Abstract
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.
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Notes
- 1.
The \(\epsilon \) unit price can be of any form, including a monetary one. The method we propose is independent from the nature of the price, so we do not need to specify it.
- 2.
From a technical point of view, the additive property holds also for large values of \(\epsilon \). However, from a practical point of view, for large values of \(\epsilon \), for instance 200 and 400, then the original information is almost entirely revealed in both cases, and would not make sense to pay twice the price of 200 \(\epsilon \) units to achieve 400 \(\epsilon \) units.
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Biswas, S., Jung, K., Palamidessi, C. (2021). An Incentive Mechanism for Trading Personal Data in Data Markets. In: Cerone, A., Ölveczky, P.C. (eds) Theoretical Aspects of Computing – ICTAC 2021. ICTAC 2021. Lecture Notes in Computer Science(), vol 12819. Springer, Cham. https://doi.org/10.1007/978-3-030-85315-0_12
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