Arbitrarily Strong Utility-Privacy Tradeoff in Multi-Agent Systems

Each agent in a network makes a local observation that is linearly related to a set of public and private parameters. The agents send their observations to a fusion center to allow it to estimate the public parameters. To prevent leakage of the private parameters, each agent first sanitizes its loca...

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Veröffentlicht in:	IEEE transactions on information forensics and security 2021, Vol.16, p.671-684
Hauptverfasser:	Wang, Chong Xiao, Song, Yang, Tay, Wee Peng
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Cramér-Rao lower bound Data privacy Estimation Inference privacy linear estimation Lower bounds multi-agent network Multi-agent systems Multiagent systems Noise measurement Parameter estimation Perturbation Perturbation methods Privacy Tradeoffs
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