微信服务号

微信订阅号

2025-4-24- 12
Home > Archive>Volume 27, Issue 11, 2016 >2828-2842. DOI:10.13328/j.cnki.jos.005099
PDF HTML XML Export Cite reminder
Data Anonymization Approach for Microdata with Relational and Transaction Attributes
Author:
Affiliation:

Fund Project:

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)

  • Article
    • | |
  • Metrics
    • |
  • Reference
    • |
  • Related
    • |
  • Cited by
    • | |
  • Comments
    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.

    Reference
    Related
    Cited by
Get Citation

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

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:November 09,2015
  • Revised:February 23,2016
  • Online: May 05,2016
Links:Institute of Software, CASChina Computer FederationCASNSFC
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address：4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code：100190
Phone：010-62562563 Fax：010-62562533 Email：jos@iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063