Towards Privacy-Preserving and Practical Data Trading for Aggregate Statistic

Data trading is an effective way for commercial companies to obtain massive personal data to develop their data-driven businesses. However, when data owners may want to sell their data without revealing privacy, data consumers also face the dilemma of high purchase costs due to purchasing too much invalid data. Therefore, there is an urgent need for a data trading scheme that can protect personal privacy and save expenses simultaneously. In this paper, we design a privACy-preserving and praCtical aggrEgate StatiStic trading scheme (named as ACCESS). Technically, we focus on the group-level pricing strategy to make ACCESS easier to implement. The differential privacy technique is applied to protect the data owners' privacy, and the sampling algorithm is adopted to reduce the data consumers' costs. Specifically, to provide a maximum tolerant privacy loss guarantee for the data owners, we design a decision algorithm to detect whether a conflict occurs between the consumer-specified accuracy level and the maximum tolerable privacy loss budget. Besides, to minimize the purchase cost for the data brokers, we develop a sampling-based aggregation method consisting of two sampling algorithms (called as BUSA and BKSA, respectively). BUSA enables reducing purchase costs with no additional background knowledge. Once the data broker knows the data boundary, BKSA can significantly reduce the amount of data that needs to be purchased, thereby the purchase cost is reduced. Rigorous theoretical analysis and extensive experiments (over four real-world and public datasets) further demonstrate the practicability of ACCESS.