TPMDP: Threshold Personalized Multi-party Differential Privacy via Optimal Gaussian Mechanism. (arXiv:2305.11192v1 [cs.CR])

In modern distributed computing applications, such as federated learning and
AIoT systems, protecting privacy is crucial to prevent misbehaving parties from
colluding to steal others' private information. However, guaranteeing the
utility of computation outcomes while protecting all parties' privacy can be
challenging, particularly when the parties' privacy requirements are highly
heterogeneous. In this paper, we propose a novel privacy framework for
multi-party computation called Threshold Personalized Multi-party Differential
Privacy (TPMDP), which addresses a limited number of semi-honest colluding
adversaries. Our …

applications computation computing differential privacy distributed distributed computing federated learning information outcomes party privacy private protecting requirements steal systems utility