As multi-agent systems proliferate and share more user data, new approaches are needed to protect sensitive data while still enabling system operation. To address this need, this paper presents a private multi-agent LQ control framework. Agents' state trajectories can be sensitive and we therefore protect them using differential privacy. We quantify the impact of privacy along three dimensions: the amount of information shared under privacy, the control-theoretic cost of privacy, and the tradeoffs between privacy and performance. These analyses are done in conventional control-theoretic terms, which we use to develop guidelines for calibrating privacy as a function of system parameters. Numerical results indicate that system performance remains within desirable ranges, even under strict privacy requirements.
翻译:随着多试剂系统扩散和分享更多的用户数据,需要采用新的方法保护敏感数据,同时仍能使系统运作。为了满足这一需要,本文件提出了一个私人多剂LQ控制框架。代理人的国家轨迹可能是敏感的,因此我们使用不同的隐私来保护它们。我们从三个方面量化隐私的影响:在隐私下共享的信息数量、隐私的控制理论成本以及隐私与性能之间的权衡。这些分析是以常规控制理论术语进行的,我们用这些术语来制定校准隐私的准则,以此作为系统参数的函数。数字结果表明,即使严格隐私要求,系统运行情况仍然处于适当范围之内。