基于云的多传感器估计问题中信息共享方案的隐私性
Privacy of Information Sharing Schemes in a Cloud-based Multi-sensor Estimation Problem
摘要：
在本文中，我们考虑了多传感器估计问题，其中每个传感器收集噪声信息的本地过程中，这是唯一观察到的传感器，和一个共同的过程，这是同时观察到的所有传感器。其目标是评估每个传感器（本地进程）的隐私级别，同时使用云计算技术估计公共进程。传感器的隐私级别被定义为给定与云共享的信息的其本地进程的条件熵。两个信息共享计划被认为是：一个本地计划，和一个全球计划。在本地方案下，每个传感器基于其测量来估计公共过程，并将其估计传输到云。在全局方案下，云接收传感器测量值的总和。它示出，在本地计划中，每个传感器的隐私水平总是高于一定的水平，其特征在于使用香农的互信息。它也被证明，这一结果变得紧密的传感器的数量增加。我们还证明了全局方案是渐近私有的，即，全局方案的隐私损失以O（1/M）的速率减小到零，其中M是传感器的数目。
英文摘要：
In this paper, we consider a multi-sensor estimation problem wherein each sensor collects noisy information about its local process, which is only observed by that sensor, and a common process, which is simultaneously observed by all sensors. The objective is to assess the privacy level of (the local process of) each sensor while the common process is estimated using cloud computing technology. The privacy level of a sensor is defined as the conditional entropy of its local process given the shared information with the cloud. Two information sharing schemes are considered: a local scheme, and a global scheme. Under the local scheme, each sensor estimates the common process based on its the measurement and transmits its estimate to a cloud. Under the global scheme, the cloud receives the sum of sensors' measurements. It is shown that, in the local scheme, the privacy level of each sensor is always above a certain level which is characterized using Shannon's mutual information. It is also proved that this result becomes tight as the number of sensors increases. We also show that the global scheme is asymptotically private, i.e., the privacy loss of the global scheme decreases to zero at the rate of O(1/M) where M is the number of sensors.
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