有向信息作为云控制中的隐私措施
Directed Information as Privacy Measure in Cloud-based Control
摘要：
我们考虑基于云的控制场景，其中具有本地控制任务的客户端将其计算或物理职责外包给云服务提供商。为了解决隐私问题，在这样的控制架构，我们首先调查的问题，找到一个适当的隐私措施，为客户端谁希望保持本地状态信息尽可能私人的控制操作过程中。具体来说，我们证明使用克莱默的概念因果条件定向信息作为一个公理的论点的基础上的隐私损失的措施。然后，我们提出了一种方法来设计一个最佳的“隐私过滤器”，最大限度地减少隐私损失，同时保证一定水平的控制性能。我们特别表明，基于云的线性二次高斯（LQG）控制的最佳隐私过滤器可以通过线性矩阵不等式（LMI）算法合成。通过数值例子说明了设计中的权衡。
英文摘要：
We consider cloud-based control scenarios in which clients with local control tasks outsource their computational or physical duties to a cloud service provider. In order to address privacy concerns in such a control architecture, we first investigate the issue of finding an appropriate privacy measure for clients who desire to keep local state information as private as possible during the control operation. Specifically, we justify the use of Kramer's notion of causally conditioned directed information as a measure of privacy loss based on an axiomatic argument. Then we propose a methodology to design an optimal "privacy filter" that minimizes privacy loss while a given level of control performance is guaranteed. We show in particular that the optimal privacy filter for cloud-based Linear-Quadratic-Gaussian (LQG) control can be synthesized by a Linear-Matrix-Inequality (LMI) algorithm. The trade-off in the design is illustrated by a numerical example.
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