A mean-field stackelberg game approach for obfuscation adoption in empirical risk minimization
Jeffrey Pawlick, Quanyan Zhu · 2017
Data ecosystems are becoming larger and more complex, while privacy concerns are threatening to erode their potential benefits. Recently, users have developed obfuscation techniques that issue fake search engine queries, undermine location tracking algorithms, or evade government surveillance. These techniques raise one conflict between each user and the machine learning algorithms which track the users, and one conflict between the users themselves. We use game theory to capture the first conflict with a Stackelberg game and the second conflict with a mean field game. Both are combined into a bi-level framework which quantifies accuracy using empirical risk minimization and privacy using differential privacy. We identify necessary and sufficient conditions under which 1) each user is incentivized to obfuscate if other users are obfuscating, 2) the tracking algorithm can avoid this by promising a level of privacy protection, and 3) this promise is incentive-compatible for the tracking algorithm.