论文标题
在网络物理系统中针对对抗分类的隐私
Privacy Against Adversarial Classification in Cyber-Physical Systems
论文作者
论文摘要
对于一类网络物理系统（CPSS），我们解决了通过云执行计算的问题，而无需揭示有关系统结构和操作的私人信息。我们将CPSS建模为输入输出动态系统（系统操作模式）的集合。根据系统正在运行的模式，输出轨迹是由这些系统之一生成的，以响应驱动输入而生成。输出测量和驾驶输入被发送到云以进行处理。我们通过（输入输出轨迹的）函数捕获了这种“处理”，我们要求云才能准确计算 - 此处称为轨迹实用程序。但是，出于隐私原因，我们希望将模式保密，即，我们不希望云正确地确定CPS的哪种模式产生了给定的轨迹。为此，我们在传输之前会扭曲轨迹，然后将损坏的数据发送到云。我们提供数学工具（基于输出调节技术），以正确设计扭曲机制，以便：1）原始轨迹和扭曲的轨迹导致相同的实用性；扭曲的数据导致云错误地分类该模式。
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.
