Privacy-preserving nonparametric decentralized detection
Meng Sun, Wee Peng Tay · 2016
We consider the problem of decentralized detection of a hypothesis H using multiple sensors. The sensors also want to keep the fusion center from inferring about another hypothesis G. Each sensor makes an observation and summarizes the observation using a local decision rule. The sensor summaries are communicated to the fusion center to perform an overall decision making. As the underlying joint distribution of the hypotheses and sensor observations is unknown, we aim at finding sensor decision rules that minimize the regularized empirical risk of deciding H at the fusion center, while ensuring that the regularized risk of the fusion center deciding G correctly is more than a given threshold. We propose an optimization approach based on the Gauss-Seidel method, and show that it converges to a critical point.