Journal of Software:2016.27(11):2828-2842

(东南大学 计算机科学与工程学院, 江苏 南京 211189)
Data Anonymization Approach for Microdata with Relational and Transaction Attributes
GONG Qi-Yuan,YANG Ming,LUO Jun-Zhou
(School of Computer Science and Engineering, Southeast University, Nanjing 211189, China)
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Received:November 09, 2015    Revised:February 23, 2016
> 中文摘要: 在发布同时包含关系和事务属性的数据(简称为关系-事务数据)时,由于关系数据和事务数据均有可能受到链接攻击,需要同时匿名这两部分的数据.现有的数据匿名技术在匿名化关系-事务数据时会造成严重的数据缺损,无法保障数据可用性.针对此问题,提出了(k,l)-多样化模型,通过等价类上的l-多样化约束和事务数据上的k-匿名约束来保证用户隐私不被泄露.在此基础上,设计并实现了APA和PAA两种满足该模型的匿名算法,以不同的顺序对关系-事务数据进行匿名,并提出了相应的数据缺损评估方法.实际公开数据集上的实验结果表明,与现有的数据匿名技术相比,APA和PAA能够在保护用户隐私的前提下,以更低的数据缺损和更高的效率完成对关系-事务数据的匿名.
Abstract:When publishing datasets that contain relational and transaction attributes, referred to as RT-data for briefness, either type of data may suffer from linking attacks. Anonymizing both of them is essential. However, previous approaches suffer from huge information loss during anonymizing RT-data, and they fail to preserve the utility of datasets. To address this problem, an anonymization model, (k,l)-diversity is proposed to ensure privacy by guaranteeing l-diversity on each equivalence class and k-anonymity on transaction data. In addition, two heuristic algorithms named APA and PAA, which anonymize RT-data in different orders, are also provided to achieve (k,l)-diversity. Extensive experiments based on real-world dataset show that APA and PAA outperform existing approaches in terms of execution time and information loss.
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基金项目:国家自然科学基金(61272054,61572130,61632008,61320106007,61502100,61402104);江苏省自然科学基金(BK20150628,BK20140648,BK20150637);中央高校基本科研业务费专项资金(2242014R30010);江苏省科技支撑项目(BE2014603);江苏省青蓝工程;江苏省网络与信息安全重点实验室资助项目(BM2003201);教育部网络与信息集成重点实验室资助项目(93K-9) 国家自然科学基金(61272054,61572130,61632008,61320106007,61502100,61402104);江苏省自然科学基金(BK20150628,BK20140648,BK20150637);中央高校基本科研业务费专项资金(2242014R30010);江苏省科技支撑项目(BE2014603);江苏省青蓝工程;江苏省网络与信息安全重点实验室资助项目(BM2003201);教育部网络与信息集成重点实验室资助项目(93K-9)
Foundation items:National Natural Science Foundation of China (61272054, 61572130, 61632008, 61320106007, 61502100, 61402104); Jiangsu Provincial Natural Science Foundation (BK20150628, BK20140648, BK20150637); Fundamental Research Funds for the Central Universities (2242014R30010); Jiangsu Provincial Key Technology R&D Program (BE2014603); Qinglan Project of Jiangsu Province; Program of Jiangsu Provincial Key Laboratory of Network and Information Security (BM2003201); Program of Key Laboratory of Computer Network and Information Integration of the Ministry of Education of China (93K-9)
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GONG Qi-Yuan,YANG Ming,LUO Jun-Zhou.Data Anonymization Approach for Microdata with Relational and Transaction Attributes.Journal of Software,2016,27(11):2828-2842