metadata: {"signature": "0x9d5f37cc8ecbae3b285b766f6e3e96b9c5b7a1cb86ba82997690066654238b2613a9823b525d3dee63fe6fb70054dc14e7e2bc228bc9f4c517dd0426f657f6dd1b", "application": {"round": "0x274554eb289004e15a7679123901b7f070dda0fa", "answers": [{"type": "email", "hidden": true, "question": "Email Address", "questionId": 0, "encryptedAnswer": {"ciphertext": "FYaYyqAX6VhGV1S3x3npaV/fgYX4B9EpPEwJwGe7tiV9hw0bdOZlZIgRF2Qx6m3p", "encryptedSymmetricKey": "cf900aeb6b858fcf4e072db9d87aca95c00372c5f9b7f967153304fd98e83196c803e2325b3e4644148000ce8917c0150f8fa177c6a176710823316cc6bf84900a34c3a081a6b85a44d4599d98980c3c6cb571b315c57e8385a585af07ebc3831b577089ba429cab1999288a9ff6b37d2677362d1e2c44ebd46861d35e47b6380000000000000020fb6e0f67491fc07689bab9601d6163ad684640db8191d8c2770eaf726eb0dca7af43393914b1a1dc0726d18869153d92"}}, {"type": "short-answer", "answer": "Grant from 0xPARC", "hidden": false, "question": "Previous project funding sources (VC, grants, token sales, etc)", "questionId": 1}, {"type": "number", "answer": "250000", "hidden": false, "question": "Total prior funding for project in USD", "questionId": 2}, {"type": "number", "answer": "4", "hidden": false, "question": "Total team size", "questionId": 3}, {"type": "number", "answer": "9", "hidden": false, "question": "How old is the project? (Months)", "questionId": 4}, {"type": "address", "hidden": true, "question": "If you participated in past grant rounds using a different project payout wallet address, please share it here:", "questionId": 5, "encryptedAnswer": {"ciphertext": "aAuYZCA24bis9Us0EVYdSgHIDbBnQ1D8lklw7MG+TgY=", "encryptedSymmetricKey": "02677fa722c9cba76193d572dfc5d2d47930b37e3608b89899320d712d9aea004043391d23909f38fd74f89fb841e71057b663575477076628ced057707975c4bffa9a2c99841e6b28703873e1121931687baeac88b3ce1305da33c44b29a7952136083e0e911d537a8945ff1625873a86d11acb9c69fbb244c9287934653d960000000000000020c92c42869e6ad00481907dff8edcfed1ea7fd6443a89c4df1dea277a13ad98c298758525eaffefecdaff519ef6e71083"}}, {"type": "paragraph", "answer": "https://github.com/alexander-camuto\nhttps://github.com/lancenonce\nhttps://github.com/JSeam2", "hidden": false, "question": "Profiles or socials of other main team members publicly associated with project", "questionId": 6}, {"type": "multiple-choice", "answer": "Yes", "hidden": true, "question": "Have you read and confirmed your grant abides by the Program General Eligibility Policy? https://gitcoin.notion.site/Gitcoin-Grants-Beta-Round-Eligibility-b7fca9fd3c26450b89ded11caaa6497f", "questionId": 7}, {"type": "paragraph", "answer": "EZKL ([https://github.com/zkonduit/ezkl](https://github.com/zkonduit/ezkl)) is a project that helps developers perform verifiable private inference on deep learning models and other computational graphs using zk-SNARKs. The library and command-line tool provide a user-friendly interface for secure and private computation of various ml and related tasks. The project meets the eligibility criteria for Gitcoin Program Beta Round and is seeking funding solely to support outside collaborators and teams building applications on ezkl, to help them move from hackathon-level code to a polished open-source project.\n\n**Mission**\n\nOur mission is to empower developers with the tools they need to easily and efficiently prove and verify the execution of AI models using zero-knowledge proofs. Our aim is not to build any applications ourselves, but stimulate the creation of applications in the community.\n\n**Problem Statement:**\n\nMore human social and economic activity is moving on-chain. We need a way to run AI/ML models as though they ran on chain, which is both scalable and privacy-preserving. \n\n**Solution:**\n\nEZKL addresses the need for easy-to-use tools for performing inference on deep learning models and other computational graphs using zk-SNARKs. With EZKL, developers can:\n\n1. Define a computational graph, such as a neural network or any arbitrary set of operations, using popular frameworks like PyTorch or TensorFlow.\n2. Export the graph of operations as an .onnx file and sample inputs to a .json file.\n3. Use EZKL to generate a ZK-SNARK circuit, enabling them to prove statements such as:\n- \"I ran this publicly available neural network on some private data and it produced this output.\"\n- \"I ran my private neural network on some public data and it produced this output.\"\n- \"I correctly ran this publicly available neural network on some public data and it produced this output.\"\n\nIn addition, EZKL's Rust API is flexible enough to allow developers to code up a computational graph and resulting circuit from scratch. The project uses the Halo2 proof system in the backend for efficient and secure zk-SNARK operations. EZKL compiles to WASM (making integration into browser and node.js servers easier) and can generate EVM bytecode and solidity verifiers for on-chain verification. \n\n**How Does the Project Meet the [Eligibilty Criteria](https://gov.gitcoin.co/t/discussion-feedback-request-gitcoin-program-beta-round-eligibility/13306)?**\n\n1. EZKL directly advances zero-knowledge tools and libraries and actively engages in the ZK community, contributing to its growth and development.\n2. Our project is provided free of charge under a permissive Apache 2.0 license without offering any unique benefits or awards in return for donations.\n3. The EZKL project team is directly affiliated with the project, but all funds received will be given to outside collaborators and project teams building on ezkl (only devs not currently being paid by us).\n4. EZKL focuses on improving the user experience of zero-knowledge tools and libraries, enhancing the developer experience, and enabling technical implementations of zero-knowledge proofs and circuits.\n5. Our project is focused on advancing the ZK ecosystem and contributing to its growth. We do not have our own token or any prior VC funding.\n6. EZKL has been actively and publicly developed and maintained over the last nine months, with consistent activity on GitHub and a bit of social media.\n7. Our project has shown concrete progress in the development of our zero-knowledge tools and libraries, and we have a clear technical roadmap outlining how we plan to advance and use ZK technology in the future.\n8. All our project deliverables, including the [EZKL library](https://github.com/zkonduit/ezkl) and command-line tool, are open-source under the permissive Apache 2.0 license.\n\n**Milestones (targets for funding):**\n\n1. External teams develop a more extensive and complete set of examples showcasing various use cases of EZKL.\n2. External teams launch end-to-end applications to production.\n3. Foster a growing community of users and contributors to drive future developments in the project. \n\n**Internal Milestones (not part of the funding) to support the Milestones:**\n\n1. Improve documentation and tutorials to make it easy for developers to get started with EZKL.\n2. Extend support for additional deep learning models, numerical frameworks, and computational graph formats.\n3. Continue to optimize the performance and efficiency of the [EZKL library](https://github.com/zkonduit/ezkl) and CLI tool.\n\n**Requested Funding:**\n\nWe are seeking funding primarily to support external developers building with EZKL, which includes the following activities:\n\n- Developer, designer, and engineering time to build applications which use ezkl in an essential way.\n- Computational resources needed by these new apps.\n- Possible upstream contributions by these teams.\n\nWe appreciate your support in helping us advance the state of privacy-preserving computation and make zk-SNARKs accessible to a broader audience.\n\n**What success looks like?**\n\n- 10 new projects built by external teams on top of EZKL in the next 6 months\n\n**Team**\n\nOur dedicated team includes experienced professionals in the fields of cryptography, software engineering, and machine learning:\n\n1. Dante Camuto\n2. Lance Davis\n3. JSeam\n4. Jason Morton\n5. Additional team members and community contributors: We have an open and welcoming community of developers and researchers who contribute to the project by providing feedback, reporting issues, and suggesting improvements.\n\nWe're working hard to get more people on board with EZKL and give developers the tools they need to use zk-SNARKs for private computing in machine learning and other areas. We appreciate you taking the time to check out our proposal. Let's team up to make a real difference in the security and privacy of data-driven apps!\n\n**Find us at our Telegram channel:** [https://t.me/+QRzaRvTPIthlYWMx](https://t.me/+QRzaRvTPIthlYWMx)", "hidden": true, "question": "How does your project fit into the ZK Tech round eligibility criteria? ", "questionId": 8}, {"type": "paragraph", "hidden": false, "question": "If you've participated in past grant rounds, please share any new updates or milestones from the prior months", "questionId": 9}, {"type": "paragraph", "answer": "EZKL ([https://github.com/zkonduit/ezkl](https://github.com/zkonduit/ezkl)) is a project that helps developers perform verifiable private inference on deep learning models and other computational graphs using zk-SNARKs. The library and command-line tool provide a user-friendly interface for secure and private computation of various ml and related tasks. The project meets the eligibility criteria for Gitcoin Program Beta Round and is seeking funding solely to support outside collaborators and teams building applications on ezkl, to help them move from hackathon-level code to a polished open-source project.\n\n**Mission**\n\nOur mission is to empower developers with the tools they need to easily and efficiently prove and verify the execution of AI models using zero-knowledge proofs. Our aim is not to build any applications ourselves, but stimulate the creation of applications in the community.\n\n**Problem Statement:**\n\nMore human social and economic activity is moving on-chain. We need a way to run AI/ML models as though they ran on chain, which is both scalable and privacy-preserving. \n\n**Solution:**\n\nEZKL addresses the need for easy-to-use tools for performing inference on deep learning models and other computational graphs using zk-SNARKs. With EZKL, developers can:\n\n1. Define a computational graph, such as a neural network or any arbitrary set of operations, using popular frameworks like PyTorch or TensorFlow.\n2. Export the graph of operations as an .onnx file and sample inputs to a .json file.\n3. Use EZKL to generate a ZK-SNARK circuit, enabling them to prove statements such as:\n- \"I ran this publicly available neural network on some private data and it produced this output.\"\n- \"I ran my private neural network on some public data and it produced this output.\"\n- \"I correctly ran this publicly available neural network on some public data and it produced this output.\"\n\nIn addition, EZKL's Rust API is flexible enough to allow developers to code up a computational graph and resulting circuit from scratch. The project uses the Halo2 proof system in the backend for efficient and secure zk-SNARK operations. EZKL compiles to WASM (making integration into browser and node.js servers easier) and can generate EVM bytecode and solidity verifiers for on-chain verification. \n\n**How Does the Project Meet the [Eligibilty Criteria](https://gov.gitcoin.co/t/discussion-feedback-request-gitcoin-program-beta-round-eligibility/13306)?**\n\n1. EZKL directly advances zero-knowledge tools and libraries and actively engages in the ZK community, contributing to its growth and development.\n2. Our project is provided free of charge under a permissive Apache 2.0 license without offering any unique benefits or awards in return for donations.\n3. The EZKL project team is directly affiliated with the project, but all funds received will be given to outside collaborators and project teams building on ezkl (only devs not currently being paid by us).\n4. EZKL focuses on improving the user experience of zero-knowledge tools and libraries, enhancing the developer experience, and enabling technical implementations of zero-knowledge proofs and circuits.\n5. Our project is focused on advancing the ZK ecosystem and contributing to its growth. We do not have our own token or any prior VC funding.\n6. EZKL has been actively and publicly developed and maintained over the last nine months, with consistent activity on GitHub and a bit of social media.\n7. Our project has shown concrete progress in the development of our zero-knowledge tools and libraries, and we have a clear technical roadmap outlining how we plan to advance and use ZK technology in the future.\n8. All our project deliverables, including the [EZKL library](https://github.com/zkonduit/ezkl) and command-line tool, are open-source under the permissive Apache 2.0 license.\n\n**Milestones (targets for funding):**\n\n1. External teams develop a more extensive and complete set of examples showcasing various use cases of EZKL.\n2. External teams launch end-to-end applications to production.\n3. Foster a growing community of users and contributors to drive future developments in the project. \n\n**Internal Milestones (not part of the funding) to support the Milestones:**\n\n1. Improve documentation and tutorials to make it easy for developers to get started with EZKL.\n2. Extend support for additional deep learning models, numerical frameworks, and computational graph formats.\n3. Continue to optimize the performance and efficiency of the [EZKL library](https://github.com/zkonduit/ezkl) and CLI tool.\n\n**Requested Funding:**\n\nWe are seeking funding primarily to support external developers building with EZKL, which includes the following activities:\n\n- Developer, designer, and engineering time to build applications which use ezkl in an essential way.\n- Computational resources needed by these new apps.\n- Possible upstream contributions by these teams.\n\nWe appreciate your support in helping us advance the state of privacy-preserving computation and make zk-SNARKs accessible to a broader audience.\n\n**What success looks like?**\n\n- 10 new projects built by external teams on top of EZKL in the next 6 months\n\n**Team**\n\nOur dedicated team includes experienced professionals in the fields of cryptography, software engineering, and machine learning:\n\n1. Dante Camuto\n2. Lance Davis\n3. JSeam\n4. Jason Morton\n5. Additional team members and community contributors: We have an open and welcoming community of developers and researchers who contribute to the project by providing feedback, reporting issues, and suggesting improvements.\n\nWe're working hard to get more people on board with EZKL and give developers the tools they need to use zk-SNARKs for private computing in machine learning and other areas. We appreciate you taking the time to check out our proposal. Let's team up to make a real difference in the security and privacy of data-driven apps!\n\n**Find us at our Telegram channel:** [https://t.me/+QRzaRvTPIthlYWMx](https://t.me/+QRzaRvTPIthlYWMx)", "hidden": false, "question": "Anything else you'd like to share about your project, previous work, or other project affiliations? Anything you'd like to add that has not been covered?", "questionId": 10}, {"type": "multiple-choice", "hidden": true, "question": "Does 50% of your team reflect the diversity of the community your project serves with a particular emphasis on including women, Black, Indigenous, and People of Color (BIPoC), individuals with disabilities, and members of the LGBTQ+ community?", "questionId": 11, "encryptedAnswer": {"ciphertext": "57HqDIgLhGJ86si/z0nbHsxIoLLGuxtKqqAVcOfQmjI=", "encryptedSymmetricKey": "2a710236bccc096d335ae972a9a9346223b2257ff1147f75e5796e383d200ff19b1aafb4f33e3233572cd5479f8b6712395db4fb8118b0cbb9117e23b21f4df00102c7374e9c778ed066f537e3b5b41abbb22c5d192e513dc0f5154e14d2168d6c3799448eb0ed4a63ed8c423712279090b046547b2a0288596ed3ff8d8716600000000000000020fe63e1866d0071ec6ed95047c2ae9ba1765fae3855d1aa16ccc32822f9475a743aca01f4c58cbcfbbcf8a0a5c006c7b4"}}], "project": {"id": "1:0x03506eD3f57892C85DB20C36846e9c808aFe9ef4:571", "title": "EZKL", "logoImg": "bafkreigrwlgshkn4utymgvzu5jxwz5ifcyh25oz43kvvsjirnrsklwbtle", "metaPtr": {"pointer": "bafkreifxg2wpoaoqdsfqoz6tyw37f6qqy5jdzvch6x55rkwrmk345y7bxe", "protocol": "1"}, "website": "https://github.com/zkonduit/ezkl", "createdAt": 1681939310343, "userGithub": "jasonmorton", "credentials": {"github": {"type": ["VerifiableCredential"], "proof": {"jws": "eyJhbGciOiJFZERTQSIsImNyaXQiOlsiYjY0Il0sImI2NCI6ZmFsc2V9..O74OkNrW9vB5uRYNm_w-ovumXJUAWBNsULF_yhjaZHzOVgbwknv-MbeZxqpO2Xu6H0GeXCCTu0x_JpcqvFwxDg", "type": "Ed25519Signature2018", "created": "2023-04-19T21:28:36.917Z", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC#z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC"}, "issuer": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC", "@context": ["https://www.w3.org/2018/credentials/v1"], "issuanceDate": "2023-04-19T21:28:36.917Z", "expirationDate": "2023-07-18T21:28:36.917Z", "credentialSubject": {"id": "did:pkh:eip155:1:0x535baE5D2b5C41D186a9A9983904BCFab4732836", "hash": "v0.0.0:f8APNd2hxvnJm/ZO58+vqMtixINW2lDT5+ihP3xSsBo=", "@context": [{"hash": "https://schema.org/Text", "provider": "https://schema.org/Text"}], "provider": "ClearTextGithubOrg#zkonduit#163558"}}}, "description": "ezkl is a library and command-line tool for doing inference for deep learning models and other computational graphs in a zk-snark. It enables the following workflow:\n- Define a computational graph, for instance a neural network (but really any arbitrary set of operations), as you would normally in pytorch or tensorflow.\n- Export the final graph of operations as an .onnx file and some sample inputs to a .json file.\n- Point ezkl to the .onnx and .json files to generate a ZK-SNARK circuit with which you can prove statements such as:\n    \"I ran this publicly available neural network on some private data and it produced this output\"\n    \"I ran my private neural network on some public data and it produced this output\"\n    \"I correctly ran this publicly available neural network on some public data and it produced this output\"\n\nThe rust API is also sufficiently flexible to enable you to code up a computational graph and resulting circuit from scratch. For examples on how to do so see the repo.\n\nIn the backend we use Halo2 as a proof system.", "lastUpdated": 0, "projectGithub": "zkonduit", "projectTwitter": "zkonduit"}, "recipient": "0x535baE5D2b5C41D186a9A9983904BCFab4732836"}}