LPFHE: Low-Complexity Polynomial CNNs for Secure Inference over FHE

Author:

Wan Junping,Li Danjie,Fang Junbing,Jiang Zoe L.

Publisher

Springer Nature Switzerland

Reference29 articles.

1. Ao, W., Boddeti, V.N.: AutoFHE: automated adaption of CNNs for efficient evaluation over FHE. In: USENIX Security 2024, to be published, pp. 360–384. Springer (2024)

2. Boemer, F., Costache, A., Cammarota, R., Wierzynski, C.: nGraph-HE2: a high-throughput framework for neural network inference on encrypted data. In: Proceedings of the 7th ACM workshop on Encrypted Computing & Applied Homomorphic cryptography, pp. 45–56 (2019)

3. Lecture Notes in Computer Science;J-P Bossuat,2021

4. Brutzkus, A., Gilad-Bachrach, R., Elisha, O.: Low latency privacy preserving inference. In: International Conference on Machine Learning, pp. 812–821. PMLR (2019)

5. Char, D.S., Shah, N.H., Magnus, D.: Implementing machine learning in health care-addressing ethical challenges. N. Engl. J. Med. 378(11), 981–983 (2018)

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