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