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Data ecosystems are becoming larger and more complex due to online tracking,wearable computing, and the Internet of Things. But privacy concerns arethreatening to erode the potential benefits of these systems. Recently, usershave developed obfuscation techniques that issue fake search engine queries,undermine location tracking algorithms, or evade government surveillance.Interestingly, these techniques raise two conflicts: one between each user andthe machine learning algorithms which track the users, and one between theusers themselves. In this paper, we use game theory to capture the firstconflict with a Stackelberg game and the second conflict with a mean fieldgame. We combine both into a dynamic and strategic bi-level framework whichquantifies accuracy using empirical risk minimization and privacy usingdifferential privacy. In equilibrium, we identify necessary and sufficientconditions under which 1) each user is incentivized to obfuscate if other usersare obfuscating, 2) the tracking algorithm can avoid this by promising a levelof privacy protection, and 3) this promise is incentive-compatible for thetracking algorithm.

A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimization