On the Differential Private Data Market: Endogenous Evolution, Dynamic Pricing, and Incentive Compatibility

Privacy is an essential issue in data trading markets. This work uses a mechanism design approach to study the optimal market model to economize the value of privacy of personal data, using differential privacy. The buyer uses a finite number of randomized algorithms to get access to the owners’ data in a sequential-composition manner, in which each randomized algorithm is differentially private. Each usage of a randomized algorithm is referred to as a period. Motivated by the discovery of an individual’s dual motives for privacy protection, we partition each data owner’s preference over privacy protection into the intrinsic and the instrumental components, in which the instrumental preference arises endogenously from the data buyer’s sequential usages of multiple private algorithms. Due to the composability of differential privacy, there are inevitable privacy losses accumulated over periods. Hence, we allow the owners to leave the market at the end of any period by making stopping decisions. We define an instrumental kernel function to capture the instrumentalness of owners’ preferences and model the formation of each owner’s (both intrinsic and instrumental) preference over periods by taking into consideration of the composability of differential privacy and time-varying nature of privacy concerns. Our desideratum is to study the buyer’s design regime of optimal market models in dynamic environment when each owner makes coupled decisions of stopping and reporting of their preferences. The buyer seeks to design a privacy allocation rule that dynamically specifies the degree of privacy protections and a payment rule to compensate the privacy losses of the owners. The buyer additionally chooses a payment rule which is independent of owners’ report of their preferences to influence the owners’ stopping decisions. We characterize the dynamic incentive compatibility and provide a design principle to construct the payment rules in terms of the privacy allocation rule. Further, we relax the buyer’s market design problem and provide a sufficient condition for an approximated dynamic incentive compatible market model.