46 lines
1.8 KiB
Markdown
46 lines
1.8 KiB
Markdown
## zk proofs applied to ml (zkml)
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<br>
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### tl; dr
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<br>
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<p align="center">
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<img width="500" src="https://user-images.githubusercontent.com/1130416/234938321-a0b052b6-e754-4e80-8351-0daa847ebd12.png">
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</p>
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<br>
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* **challenges:**
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* transpile NNs into ZKP circuts (floating-point weigths -> fixed-point arithmetic)
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* model size/depth
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* **ideas:**
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* model authenticity
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- assurance that the ml model is the one that run (e.g. the most accurate one)
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- functional commitments allow the prover to establosj that it used a commited model (but no guarantess about the commited model).
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* model integrity
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- assurance that the same ml algorithm is ran on different data the same way
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* attestations
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- integrate attestations from external parties
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* decentralized inference or traning
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- perform ml training in a decentralized way
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* proof of personhood
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<br>
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---
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### cool resources
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<br>
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* **[humanness in the age of ai, by worldcoin](https://worldcoin.org/blog/engineering/humanness-in-the-age-of-ai)**
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* **[zk-img: attested images via zk-proofs, d. kang et al.](https://arxiv.org/pdf/2211.04775.pdf)**
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* **[checks and balances ml and zk, by a16](https://a16zcrypto.com/content/article/checks-and-balances-machine-learning-and-zero-knowledge-proofs/)**
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* **[trustless verification of ml, by d. kang](https://ddkang.github.io/blog/2022/10/18/trustless/)**
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* **[tachikoma, neural nets for zk proof systems](https://github.com/zk-ml/tachikoma)**
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* **[zkml, framework for constructing proofs of ml model in zksnarks](https://github.com/ddkang/zkml)**
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* **[ezkl, deep learning inference in zk-snark](https://github.com/zkonduit/ezkl)**
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* **[unraveling zkml, by dr. cathie so](https://www.canva.com/design/DAFgqqAboU0/4HscC5E3YkFRFk3bB64chw/view#1)**
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