Differentially Private LQ Control
Differentially Private LQ Control
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DOI：
10.1109/tac.2022.3148710
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发表时间：
2018-07
影响因子：
6.8
通讯作者：
Kasra Yazdani;Austin M. Jones;Kevin J. Leahy;M. Hale
中科院分区：
文献类型：
--
作者：
Kasra Yazdani;Austin M. Jones;Kevin J. Leahy;M. Hale
随着多代理系统的激增和更多的用户数据，需要新的方法来保护敏感数据，同时仍然支持系统操作。为了满足这一需求，本文提出了一个私人多智能体LQ控制框架。代理的状态轨迹可能是敏感的，因此我们使用差分隐私来保护它们。我们沿着沿着三个维度量化隐私的影响：隐私下共享的信息量，隐私的控制理论成本，以及隐私和性能之间的权衡。这些分析是在传统的控制理论方面进行的，我们用它来制定准则，用于校准隐私作为系统参数的函数。数值结果表明，系统性能保持在理想的范围内，即使在严格的隐私要求。
As multi-agent systems proliferate and more user data, new approaches are needed to protect sensitive data while still enabling system operation. To address this need, this article presents a private multiagent 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.