The Economics of Privacy and Utility: Investment Strategies

TL;DR

This paper studies how users can optimize data disclosures over time to maximize utility while controlling cumulative privacy leakage, introducing dynamic strategies for privacy-utility tradeoffs.

Contribution

It presents a novel dynamic framework for privacy-utility tradeoff over finite horizons, with algorithms for optimizing data compression strategies.

Findings

Proposes a finite-horizon privacy leakage model.

Develops algorithms for optimal data compression strategies.

Evaluates performance through extensive synthetic tests.

Abstract

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.