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arXiv 2006.07063eess.SYcs.SY
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信息物理系统中对抗对抗分类的隐私保护

Privacy Against Adversarial Classification in Cyber-Physical Systems

Carlos Murguia, Paulo Tabuada

摘要:

对于一类网络物理系统(CPS),我们解决了在云上执行计算而不泄露系统结构和操作的私有信息的问题。我们将CPS建模为输入-输出动力系统(系统运行模式)的集合。根据系统运行的模式,输出轨迹由这些系统中的一个响应于驾驶输入而生成。输出测量和驱动输入被发送到云端进行处理。我们通过一些函数(输入-输出轨迹的函数)来捕获这种“处理”,我们需要云来精确计算-这里称为轨迹效用。但是,出于隐私原因,我们希望保持模式私密,即,我们不希望云正确地识别CPS的什么模式产生给定轨迹。为此,我们在传输之前扭曲轨迹,并将损坏的数据发送到云端。我们提供数学工具(基于输出调节技术)来正确设计扭曲机制,以便:1)原始和扭曲的轨迹导致相同的效用;扭曲的数据导致云错误分类模式。

英文摘要:

For a class of Cyber-Physical Systems (CPSs), we address the problem of performing computations over the cloud without revealing private information about the structure and operation of the system. We model CPSs as a collection of input-output dynamical systems (the system operation modes). Depending on the mode the system is operating on, the output trajectory is generated by one of these systems in response to driving inputs. Output measurements and driving inputs are sent to the cloud for processing purposes. We capture this "processing" through some function (of the input-output trajectory) that we require the cloud to compute accurately - referred here as the trajectory utility. However, for privacy reasons, we would like to keep the mode private, i.e., we do not want the cloud to correctly identify what mode of the CPS produced a given trajectory. To this end, we distort trajectories before transmission and send the corrupted data to the cloud. We provide mathematical tools (based on output-regulation techniques) to properly design distorting mechanisms so that: 1) the original and distorted trajectories lead to the same utility; and the distorted data leads the cloud to misclassify the mode.

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