A Privacy Protection Model of Data Publication Based on Game Theory

With the rapid development of sensor acquisition technology, more and more data are collected, analyzed, and encapsulated into application services. However, most of applications are developed by untrusted third parties. Therefore, it has become an urgent problem to protect users’ privacy in data pu...

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Bibliographic Details
Published in:	Security and communication networks 2018-01, Vol.2018, p.1-13
Main Authors:	Kuang, Li, Zhu, Yujia, Li, Shuqi, Yan, Xuejin, Yan, Han, Deng, Shuiguang
Format:	Article
Language:	eng
Subjects:	Algorithms Clustering Communication Data collection Data encryption Data loss Data mining Datasets Game theory Identification Privacy Quality of service
Online Access:	Get full text
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Summary:	With the rapid development of sensor acquisition technology, more and more data are collected, analyzed, and encapsulated into application services. However, most of applications are developed by untrusted third parties. Therefore, it has become an urgent problem to protect users’ privacy in data publication. Since the attacker may identify the user based on the combination of user’s quasi-identifiers and the fewer quasi-identifier fields result in a lower probability of privacy leaks, therefore, in this paper, we aim to investigate an optimal number of quasi-identifier fields under the constraint of trade-offs between service quality and privacy protection. We first propose modelling the service development process as a cooperative game between the data owner and consumers and employing the Stackelberg game model to determine the number of quasi-identifiers that are published to the data development organization. We then propose a way to identify when the new data should be learned, as well, a way to update the parameters involved in the model, so that the new strategy on quasi-identifier fields can be delivered. The experiment first analyses the validity of our proposed model and then compares it with the traditional privacy protection approach, and the experiment shows that the data loss of our model is less than that of the traditional k-anonymity especially when strong privacy protection is applied.
ISSN:	1939-0114 1939-0122