In many cases, machine learning and privacy are perceived to be at odds. Privacy concerns are especially relevant when the involved data are sensitive. This paper deals with the privacy-preserving inference of deep neural networks. We report on first experiments with a new library implementing a variant of the TFHE fully homomorphic encryption scheme. The underlying key technology is the programmable bootstrapping. It enables the homomorphic evaluation of any function of a ciphertext, with a controlled level of noise. Our results indicate for the first time that deep neural networks are now within the reach of fully homomorphic encryption. Importantly, in contrast to prior works, our framework does not necessitate re-training the model.
CITATION STYLE
Chillotti, I., Joye, M., & Paillier, P. (2021). Programmable Bootstrapping Enables Efficient Homomorphic Inference of Deep Neural Networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12716 LNCS, pp. 1–19). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-78086-9_1
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