Abstract
The rapid development of the mobile Internet coupled with the widespread use of intelligent terminals have intensified the digitization of personal information and accelerated the evolution of the era of big data. The sharing and publishing of various big data brings convenience and also increases the risk of personal privacy leakage. In order to reduce users’ privacy leakage that may be caused by data release, many privacy preserving data publishing methods have been proposed by scientists in both academia and industry in the recent years. However, non-numerical sensitive information has natural semantic relevance, and therefore, synonymous linkages may still exist and cause serious privacy disclosures in privacy protection methods based on an anonymous model. To address this issue, this paper proposes a privacy preserving dynamic data publishing method based on microaggregation. A series of indicators are accordingly designed to evaluate the synonymous linkages between the non-numerical sensitive values which in turn facilitate in improving the clustering effect of the microaggregation anonymous method. The dynamic update program is introduced into the proposed microaggregation method to realize the dynamic release and update of data. Experimental analysis suggests that the proposed method provides better privacy protection effect and availability of published data in contrast to the state-of-the-art methods.
Highlights
Age Sex Zipcode Disease 58 F 10030 Anemia 49 M 10037 Enteritis 49 M 10022 Anemia 51 M 10029 Lymphoma 50 F 10033 Leukemia 33 F 10019 Enteritis M 10013 Bronchitis F 10010 Anemia 51 M 10024 Leukemia
Our principal contributions are as follows: (1) A series of indicators are designed to evaluate the synonymous linkages between the non-numerical sensitive values in turn facilitating an improvement in the clustering effect of the proposed microaggregation anonymous method; (2) The improved microaggregation algorithm is proposed to enhance the privacy protection effect of the published data by minimizing the distance between records and the total number of linkages, and for maximizing an increase of entropy; (3) The dynamic update program is introduced into the proposed microaggregation method to realize the dynamic release and update of data
Data publishing methods based on K-anonymity model, l-diversity model, and their improvement strategies cannot effectively prevent the semantic linkages between the non-numerical sensitive values, thereby leading to privacy leakage problems
Summary
Age Sex Zipcode Disease 58 F 10030 Anemia 49 M 10037 Enteritis 49 M 10022 Anemia 51 M 10029 Lymphoma 50 F 10033 Leukemia 33 F 10019 Enteritis M 10013 Bronchitis F 10010 Anemia 51 M 10024 Leukemia. In order to reduce the privacy leakage caused by semantic correlation between sensitive values during data publishing, we propose a new privacy preserving publishing algorithm against synonymous linkage based on microaggregation. The last part of the proposed microaggregation metric, i.e., probability mass synonymous linkage, maximizes the semantic diversity of sensitive attribute in each equivalent group.
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