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cs.LG2023
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning
Yeonsoo Jeon, Mattan Erez, Michael Orshansky
Privacy-Preserving ML (PPML) based on Homomorphic Encryption (HE) is a promising foundational privacy technology. Making it more practical requires lowering its computational cost,…
cs.CR2023
A Provably Secure Strong PUF based on LWE: Construction and Implementation
Xiaodan Xi, Ge Li, Ye Wang +2
We construct a strong PUF with provable security against ML attacks on both classical and quantum computers. The security is guaranteed by the cryptographic hardness of learning de…