SFPDML: Securer and Faster Privacy-Preserving Distributed Machine Learning based on MKTFHE

arXiv:2211.09353v2 Announce Type: replace
Abstract: In recent years, distributed machine learning has garnered significant attention. However, privacy continues to be an unresolved issue within this field. Multi-key homomorphic encryption over torus (MKTFHE) is one of the promising candidates for addressing this concern. Nevertheless, there may be security risks in the decryption of MKTFHE. Moreover, to our best known, the latest works about MKTFHE only support Boolean operation and linear operation which cannot directly compute the non-linear function like Sigmoid. Therefore, …

arxiv attention candidates cs.cr cs.lg distributed encryption homomorphic encryption issue key machine machine learning may privacy risks security security risks