Hiding sensitive knowledge without side effects

Knowledge and Information Systems

Volumn 20, Issue 3, 2009, Pages 263-299

  Gkoulalas Divanis, Aris ^a   Verykios, Vassilios S ^a  

^a UNIVERSITY OF THESSALY   (Greece)

Author keywords

Association rule hiding; Borders of frequent itemsets; Data mining; Parallelization

Indexed keywords

ASSOCIATION RULES; COMPUTATIONAL EFFICIENCY; PRIVACY BY DESIGN;

EXACT SOLUTION; HIGH QUALITY; ITEM SETS; PARALLELIZATIONS; PRIVACY PRESERVING DATA MINING; SENSITIVE KNOWLEDGE HIDINGS; SIDE EFFECT; TRANSACTIONAL DATABASE;

DATA MINING;

EID: 69949190981     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-008-0178-7     Document Type: Review

References (39)

1. Agrawal R, Shafer JC (1996) Parallel mining of association rules. IEEE Trans Knowl Data Eng (TKDE) 8(1):962-969
2. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Databases (VLDB), pp 487-499
3. Agrawal R, Srikant R (2000) Privacy-preserving data mining. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp 439-450
4. Atallah M, Bertino E, Elmagarmid A, Ibrahim M, Verykios VS (1999) Disclosure limitation of sensitive rules. In: Proceedings of the 1999 IEEE Knowledge and Data Engineering Exchange Workshop (KDEX), pp 45-52
5. Bayardo R (1998) Efficiently mining long patterns from databases. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data
6. Bertino E, Fovino IN, Povenza LP (2005) A framework for evaluating privacy preserving data mining algorithms. Data Mining Knowl Discov (DMKD) 11(2):121-154
7. Cheung D, Xiao Y (1998) Effect of data skewness in parallel mining of association rules. In: Proceedings of the 2nd Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining (PAKDD), pp 48-60
8. Clifton C, Kantarciog̈lu M, Vaidya J (2002) Defining privacy for data mining. National Science Foundation Workshop on Next Generation Data Mining (WNGDM), pp 126-133
9. Clifton C, Marks D (1996) Security and privacy implications of data mining. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pp 15-19
10. Dasseni E, Verykios VS, Elmagarmid AK, Bertino E (2001) Hiding association rules by using confidence and support. In: Proceedings of the 4th International Workshop on Information Hiding, pp 369-383
11. Evfimievski A, Srikant R, Agrawal R, Gehrke J (2002) Privacy preserving mining of association rules. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 343-364
12. Farkas C, Jajodia S (2002) The inference problem: A survey. ACM SIGKDD Exploration Newsl 4(2):6-11
13. Fienberg S, Slavkovic A (2005) Preserving the confidentiality of categorical statistical data bases when releasing information for association rules. Data Mining Knowl Discov (DMKD) 11(2):155-180
14. Gkoulalas-Divanis A, Verykios VS (2006) An integer programming approach for frequent itemset hiding. In: Proceedings of the 2006 ACM Conference on Information and Knowledge Management (CIKM)
15. Gkoulalas-Divanis A, Verykios VS (2007) A hybrid approach to frequent itemset hiding. In: Proceedings of the 2007 IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp 297-304
16. Han E-H, Karypis G, Kumar V (2007) Scalable parallel data mining for association rules. In: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, pp 277-288
17. ILOG CPLEX 9.0 User's Manual (2003) ILOG Inc, Gentilly, France
18. Kantarciog̈lu M, Clifton C (2004) Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Trans Knowl Data Eng (TKDE) 16(9):1026-1037
19. Kargupta H, Datta S, Wang Q, Sivakumar K (2005) Random-data perturbation techniques and privacy-preserving data mining. Knowl Inform Syst (KAIS) 7(4):387-414
20. Karypis G, Kumar V (1998) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20(1):359-392
21. Kohavi R, Brodley C, Frasca B, Mason L, Zheng Z (2000) KDD-Cup 2000 organizers' report: Peeling the onion. SIGKDD Explorations 2(2):86-98. http://www.ecn.purdue.edu/KDDCUP
22. Lee G, Lee K, Chen A (2001) Efficient graph-based algorithms for discovering and maintaining association rules in large databases. Knowl Inform Syst (KAIS) 3(3):338-355
23. Menon S, Sarkar S, Mukherjee S (2005) Maximizing accuracy of shared databases when concealing sensitive patterns. Inform Syst Res 16(3):256-270
24. Morgenstern M (1988) Controlling logical inference in multilevel database and knowledge-base systems. In: Proceedings of the 1988 IEEE Symposium on Security and Privacy, pp 245-255
25. Moustakides G, Verykios VS (2006) A max-min approach for hiding frequent itemsets. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM), pp 502-506
26. Oliveira SRM, Zaïane OR (2002) Privacy preserving frequent itemset mining. In: Proceedings of the 2002 IEEE International Conference on Privacy, Security and Data Mining (CRPITS), pp 43-54
27. Oliveira SRM, Zaïane OR (2003) Protecting sensitive knowledge by data sanitization. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM), pp 211-218
28. Parthasarathy S, Zaki M, Ogihara M, Li W (2001) Parallel data mining for association rules on shared-memory systems. Knowl Inform Syst (KAIS) 3(1):1-29
29. Pontikakis E, Theodoridis Y, Tsitsonis A, Chang L, Verykios VS (2004) A quantitative and qualitative analysis of blocking in association rule hiding. In: Proceedings of the 2004 ACM Workshop on Privacy in the Electronic Society (WPES), pp 29-30
30. Rizvi S, Haritsa JR (2002) Maintaining data privacy in association rule mining. In: Proceedings of the 28th International Conference on Very Large Databases (VLDB)
31. Saygin Y, Verykios VS, Clifton C (2001) Using unknowns to prevent discovery of association rules. ACM SIGMOD Record 30(4):45-54
32. Sun X, Yu PS (2005) A border-based approach for hiding sensitive frequent itemsets. In: Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM), pp 426-433
33. Vaidya J, Clifton C (2002) Privacy preserving association rule mining in vertically partitioned data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 639-644
34. Verykios VS, Bertino E, Fovino IN, Provenza LP, Saygin Y, Theodoridis Y (2004a) State-of-the-art in privacy preserving data mining. ACM SIGMOD Record 33(1):50-57
35. Verykios VS, Emagarmid AK, Bertino E, Saygin Y, Dasseni E (2004b) Association rule hiding. IEEE Trans Knowl Data Eng (TKDE) 16(4):434-447
36. Xu S, Zhang J, Han D, Wang J (2006) Singular value decomposition based data distortion strategy for privacy protection. Knowl Inform Syst (KAIS) 10(3):383-397
37. Yokoo M, Durfee E, Ishida T, Kuwabara K (1998) The distributed constraint satisfaction problem: Formalization and algorithms. IEEE Trans Knowl Data Eng (TKDE) 10(5):673-685
38. Zaïane OR, El-Hajj M, Lu P (2001) Fast parallel association rule mining without candidacy generation. In: Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM), pp 665-668
39. Zou Q, Chu W, Johnson D, Chiu H (2002) A pattern decomposition algorithm for data mining of frequent patterns. Knowl Inform Syst (KAIS) 4(4):466-482