Data privacy and utility trade-off based on mutual information neural estimator

Abstract

In the era of big data and the Internet of Things (IoT), data owners need to share a large amount of data with the intended receivers in an insecure environment, posing a trade-off issue between user privacy and data utility. The privacy utility trade-off was facilitated through a privacy funnel based on mutual information. In this article, we propose a privacy funnel which is using mutual information neural estimator (MINE) to optimize the privacy utility trade-off by estimating mutual information. Firstly, we estimate mutual information in a training way for data with unknown distributions and make the result a measure of privacy and utility. Secondly, we optimize the privacy utility trade-off by optimizing the mutual information added noise as an encoding process and minimizing cross-entropy mutual information between published data and non-sensitive data as a decoding process. Finally, simulations are conducted comparing our methodology to the Kraskov, Stögbauer, and Grassberger (KSG) estimation obtained by k-nearest neighbor as well as information bottleneck in the traditional method. Our results clearly demonstrate that the designed framework has better performance and attains convergence quicker in the scenario where enormous volumes of data are handled, and the largest data utility obtained by the MINE for a given privacy threshold is even better.