Dynamic Privacy Pricing

Abstract

Personal data market provides a promising way to deal with the conflict between exploiting the value of personal data and protecting individuals’ privacy. However, determining the price of privacy is a tough issue. In this chapter, we study the pricing problem in a scenario where a data collector sequentially buys data from multiple data providers whose valuations of privacy are randomly drawn from an unknown distribution. To maximize the total payoff, the collector needs to dynamically adjust the prices offered to the providers. We model the sequential decision-making problem of the collector as a multi-armed bandit problem with each arm representing a candidate price. Specifically, the privacy protection technique adopted by the collector is taken into account. Protecting privacy generally causes a negative effect on the value of data, and this effect is embodied by the time-variant distributions of the rewards associated with arms. Based on the classic upper confidence bound policy, we propose two learning policies for the bandit problem. The first policy estimates the expected reward of a price by counting how many times the price has been accepted by data providers. The second policy treats the time-variant data value as a context and uses ridge regression to estimate the rewards in different contexts. Simulation results on real-world data demonstrate that by applying the proposed policies, the collector can get a payoff approximating to that he can get by setting a fixed price, which is the best in hindsight, for all data providers.