《计算机应用研究》|Application Research of Computers

K-Q:支持海量查询的隐私泄露检测算法

K-Q:algorithm for privacy disclosure detection supportinglarge number of queries

免费全文下载 (已被下载 次)  
获取PDF全文
作者 林永妍,宋玲,陈玉婵
机构 广西大学 计算机与电子信息学院,南宁 530004
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2013)12-3767-04
DOI 10.3969/j.issn.1001-3695.2013.12.064
摘要 针对推理检测需要的所有历史查询结果的数据规模较大这一问题, K-Q算法结合K-匿名模型在历史查询结果的存储规模上进行了优化, 通过推理攻击模拟算法在线检测恶意查询。在真实数据集上的实验证明了K-Q算法可以自适应于查询规模的增长, 在准确率和内存消耗上都明显优于已有的直接基于相关元组合并优化的T-D算法。
关键词 K-匿名;数据共享平台;隐私泄露检测;推理攻击
基金项目
本文URL http://www.arocmag.com/article/01-2013-12-064.html
英文标题 K-Q:algorithm for privacy disclosure detection supportinglarge number of queries
作者英文名 LIN Yong-yan, SONG Ling, CHEN Yu-chan
机构英文名 School of Computer, Electronics & Information, Guangxi University, Nanning 530004, China
英文摘要 A key problem remained that the data set required to detect inference attack cannot all fit in memory, K-Q algorithm optimized the real data storage for each history query based on K-anonymization model, it detected the illegal query online through simulating the real inference attack. Experiments on real data demonstrate that K-Q algorithm can scale on query size, and perform on detect accuracy and memory consumption is better than the existed T-D algorithm which directly merge related tuples and also assure the privacy control's granularity.
英文关键词 K-anonymity; data sharing platform; privacy disclosure; inference attack
参考文献 查看稿件参考文献
  [1] 焉凯, 何贤芒. 基于局部聚类的数据匿名化算法[J] . 计算机应用研究, 2012, 29(1):148-151.
[2] LI Tian-cheng, LI Ning-hui. On the tradeoff between privacy and utility in data publishing[C] //Proc of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2009:517-526.
[3] LI Tian-cheng, LI Ning-hui, ZHANG Jian. Modeling and integrating background knowledge in data anonymization[C] //Proc of the 25th IEEE International Conference on Data Engineering. Washington DC:IEEE Computer Society, 2009:6-17.
[4] 王平水, 王建东. 匿名化隐私保护技术研究进展[J] . 计算机科学, 2010, 27(6):2016-2019.
[5] 王平水, 马钦娟. 隐私保护K-匿名算法研究[J] . 计算机工程与应用, 2011, 47(28):117-200.
[6] YIP R, LEVITT K. Data level inference detection in database systems[C] //Proc of the 11th IEEE Computer Security Foundations Workshop. 1998:179-189.
[7] BRODSKY A, FARKAS C, JOJODIA S. Secure databases:constraints, inference channels, and monitoring disclosures[J] . IEEE Trans on Knowledge and Data Engineering, 2000, 12(6):900-919.
[8] TRUTA T M, VINAY B. Privacy protection:P-sensitine K-anonymity property[C] //Proc of the 22nd International Conference on Data Engineering Workshops. Washington DC:IEEE Computer Society, 2006:94-98.
[9] MACHANAVAJJHALA A, GEHRKE J, KIFER D, et al. L-diversity:privacy beyond K-anonymity[J] . ACM Trans on Knowledge Discover from Data, 2007, 1(1):3-16.
[10] CUI Bin-ge, LIU Da-xin. An inference detection algorithm based on related tuples P-sensitive K-anonymit property[C] //Proc of the 22nd International Conference on Data Mining. 2005:1011-1017.
收稿日期
修回日期
页码 3767-3770
中图分类号 TP309
文献标志码 A