We consider a decentralized detection network whose aim is to infer a public hypothesis of interest. However, the raw sensor observations also allow the fusion center to infer private hypotheses that we wish to protect. We consider the case where there are an uncountable number of private hypotheses belonging to an uncertainty set, and develop local privacy mappings at every sensor so that the sanitized sensor information minimizes the Bayes error of detecting the public hypothesis at the fusion center while achieving information privacy for all private hypotheses. We introduce the concept of a most favorable hypothesis (MFH) and show how to find an MFH in the set of private hypotheses. By protecting the information privacy of the MFH, information privacy for every other private hypothesis is also achieved. We provide an iterative algorithm to find the optimal local privacy mappings, and derive some theoretical properties of these privacy mappings. The simulation results demonstrate that our proposed approach allows the fusion center to infer the public hypothesis with low error while protecting information privacy of all the private hypotheses.

In a decentralized Internet of Things (IoT) network, a fusion center receives information from multiple sensors to infer a public hypothesis of interest. To prevent the fusion center from abusing the sensor information, each sensor sanitizes its local observation using a local privacy mapping, which is designed to achieve both inference privacy of a private hypothesis and data privacy of the sensor raw observations. Various inference and data privacy metrics have been proposed in the literature. We introduce the concept of privacy implication (with vanishing budget) to study the relationships between these privacy metrics. We propose an optimization framework in which both local differential privacy (data privacy) and information privacy (inference privacy) metrics are incorporated. In the parametric case where sensor observations' distributions are known a priori, we propose a two-stage local privacy mapping at each sensor, and show that such an architecture is able to achieve information privacy and local differential privacy to within the predefined budgets. For the nonparametric case where sensor distributions are unknown, we adopt an empirical optimization approach. Simulation and experiment results demonstrate that our proposed approaches allow the fusion center to accurately infer the public hypothesis while protecting both inference and data privacy.

In Internet of Things, with large amounts of sensor data gathered in fusion center, it is important to detect a public hypothesis, but at the same time it is crucial to prevent a private hypothesis being detected. In order to achieve this goal, a multilayer nonlinear processing procedure is proposed to distort the sensor's data before it is sent to the fusion center. In particular, each sensor applies linear and nonlinear distortions to balance the public hypothesis test and the privacy distortion. Mirror descent methodology is reformulated to optimize the distortion matrices so as to ensure that the regularized empirical risk of detecting the private hypothesis is above a given privacy threshold, while minimizing the regularized empirical risk of detecting the public hypothesis. Experiments on empirical datasets demonstrate that the proposed approach achieves a good tradeoff between the error rates of the public and private hypotheses.

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.