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Data is the new oil; this refrain is repeated extensively in the age ofinternet tracking, machine learning, and data analytics. As data collectionbecomes more personal and pervasive, however, public pressure is mounting forprivacy protection. In this atmosphere, developers have created applications toadd noise to user attributes visible to tracking algorithms. This creates astrategic interaction between trackers and users when incentives to maintainprivacy and improve accuracy are misaligned. In this paper, we conceptualizethis conflict through an N+1-player, augmented Stackelberg game. First amachine learner declares a privacy protection level, and then users respond bychoosing their own perturbation amounts. We use the general frameworks ofdifferential privacy and empirical risk minimization to quantify the utilitycomponents due to privacy and accuracy, respectively. In equilibrium, each userperturbs her data independently, which leads to a high net loss in accuracy. Toremedy this scenario, we show that the learner improves his utility byproactively perturbing the data himself. While other work in this area hasstudied privacy markets and mechanism design for truthful reporting of userinformation, we take a different viewpoint by considering both user and learnerperturbation.

A Stackelberg Game Perspective on the Conflict Between Machine Learning and Data Obfuscation