On Privacy of Quantized Sensor Measurements through Additive Noise
For sensor network designers, it provides a method to enhance privacy against adversaries with access to quantized data, but results are incremental and lack concrete performance numbers.
This paper addresses privacy in quantized sensor measurements by adding random noise to minimize mutual information between the sum and the quantized measurements under a distortion constraint. Simulations demonstrate the trade-off between privacy and distortion.
We study the problem of maximizing privacy of quantized sensor measurements by adding random variables. In particular, we consider the setting where information about the state of a process is obtained using noisy sensor measurements. This information is quantized and sent to a remote station through an unsecured communication network. It is desired to keep the state of the process private; however, because the network is not secure, adversaries might have access to sensor information, which could be used to estimate the process state. To avoid an accurate state estimation, we add random numbers to the quantized sensor measurements and send the sum to the remote station instead. The distribution of these random variables is designed to minimize the mutual information between the sum and the quantized sensor measurements for a desired level of distortion -- how different the sum and the quantized sensor measurements are allowed to be. Simulations are presented to illustrate our results.