论文标题
多代理系统中任意强大的公用事业私人关系权衡
Arbitrarily Strong Utility-Privacy Tradeoff in Multi-Agent Systems
论文作者
论文摘要
网络中的每个代理都会进行本地观察，该观察与一组公共和私人参数线性相关。代理商将其观察结果发送到融合中心，以允许其估计公共参数。为了防止私人参数的泄漏，每个代理首先使用本地隐私机制对其本地观察进行消毒，然后再将其传输到融合中心。我们根据cramér-rao的较低界限调查了公用事业私人的权衡，以估计公共和私人参数。我们研究了线性压缩和噪声扰动给出的隐私机制类别，并为在多代理系统中分别提供了与先前信息可用且无法使用的情况下，在多代理系统中实现任意强大的公用事业私人关系权衡的必要条件。我们还提供了一种方法，可以在不损害公用事业的情况下找到最大的估计隐私性，并提出一种交替的算法，以优化公用事业 - 私人关系权衡，而在任意强大的公用事业私人空间交易是无法实现的情况下。
Each agent in a network makes a local observation that is linearly related to a set of public and private parameters. The agents send their observations to a fusion center to allow it to estimate the public parameters. To prevent leakage of the private parameters, each agent first sanitizes its local observation using a local privacy mechanism before transmitting it to the fusion center. We investigate the utility-privacy tradeoff in terms of the Cramér-Rao lower bounds for estimating the public and private parameters. We study the class of privacy mechanisms given by linear compression and noise perturbation, and derive necessary and sufficient conditions for achieving arbitrarily strong utility-privacy tradeoff in a multi-agent system for both the cases where prior information is available and unavailable, respectively. We also provide a method to find the maximum estimation privacy achievable without compromising the utility and propose an alternating algorithm to optimize the utility-privacy tradeoff in the case where arbitrarily strong utility-privacy tradeoff is not achievable.
