Privacy-Aware Distributed Bayesian Detection
Privacy-Aware Distributed Bayesian Detection
复制标题
隐私感知分布式贝叶斯检测
DOI：
10.1109/jstsp.2015.2429123
复制
发表时间：
2015
影响因子：
7.5
通讯作者：
T. Oechtering
中科院分区：
文献类型：
--
作者：
Zuxing Li;T. Oechtering
我们从贝叶斯检测的角度研究了隐私敏感假设下的远程分布式感知中的窃听问题。我们考虑一个并行分布式检测网络，远程决策者独立地作出本地决定，有限域上定义的，并将它们转发到融合中心，使最终的决定。假设窃听者拦截一组特定的本地决策，也对假设进行猜测。我们提出了一种新的贝叶斯检测操作隐私度量的窃听者的最小可实现的贝叶斯风险。此外，我们介绍了两个隐私感知的分布式贝叶斯检测公式，即隐私约束的分布式贝叶斯检测问题和隐私相关的分布式贝叶斯检测问题的检测性能优化的隐私保证约束下，并分别最小化的加权和目标的检测性能和隐私风险。对于最优隐私感知分布式贝叶斯检测设计，识别采用确定性似然检验或其随机化策略的最优决策策略。此外，它表明，不同配方的等价问题总是存在，并导致相同的最佳隐私感知分布式贝叶斯检测设计。最后通过数值算例对结果进行了说明和讨论。隐私感知的分布式贝叶斯检测设计思想为实现未来可信物联网应用提供了一种新的解决方案。
We study the eavesdropping problem in the remotely distributed sensing of a privacy-sensible hypothesis from the Bayesian detection perspective. We consider a parallel distributed detection network where remote decision makers independently make local decisions defined on finite domains and forward them to the fusion center which makes the final decision. An eavesdropper is assumed to intercept a specific set of local decisions to make also a guess on the hypothesis. We propose a novel Bayesian detection-operational privacy metric given by the minimal achievable Bayesian risk of the eavesdropper. Further, we introduce two privacy-aware distributed Bayesian detection formulations, namely the privacy-constrained distributed Bayesian detection problem and the privacy-concerned distributed Bayesian detection problem where the detection performance is optimized under a privacy guarantee constraint and a weighted sum objective of the detection performance and privacy risk is minimized respectively. For an optimal privacy-aware distributed Bayesian detection design, the optimal decision strategy of employing a deterministic likelihood test or a randomized strategy thereof is identified. Further, it is shown that equivalent problems of different formulations always exist and lead to the same optimal privacy-aware distributed Bayesian detection design. The results are illustrated and discussed by numerical examples. The idea of privacy-aware distributed Bayesian detection design provides a novel solution to realize future trustworthy Internet of Things applications.