomlib-ml - [@socathie](https://twitter.com/drCathieSo_eth) (2022)
- Gitcoin Grant Proposal
- proto-neural-zkp - [@recmo](https://twitter.com/recmo)
- @dcbuild3r
- ezkl
- keras2circom
- Linear A - uchikoma
- Zator - Verified inference of a 512-layer neural network using recursive SNARKs.
- Otti
- Linear A - uchikoma
- zk-dtp - Zero Knowledge Decision Tree Predict is designed to address this pressing issue by offering privacy-preserving predictions using decision tree models, built on top of RISC Zero's zkVM.
- ZKaggle - [@socathie](https://twitter.com/drCathieSo_eth) (2023)
- ZKaggle - [@socathie](https://twitter.com/drCathieSo_eth) (2023)
- zk-mnist - [@hopanml](https://twitter.com/hopanml) [@sunfishstanford](https://twitter.com/fho888) [@henripal](https://twitter.com/henripal) (2022)
- circomlib-ml - [@socathie](https://twitter.com/drCathieSo_eth) (2022)
- ezkl
- Zator - Verified inference of a 512-layer neural network using recursive SNARKs.
- Otti
- Linear A - tachikoma
- Linear A - uchikoma
- zkp-gravity/0g - ZeroGravity - Zero Gravity is a system for proving an inference run (i.e. a classification) for a pre-trained, public Weightless NN and a private input. (2023)
- ddkang/zkml - zkml is a framework for constructing proofs of ML model execution in ZK-SNARKs.
- ZKaggle - [@socathie](https://twitter.com/drCathieSo_eth) (2023)
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Projects interested in ZKML
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Articles and podcasts
- zkMachineLearning - ZKML tooling for Circom
- proto-neural-zkp
- 0xPARC - The 0xPARC Foundation promotes application-level innovation on Ethereum and other decentralized platforms
- zkMnist
- Worldcoin - A Privacy-Preserving Proof-of-Personhood Protocol
- Gizatech - Fully on-chain artificial intelligence on Starknet
- Risc Zero - The General Purpose Zero-Knowledge VM
- Supranational - A product and service company developing hardware-accelerated cryptography for verifiable and confidential computing
- Zama.ai - ZK ML) - FHE tooling for machine learning, blockchain and more. ZK-FHE is an interesting research area. [FHE.org](https://fhe.org/) is a very interesting community with a lot of potential for collaboration.
- Aleo - Platform for building fully private and programmable Web applications.
- Ion Protocol - Lending protocol for staked & restaked assets. They partnered with Modulus to build a risk engine that analyzes validator credit risk. Read more [here](https://twitter.com/ionprotocol/status/1727193815218581726)
- Axiom - Axiom provides smart contracts trustless access to all on-chain data and arbitrary expressive compute over it. Like GPUs do for CPUs, Axiom augments blockchain consensus with zero-knowledge proofs
- 0xPARC - The 0xPARC Foundation promotes application-level innovation on Ethereum and other decentralized platforms
- Worldcoin - A Privacy-Preserving Proof-of-Personhood Protocol
- Gizatech - Fully on-chain artificial intelligence on Starknet
- Risc Zero - The General Purpose Zero-Knowledge VM
- Supranational - A product and service company developing hardware-accelerated cryptography for verifiable and confidential computing
- Ingonyama - Zero Knowledge ASICs (ZPU)
- Zama.ai - ZK ML) - FHE tooling for machine learning, blockchain and more. ZK-FHE is an interesting research area. [FHE.org](https://fhe.org/) is a very interesting community with a lot of potential for collaboration.
- Aleo - Platform for building fully private and programmable Web applications.
- Ion Protocol - Lending protocol for staked & restaked assets. They partnered with Modulus to build a risk engine that analyzes validator credit risk. Read more [here](https://twitter.com/ionprotocol/status/1727193815218581726)
- zkMnist
- 0xPARC - The 0xPARC Foundation promotes application-level innovation on Ethereum and other decentralized platforms
- Worldcoin - A Privacy-Preserving Proof-of-Personhood Protocol
- Zama.ai - ZK ML) - FHE tooling for machine learning, blockchain and more. ZK-FHE is an interesting research area. [FHE.org](https://fhe.org/) is a very interesting community with a lot of potential for collaboration.
- Ion Protocol - Lending protocol for staked & restaked assets. They partnered with Modulus to build a risk engine that analyzes validator credit risk. Read more [here](https://twitter.com/ionprotocol/status/1727193815218581726)
- proto-neural-zkp
- zkMnist
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Papers
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Articles and podcasts
- Justin Thaler (2013). "Time-Optimal Interactive Proofs for Circuit Evaluation"
- Pengtao Xie, Misha Bilenko, Tom Finley, Ran Gilad-Bachrach, Kristin Lauter, Michael Naehrig (2014). "Crypto-Nets: Neural Networks over Encrypted Data"
- Nathan Dowlin, Ran Gilad-Bachrach, Kim Laine, Kristin Lauter, Michael Naehrig, John Wernsing (2016). "CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy"
- Zahra Ghodsi, Tianyu Gu, Siddharth Garg (2017). "SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud"
- Payman Mohassel and Yupeng Zhang (2017). "SecureML: A System for Scalable Privacy-Preserving Machine Learning"
- Jian Liu, Mika Juuti, Yao Lu, and N. Asokan (2017). "Oblivious Neural Network Predictions via MiniONN transformations"
- Seunghwa Lee, Hankyung Ko, Jihye Kim, and Hyunok Oh (2020). "vCNN: Verifiable Convolutional Neural Network based on zk-SNARKs"
- Ramy E. Ali, Jinhyun So, A. Salman Avestimehr (2020). "On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks"
- Boyuan Feng, Lianke Qin, Zhenfei Zhang, Yufei Ding, and Shumo Chu (2021). "ZEN: An Optimizing Compiler for Verifiable, Zero-Knowledge Neural Network Inferences"
- Tianyi Liu, Xiang Xie, and Yupeng Zhang (2021). "zkCNN: Zero Knowledge Proofs for Convolutional Neural Network Predictions and Accuracy"
- Chenkai Weng, Kang Yang, Xiang Xie, Jonathan Katz, and Xiao Wang (2021). "Mystique: Efficient Conversions for Zero-Knowledge Proofs with Applications to Machine Learning"
- Jiasi Weng, Jian Weng, Member, IEEE, Gui Tang, Anjia Yang, Ming Li, Jia-Nan Liu (2022). pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing
- Sebastian Angel, Andrew J. Blumberg, Eleftherios Ioannidis, Jess woods (2022). Efficient Representation of Numerical Optimization Problems for SNARKs
- Daniel Kang - Knowledge Proofs](https://arxiv.org/abs/2210.08674)
- Haodi Wang, Thang Hoang (2022). ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline
- Modulus Labs - The Cost of Intelligence: Proving Machine Learning Inference with Zero-Knowledge
- Justin Thaler (2013). "Time-Optimal Interactive Proofs for Circuit Evaluation"
- Pengtao Xie, Misha Bilenko, Tom Finley, Ran Gilad-Bachrach, Kristin Lauter, Michael Naehrig (2014). "Crypto-Nets: Neural Networks over Encrypted Data"
- Zahra Ghodsi, Tianyu Gu, Siddharth Garg (2017). "SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud"
- Payman Mohassel and Yupeng Zhang (2017). "SecureML: A System for Scalable Privacy-Preserving Machine Learning"
- Jian Liu, Mika Juuti, Yao Lu, and N. Asokan (2017). "Oblivious Neural Network Predictions via MiniONN transformations"
- Seunghwa Lee, Hankyung Ko, Jihye Kim, and Hyunok Oh (2020). "vCNN: Verifiable Convolutional Neural Network based on zk-SNARKs"
- Ramy E. Ali, Jinhyun So, A. Salman Avestimehr (2020). "On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks"
- Boyuan Feng, Lianke Qin, Zhenfei Zhang, Yufei Ding, and Shumo Chu (2021). "ZEN: An Optimizing Compiler for Verifiable, Zero-Knowledge Neural Network Inferences"
- Tianyi Liu, Xiang Xie, and Yupeng Zhang (2021). "zkCNN: Zero Knowledge Proofs for Convolutional Neural Network Predictions and Accuracy"
- Chenkai Weng, Kang Yang, Xiang Xie, Jonathan Katz, and Xiao Wang (2021). "Mystique: Efficient Conversions for Zero-Knowledge Proofs with Applications to Machine Learning"
- Jiasi Weng, Jian Weng, Member, IEEE, Gui Tang, Anjia Yang, Ming Li, Jia-Nan Liu (2022). pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing
- Sebastian Angel, Andrew J. Blumberg, Eleftherios Ioannidis, Jess woods (2022). Efficient Representation of Numerical Optimization Problems for SNARKs
- Daniel Kang - Knowledge Proofs](https://arxiv.org/abs/2210.08674)
- Haodi Wang, Thang Hoang (2022). ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline
- Modulus Labs - The Cost of Intelligence: Proving Machine Learning Inference with Zero-Knowledge
- Zahra Ghodsi, Tianyu Gu, Siddharth Garg (2017). "SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud"
- Payman Mohassel and Yupeng Zhang (2017). "SecureML: A System for Scalable Privacy-Preserving Machine Learning"
- Jian Liu, Mika Juuti, Yao Lu, and N. Asokan (2017). "Oblivious Neural Network Predictions via MiniONN transformations"
- Ramy E. Ali, Jinhyun So, A. Salman Avestimehr (2020). "On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks"
- Jiasi Weng, Jian Weng, Member, IEEE, Gui Tang, Anjia Yang, Ming Li, Jia-Nan Liu (2022). pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing
- Daniel Kang - Knowledge Proofs](https://arxiv.org/abs/2210.08674)
- Haodi Wang, Thang Hoang (2022). ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline
- Modulus Labs - The Cost of Intelligence: Proving Machine Learning Inference with Zero-Knowledge
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