Privacy beyond sensitive values

Privacy-preserving data publishing （PPDP） has become one of the hot topics in the field of network security. The existing privacy-preserving work cannot deal with vulnerability in sensitivity, under which an attacker can effectively infer the victim＇s sensitivity of the sensitive value with limited...

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Bibliographic Details
Published in:Science China. Information sciences 2015-07, Vol.58 (7), p.166-180
Main Authors: Huang, XueZhen, Liu, JiQiang, Han, Zhen, Yang, Jun
Format: Article
Language:English
Subjects:
Computer Science
Information Systems and Communication Service
LSV
Privacy
Research Paper
Sensitivity
受害人
攻击者
敏感性
数据发布
灵敏度
网络安全
隐私保护
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Summary:Privacy-preserving data publishing （PPDP） has become one of the hot topics in the field of network security. The existing privacy-preserving work cannot deal with vulnerability in sensitivity, under which an attacker can effectively infer the victim＇s sensitivity of the sensitive value with limited knowledge. In this paper, we propose a novel privacy model, （V, L）-anonymity, to avoid sensitivity vulnerability while having the capabilities of the existing PPDP models. This model considers the difference among sensitive values （SV） and applies SV classification, named as sensitivity levels. The model can be applied not only to the single attribute, but also to multiple sensitive attributes situation. We use （l1, l2）-diversity to validate the capability of our model theoretically. We carry out extensive experiments to verify the improvement on effectiveness and efficiency. Furthermore, we present a Levels of Sensitive Values （LSV） measure to calculate the sensitivity level.
ISSN:1674-733X
1869-1919
DOI:10.1007/s11432-014-5232-3