A Stackelberg Game Perspective on the Conflict Between Machine Learning and Data Obfuscation
This addresses privacy protection in data-driven applications, offering a strategic solution for balancing accuracy and privacy, though it is incremental by building on existing game theory and differential privacy frameworks.
The paper tackles the conflict between machine learning accuracy and user privacy by modeling it as a Stackelberg game, showing that independent user data perturbation leads to high accuracy loss, but the learner can improve utility by proactively perturbing data.
Data is the new oil; this refrain is repeated extensively in the age of internet tracking, machine learning, and data analytics. As data collection becomes more personal and pervasive, however, public pressure is mounting for privacy protection. In this atmosphere, developers have created applications to add noise to user attributes visible to tracking algorithms. This creates a strategic interaction between trackers and users when incentives to maintain privacy and improve accuracy are misaligned. In this paper, we conceptualize this conflict through an N+1-player, augmented Stackelberg game. First a machine learner declares a privacy protection level, and then users respond by choosing their own perturbation amounts. We use the general frameworks of differential privacy and empirical risk minimization to quantify the utility components due to privacy and accuracy, respectively. In equilibrium, each user perturbs her data independently, which leads to a high net loss in accuracy. To remedy this scenario, we show that the learner improves his utility by proactively perturbing the data himself. While other work in this area has studied privacy markets and mechanism design for truthful reporting of user information, we take a different viewpoint by considering both user and learner perturbation.