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No More Privacy Any More?
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Information 2019, 10(4), 123; https://doi.org/10.3390/info10040123

Personal Data Market Optimization Pricing Model Based on Privacy Level

1
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Research Institute of Information, Beijing National Research Center for Information Science and Technology, Department of Computer Science and Technology, Institute of Internet Industry, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Received: 3 March 2019 / Revised: 29 March 2019 / Accepted: 30 March 2019 / Published: 3 April 2019
(This article belongs to the Special Issue The End of Privacy?)
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PDF [2605 KB, uploaded 3 April 2019]
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Abstract

In the era of the digital economy, data has become a new key production factor, and personal data represents the monetary value of a data-driven economy. Both the public and private sectors want to use these data for research and business. However, due to privacy issues, access to such data is limited. Given the business opportunities that have gaps between demand and supply, we consider establishing a private data market to resolve supply and demand conflicts. While there are many challenges to building such a data market, we only focus on three technical challenges: (1) How to provide a fair trading mechanism between data providers and data platforms? (2) What is the consumer’s attitude toward privacy data? (3) How to price personal data to maximize the profit of the data platform? In this paper, we first propose a compensation mechanism based on the privacy attitude of the data provider. Second, we analyze consumer self-selection behavior and establish a non-linear model to represent consumers’ willingness to pay (WTP). Finally, we establish a bi-level programming model and use genetic simulated annealing algorithm to solve the optimal pricing problem of personal data. The experimental results show that multi-level privacy division can meet the needs of consumers and maximize the profit of data platform. View Full-Text
Keywords: privacy data; compensation; willingness to pay (WTP); pricing; multi-level privacy data; compensation; willingness to pay (WTP); pricing; multi-level
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Yang, J.; Xing, C. Personal Data Market Optimization Pricing Model Based on Privacy Level. Information 2019, 10, 123.

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