A novel privacy-aware model for nonparametric decentralized detection

In recent years, the increasing development of the Internet of Things (IoT) demands the enhancement of both network security and user privacy protection. In the decentralized IoT network, multiple sensors send local observations to a fusion center for data aggregation and authorized hypothesis detection. But at the same time, private information might be inferred illegally, which would cause privacy leakage. In this paper, a novel privacy-aware model named AL-UP is proposed for the nonparametric decentralized detection in the IoT network. It aims to design a local differential privacy and data projection based sanitization mechanism for sensors, to hide the sensitive information in raw observations and protect the data and inference privacy. Based on the adversarial learning framework and linear discriminant analysis, we propose a max-min optimization problem to design parameters of the sanitization mechanism and hypothesis detection rules. The problem is solved via the block coordinate descend method. Numerical results on various public datasets indicate that the proposed model achieves better utility-privacy tradeoff than the state of the arts. (C) 2022 Elsevier Ltd. All rights reserved.