论文标题
灰色智力网络中的隐私感知的分布式假设测试，并具有附带信息
Privacy-aware Distributed Hypothesis Testing in Gray-Wyner Network with Side Information
论文作者
论文摘要
本文研究了在灰色智力网络中分布式二进制假设检验的问题。观察者可以访问离散的无内存和固定源，并通过一个常见和两个私人通道将其观察到两个检测器。这些通道被认为是无错误但限制的。每个检测器还可以访问其自己的离散无内存和固定源，即侧面信息。目的是对检测器观测值的联合分布进行两个不同的二元假设检验。此外，观察者旨在将相关的潜在源与检测器保持私密。模棱两可用作为潜在来源保留的隐私的度量。通过引入随机套筒输出统计的非反应说明，为总体情况得出了可实现的内部结合。
The problem of distributed binary hypothesis testing in the Gray-Wyner network with side information is studied in this paper. An observer has access to a discrete memoryless and stationary source and describes its observation to two detectors via one common and two private channels. The channels are considered error-free but rate-limited. Each detector also has access to its own discrete memoryless and stationary source, i.e., the side information. The goal is to perform two distinct binary hypothesis testings on the joint distribution of observations at detectors. Additionally, the observer aims to keep a correlated latent source private against the detectors. Equivocation is used as the measure of the privacy preserved for the latent source. An achievable inner bound is derived for the general case by introducing a non-asymptotic account of the output statistics of the random binning.
