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Hi everyone, we have developed a library that applies numpy functions over encrypted data (using homomorphic encryption). The repo is available in open source at https://github.com/zama-ai/concrete-numpy
Hey now I can't say anything about concrete, but from my experience with MS-SEAL it is an order of magnitude slower on cyphertexts than the straight addition or multiplication on the plaintext message. You can try it out using one of my own libraries in a jupyter notebook here: https://gitlab.com/deepcypher/python-fhez/ in the examples directory, on Fashion-MNIST (for how see: https://python-fhez.readthedocs.io/en/latest/examples.html) . I am working on a sister project using go and lattigo to see if I can improve performance as the real problem is not only is FHE slower but wrapping it in a language like python with numpy custom containers (which is also what concrete does AFAIK) adds probably even more time and lots of space too!
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