An Incentive Mechanism for Trading Personal Data in Data Markets

With the proliferation of digital data economy, is considered as crude oil in twenty-first century, and its value increasing. Keeping pace with this trend, model market trading between providers consumers, starting to emerge a process obtain high-quality personal information exchange for some compensation. However, risk privacy violations caused by analysis hinders providers' participation market. Differential privacy, de-facto standard protection, can solve problem, but, on other hand, it deteriorates utility. In paper, we introduce pricing mechanism that takes into account trade-off accuracy. We propose method induce provider accurately report her price and, optimize order maximize consumer's profit within budget constraints. show formally proposed achieves these properties, also, validate them experimentally.