At present most privacy preserving algorithms based on l-diversity model are limited only to static data release. It is low efficiency and vulnerable to inference attack if these anonymous algorithms are directly applied to dynamic data publishing. To address this issue, this paper analyzes various inference channels that possibly exist between multiple anonymized datasets and discusses how to avoid such inferences and provides an effective approach to securely anonymize a dynamic dataset based on incremental clustering: incremental l-diversity algorithm. Theory analysis and experiment results show that the proposed method is effective and efficient.
With the rapid development of the new generation of information technology, the analysis of mobile social network big data is getting deeper and deeper. At the same time, the risk of privacy disclosure in social network is also very obvious. In this paper, we summarize the main access control model in mobile social network, analyze their contribution and point out their disadvantages. On this basis, a practical privacy policy is defined through authorization model supporting personalized privacy preferences. Experiments have been conducted on synthetic data sets. The result shows that the proposed privacy protecting model could improve the security of the mobile social network while keeping high execution efficiency.
In recent years, with the explosive development in Internet, data storage and data processing technologies, privacy preservation has been one of the greater concerns in data mining. A number of methods and techniques have been developed for privacy preserving data mining. This paper provided a wide survey of different privacy preserving data mining algorithms and analyzed the representative techniques for privacy preservation. The existing problems and directions for future research are also discussed.
The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications. The existing researches on user privacy protection in mobile social network mainly focus on privacy preserving data publishing and access control. There is little research on the association of user privacy information, so it is not easy to design personalized privacy protection strategy, but also increase the complexity of user privacy settings. Therefore, this paper concentrates on the association of user privacy information taking big data analysis tools, so as to provide data support for personalized privacy protection strategy design.
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