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.

中文翻译：

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分散式检测与强大的信息隐私保护

我们考虑一个去中心化的检测网络，其目的是推断一个感兴趣的公共假设。但是，原始传感器的观测结果也使融合中心可以推断出我们希望保护的私人假设。我们考虑属于不确定性集合的私人假设的数量不计其数的情况，并在每个传感器上开发局部隐私映射，以便经过消毒的传感器信息将在融合中心检测公共假设的Bayes错误最小化，同时实现信息隐私对于所有私人假设。我们介绍了最有利假设（MFH）的概念，并展示了如何在私人假设集中找到MFH。通过保护MFH的信息隐私，还可以实现其他所有私人假设的信息隐私。我们提供一种迭代算法，以找到最佳的本地隐私映射，并推导这些隐私映射的一些理论属性。仿真结果表明，我们提出的方法允许融合中心以较低的误差推断公共假设，同时保护所有私人假设的信息隐私。