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 Cram\'er-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效用-隐私权衡

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有参数线性相关。代理将他们的观察结果发送到融合中心，使其能够估计公共参数。为了防止私有参数泄漏，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。我们根据用于估计公共和私人参数的 Cram\'er-Rao 下界来研究效用 - 隐私权衡。我们研究了由线性压缩和噪声扰动给出的隐私机制类别，并分别在先验信息可用和不可用的情况下，推导出在多代理系统中实现任意强效用-隐私权衡的充分必要条件。