The Economics of Privacy and Utility: Investment Strategies

The inevitable leakage of privacy as a result of unrestrained disclosure of personal information has motivated extensive research on robust privacy-preserving mechanisms. However, existing research is mostly limited to solving the problem in a static setting with disregard for the privacy leakage over time. Unfortunately, this treatment of privacy is insufficient in practical settings where users continuously disclose their personal information over time resulting in an accumulated leakage of the users’ sensitive information. In this paper, we consider privacy leakage over a finite time horizon and investigate optimal strategies to maximize the utility of the disclosed data while limiting the finite-horizon privacy leakage. We consider a simple privacy mechanism that involves compressing the user’s data before each disclosure to meet the desired constraint on future privacy. We further motivate several algorithms to optimize the dynamic privacy-utility tradeoff and evaluate their performance via extensive synthetic performance tests.

I. Introduction

The unprecedented growth of big-data applications suggests that there is a growing competition in the technological world to collect and harness tremendous amounts of user information. Tech companies and other online service providers are always seeking to enhance the quality of their products and services by collecting massive amounts of information from their user base. Users often disclose their personal information either by directly engaging with the service providers, such as in the case of social media and online shopping, or indirectly, and often inadvertently, by simply possessing IoT and other smart devices, such as location trackers.