基于敏感项集动态隐藏的用户隐私保护方法

发布时间：2018-01-19 01:28

本文关键词： 隐私保护 k-匿名敏感项集动态隐藏　出处：《计算机应用研究》2017年12期 　论文类型：期刊论文

【摘要】：时空k-匿名因其适应移动性环境以及实现更为简单方便等特点,是当前LBS(基于位置服务)领域中被使用最广泛的模型。由于LBS在线及动态的特性,使传统的数据变形或重构方法不足以对抗利用从大量时空k-匿名数据集挖掘到的关联规则的用户隐私攻击。针对以上问题提出了基于敏感项集动态隐藏的用户隐私保护方法(SIDH),感知敏感规则对应项集空间的正负边界,增量扩展原始快照查询匿名集数据,以敏感项集的动态隐藏净化敏感关联规则,最终实现用户隐私保护。通过对2 612辆出租车的GPS数据生成的匿名集进行敏感项集隐藏实验,结果表明,SIDH方法隐藏敏感项集的数量和速度明显高于传统匿名方法,并且不会新增敏感项集。因此SIDH方法更能有效应对匿名集敏感关联规则的推理攻击,副作用较小。
[Abstract]:Space-time k- anonymity is the most widely used model in the field of location-based services due to its characteristics of adaptability to mobile environment and easier implementation. Due to the online and dynamic characteristics of LBS. The traditional methods of data transformation or reconstruction are not sufficient to resist user privacy attacks using association rules mined from a large number of space-time k- anonymous datasets. A dynamic hiding user based on sensitive itemsets is proposed to solve the above problems. Privacy protection methods (. SIDH. Sensing sensitive rules correspond to positive and negative boundary of itemset space, and incrementally extend original snapshot to query anonymous set data, and purify sensitive association rules by dynamic hiding of sensitive item set. Finally, the user privacy protection is realized. The sensitive itemset hiding experiment is carried out on the anonymous set generated from the GPS data of 2612 taxis, and the results show that. The number and speed of hiding sensitive itemsets by SIDH method is obviously higher than that of traditional anonymous methods, and no sensitive itemsets are added. Therefore, SIDH method is more effective to deal with the reasoning attack of anonymous set sensitive association rules. The side effect is small.
【作者单位】：南京邮电大学地理与生物信息学院;南京邮电大学通信与信息工程学院;
【基金】：国家自然科学基金资助项目(41201465) 江苏省自然科学基金资助项目(BK2012439) 江苏省社会发展项目(BE2016774)
【分类号】：TP309
【正文快照】： 近年来,由于用户隐私的泄露问题日益严重,使得隐私保护逐渐变成大众聚焦的热点问题以及LBS进一步发展需要克服的难点问题[1~4]。时空k-匿名[5]是国内外众多隐私保护方法中在LBS领域使用最广泛的模型,该方法一般基于分布式LBS系统[6],其应用服务器通常会针对大时空范围的LBS用

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