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by osaariki
2396 days ago
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This is not how this works. Homomorphic encryption (HE) is best thought of as a software CPU that gives you ways to mutate data (but not read it without the secret key of course). Any computation you run has to be using the instruction set offered by HE: addition, multiplication or rotation. When you do an operation on two ciphertexts, you get a new ciphertext back, but no information on its contents. Now the way you do machine learning here is by translating your model to use the instructions offered by HE. You've effectively recompiled the model to a new architecture. If you'd like to read more about machine learning with homomorphic encryption, we published a paper on our CHET compiler [1]. I also talk about this space on a high level in this MSR podcast episode [2]. [1]: https://www.cs.utexas.edu/~roshan/CHET.pdf
[2]: https://www.microsoft.com/en-us/research/blog/he-compilers-f... |
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