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pro vyhledávání: '"Reshef, Roie"'
Autor:
Reshef, Roie, Levy, Kfir Y.
This paper addresses the challenge of preserving privacy in Federated Learning (FL) within centralized systems, focusing on both trusted and untrusted server scenarios. We analyze this setting within the Stochastic Convex Optimization (SCO) framework
Externí odkaz:
http://arxiv.org/abs/2407.12396
Neural networks are susceptible to privacy attacks. To date, no verifier can reason about the privacy of individuals participating in the training set. We propose a new privacy property, called local differential classification privacy (LDCP), extend
Externí odkaz:
http://arxiv.org/abs/2310.20299
The two-volume set LNCS 14499 and 14500 constitutes the proceedings of the 25th International Conference on Verification, Model Checking, and Abstract Interpretation, VMCAI 2024, which took place in London, Ontario, Canada, in January 2024. The 30 f