Personal Data Market Optimization Pricing Model Based on Privacy Level
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 limit...
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doaj-d4fc76cd89eb480daa30a2504ba7b0f22020-11-24T22:28:49ZengMDPI AGInformation2078-24892019-04-0110412310.3390/info10040123info10040123Personal Data Market Optimization Pricing Model Based on Privacy LevelJian Yang0Chunxiao Xing1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaResearch 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, ChinaIn 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.https://www.mdpi.com/2078-2489/10/4/123privacy datacompensationwillingness to pay (WTP)pricingmulti-level |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jian Yang Chunxiao Xing |
spellingShingle |
Jian Yang Chunxiao Xing Personal Data Market Optimization Pricing Model Based on Privacy Level Information privacy data compensation willingness to pay (WTP) pricing multi-level |
author_facet |
Jian Yang Chunxiao Xing |
author_sort |
Jian Yang |
title |
Personal Data Market Optimization Pricing Model Based on Privacy Level |
title_short |
Personal Data Market Optimization Pricing Model Based on Privacy Level |
title_full |
Personal Data Market Optimization Pricing Model Based on Privacy Level |
title_fullStr |
Personal Data Market Optimization Pricing Model Based on Privacy Level |
title_full_unstemmed |
Personal Data Market Optimization Pricing Model Based on Privacy Level |
title_sort |
personal data market optimization pricing model based on privacy level |
publisher |
MDPI AG |
series |
Information |
issn |
2078-2489 |
publishDate |
2019-04-01 |
description |
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. |
topic |
privacy data compensation willingness to pay (WTP) pricing multi-level |
url |
https://www.mdpi.com/2078-2489/10/4/123 |
work_keys_str_mv |
AT jianyang personaldatamarketoptimizationpricingmodelbasedonprivacylevel AT chunxiaoxing personaldatamarketoptimizationpricingmodelbasedonprivacylevel |
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