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Home > Archive>Volume 27, Issue 11, 2016 >2828-2842. DOI:10.13328/j.cnki.jos.005099
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Data Anonymization Approach for Microdata with Relational and Transaction Attributes
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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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    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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龚奇源,杨明,罗军舟.面向关系-事务数据的数据匿名方法.软件学报,2016,27(11):2828-2842

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History
  • Received:November 09,2015
  • Revised:February 23,2016
  • Online: May 05,2016
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