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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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Published in: | Science China. Information sciences 2015-07, Vol.58 (7), p.166-180 |
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container_title | Science China. Information sciences |
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creator | Huang, XueZhen Liu, JiQiang Han, Zhen Yang, Jun |
description | 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. |
doi_str_mv | 10.1007/s11432-014-5232-3 |
format | article |
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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 |
title | Privacy beyond sensitive values |
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