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.

中文翻译：

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多代理系统中任意强的实用程序与隐私权衡


网络中的每个代理都会进行与一组公共和私有参数线性相关的本地观察。代理将他们的观察结果发送到融合中心，以使其能够估计公共参数。为了防止私人参数泄露，每个智能体首先使用本地隐私机制对其本地观察结果进行清理，然后再将其传输到融合中心。我们根据估计公共和私人参数的 Cramér-Rao 下界来研究公用事业与隐私的权衡。我们研究了线性压缩和噪声扰动给出的一类隐私机制，并分别在先验信息可用和不可用的情况下推导了在多智能体系统中实现任意强效用-隐私权衡的必要和充分条件。我们还提供了一种在不损害效用的情况下找到可实现的最大估计隐私的方法，并提出了一种替代算法，以在无法实现任意强的效用-隐私权衡的情况下优化效用-隐私权衡。