Abstract
Privacy-preserving data publishing (PPDP) has become one of the hot topics in the field of networksecurity. The existing privacy-preserving work cannot deal with vulnerability in sensitivity, under which anattacker can effectively infer the victim’s sensitivity of the sensitive value with limited knowledge. In thispaper, we propose a novel privacy model, (V,L)-anonymity, to avoid sensitivity vulnerability while having thecapabilities 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 singleattribute, but also to multiple sensitive attributes situation. We use (l1, l2)-diversity to validate the capabilityof our model theoretically. We carry out extensive experiments to verify the improvement on effectiveness andefficiency. Furthermore, we present a Levels of Sensitive Values (LSV) measure to calculate the sensitivity level.
创新点
文章提出了一个新的隐私模型匿名, 在拥有现有隐私模型能力的同时, 解决了隐私泄露这一威胁。文章考虑了敏感值的差异并对敏感值分类, 称为敏感级。模型不仅适用于单个敏感属性, 而且适用于多敏感属性的情形。实验验证了模型的改进是有效且高效的。此外, 提出了一种敏感值级别测量方法以计算敏感级。
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Huang, X., Liu, J., Han, Z. et al. Privacy beyond sensitive values. Sci. China Inf. Sci. 58, 1–15 (2015). https://doi.org/10.1007/s11432-014-5232-3
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DOI: https://doi.org/10.1007/s11432-014-5232-3