Personalized privacy-preserving frequent itemset mining using randomized response

The Scientific World Journal

Volumn 2014, Issue , 2014, Pages

  Sun, Chongjing ^a   Fu, Yan ^a   Zhou, Junlin ^a   Gao, Hui ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CONTROLLED STUDY; DATA COLLECTION METHOD; DATA MINING; FREQUENT ITEMSET MINING; MEASUREMENT ACCURACY; PERSONALIZED PRIVACY; PRIVACY; PROTECTION; RANDOMIZED RESPONSE TECHNIQUE; SYSTEM ANALYSIS; ALGORITHM; FACTUAL DATABASE;

ALGORITHMS; DATA MINING; DATABASES, FACTUAL; PRIVACY;

EID: 84899574762     PISSN: None     EISSN: 1537744X     Source Type: Journal    
DOI: 10.1155/2014/686151     Document Type: Article

References (21)

1. Agrawal R., Srikant R., Fast algorithms for mining association rules. 1215 Proceedings of the 20th International Conference on Very Large Data Bases (VLDB '94) September 1994 Santiago de Chile, Chile 487 499
2. Cranor L. F., Reagle J., Ackerman M. S., Beyond Concern: Understanding Net Users' Attitudes about Online Privacy 2000 Cambridge, Mass, USA MIT Press
3. Haritsa J. R., Mining association rules under privacy constraints. Privacy-Preserving Data Mining 2008 New York, NY, USA Springer 239 266
4. Xiao X., Tao Y., Personalized privacy preservation. Proceedings of the ACM International Conference on Management of Data (SIGMOD '06) June 2006 Chicago, Ill, USA ACM 229 240 2-s2.0-34250680246 10.1145/1142473.1142500
5. Xiao X., Tao Y., Chen M., Optimal random perturbation at multiple privacy levels. Proceedings of the VLDB Endowment 2009 2 1 814 825
6. Tamhane A. C., Randomized response techniques for multiple sensitive attributes. Journal of the American Statistical Association 1981 76 376 916 923 10.1080/01621459.1981.10477741
7. Rizvi S. J., Haritsa J. R., Maintaining data privacy in association rule mining. Proceedings of the 28th International Conference on Very Large Data Bases (VLDB '02) August 2002 Hong Kong VLDB Endowment 682 693
8. Verykios V. S., Gkoulalas-Divanis A., A survey of association rule hiding methods for privacy. Privacy-Preserving Data Mining 2008 New York, NY, USA Springer 267 289
9. Wu Y.-H., Chiang C.-M., Chen A. L. P., Hiding sensitive association rules with limited side effects. IEEE Transactions on Knowledge and Data Engineering 2007 19 1 29 42 2-s2.0-33845625238 10.1109/TKDE.2007.250583
10. Pontikakis E. D., Tsitsonis A. A., Verykios V. S., An experimental study of distortion-based techniques for association rule hiding. Research Directions in Data and Applications Security XVIII 2004 144 Springer 325 339 IFIP International Federation for Information Processing 10.1007/1-4020-8128-6-22
11. Pontikakis E. D., Theodoridis Y., Tsitsonis A. A., Chang L., Verykios V. S., A quantitative and qualitative analysis of blocking in association rule hiding. Proceedings of the ACM Workshop on Privacy in the Electronic Society (WPES '04) October 2004 Washington, DC, USA ACM Press 29 30 2-s2.0-20444468588
12. Wang S.-L., Jafari A., Using unknowns for hiding sensitive predictive association rules. Proceedings of the IEEE International Conference on Information Reuse and Integration (IRI '05) August 2005 Las Vegas, Nev, USA IEEE 223 228 2-s2.0-33745725573 10.1109/IRI-05.2005.1506477
13. Xingzhi S., Yu P. S., A border-based approach for hiding sensitive frequent itemsets. Proceedings of the 5th IEEE International Conference on Data Mining (ICDM '05) November 2005 Houston, Tex, USA IEEE 426 433 2-s2.0-34548591826 10.1109/ICDM.2005.2
14. Moustakides G. V., Verykios V. S., A max-min approach for hiding frequent itemsets. Proceedings of the 6th IEEE International Conference on Data Mining Workshops (ICDM '06) December 2006 Hong Kong 502 506 2-s2.0-48649086773 10.1109/ICDMW.2006.8
15. Menon S., Sarkar S., Mukherjee S., Maximizing accuracy of shared databases when concealing sensitive patterns. Information Systems Research 2005 16 3 256 270 2-s2.0-25844456648 10.1287/isre.1050.0056
16. Gkoulalas-Divanis A., Verykios V. S., An integer programming approach for frequent itemset hiding. Proceedings of the 15th ACM Conference on Information and Knowledge Management (CIKM '06) November 2006 Arlington, Va, USA ACM 748 757 2-s2.0-34547641381 10.1145/1183614.1183721
17. Vaidya J., Clifton C., Privacy preserving association rule mining in vertically partitioned data. Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '02) July 2002 Alberta, Canada ACM 639 644 2-s2.0-0242709355
18. Kantarcioglu M., Clifton C., Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Transactions on Knowledge and Data Engineering 2004 16 9 1026 1037 2-s2.0-4544361334 10.1109/TKDE.2004.45
19. Zhang N., Wang S., Zhao W., A new scheme on privacy preserving association rule mining. Knowledge Discovery in Databases: PKDD 2004 2004 3202 484 495 Lecture Notes in Computer Science 2-s2.0-35048843897 10.1007/978-3-540-30116-5-44
20. Warner S. L., Randomized response: a survey technique for eliminating evasive answer bias. Journal of the American Statistical Association 1965 60 309 63 66 2-s2.0-0013776710
21. Zheng Z., Kohavi R., Mason L., Real world performance of association rule algorithms. Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '01) August 2001 San Francisco, Calif, USA ACM 401 406 2-s2.0-0035788918