Concealing party-centric sensitive rules in a centralized data source
详细信息 查看全文
- 作者：M. Rajalakshmi (1)
T. Purusothaman (2) - 关键词：Data mining ; Association rule mining ; Privacy preserving data mining ; Sensitive rule ; Rule hiding ; Sanitization
- 刊名：International Journal of Machine Learning and Cybernetics
- 出版年：2013
- 出版时间：October 2013
- 年：2013
- 卷：4
- 期：5
- 页码：515-525
- 全文大小：464KB
- 参考文献：1. Agrawal D, Aggarwal C (2001) On the design and quantification of privacy preserving data mining algorithms. In: The Proceedings of 20th ACMSIGACT-SIGMOD-SIGART Symposium on Principles of database systems, CA, USA, pp 247鈥?55
2. Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: The Proceeding ACM SIGMOD International Conference on Management of Data, Washington, D.C., pp 207鈥?16
3. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB Conference Santiago, Chile, pp 487鈥?99
4. Agarwal R, Srikant R (2000) Privacy preserving data mining. In: Proceedings of ACM SIGMOD Conference on Management of Data (Texas, USA), pp 439鈥?50
5. Angiulli F, Ianni G, Palopoli L (2001) On the complexity of mining association rules. In: Proceedings of Nono Convegno Nazionale su Sistemi Evoluti di asidi Dati (SEBD), pp. 177鈥?84
6. Atallah M, Bertino E, Elmagarmid A, Ibrahim M, Verykios VS (1999) Disclosure limitation of sensitive rules. In: Proceedings of the IEEE Knowledge and Data Exchange Workshop (KDEX鈥?9). IEEE Computer Society, pp 45鈥?2
7. Bertino E, Fovino I, Provenza L (2005) A framework for evaluating privacy preserving data mining algorithms. Data Mining Knowl Discov 11(2):121鈥?54
8. Berson A, Smith SJ (2004) Data warehouse, data mining and OLAP. Tata McGraw Hill Press, New Delhi
9. Biggio B, Fumera G, Roli F (2010) Multiple classifier systems for robust classifier design in adversarial environments. Int J Mach Learn Cybern 1(4):27鈥?1 CrossRef
10. Clifton C, Marks D (1996) Security and privacy implications of data mining. In: Proceedings of the ACM SIGMOD Workshop on data mining and knowledge discovery, pp 15鈥?9
11. Cormen TH, Leiserson CE, Rivest RL (1990) Introduction to algorithms. MIT Press, Cambridge
12. Fayyad U, Shapiro G, Smyth P, Uthrusamy R (1996) Advances in knowledge discovery and data mining, AAAI Press/MIT Press, Menlo Park
13. Gkoulalas-Divanis A, Verykios S (2009) Exact knowledge hiding through database extension. IEEE Trans Knowl Data Eng 21(5):699鈥?13 CrossRef
14. Han J, Fu Y (1995) Discovery of multiple-level association rules from large databases. In: Proceedings of 1995 International Conference on Very Large Data Bases (VLDB'95), Z眉rich, Switzerland, pp 420鈥?31
15. Han J, Kamber M (2001) Data mining: concepts and techniques, Morgan Kaufmann Publishers, San Francisco
16. Han J, Pei J, Yin Y, Mao R (2004) Mining frequent patterns without candidate generation: a frequent-pattern tree approach. IEEE Trans Data Mining Knowl Discov 8(1):53鈥?7 CrossRef
17. Lee G, Chang C, Chen ALP (2004) Hiding sensitive patterns in association rules mining. In: Proceedings of the 28th Annual International Computer Software and Applications Conference (COMPSAC鈥?4)
18. Lin D, Kedem ZM (2002) Pincer-search: an efficient algorithm for discovering the maximum frequent set. IEEE Trans Knowl Data Eng 14(3):553鈥?56
19. Lindell Y, Pinkas B (2000) Privacy preserving data mining. Lecture Notes in Computer Science
20. Nedunchezhian R, Anbumani K (2006) Rapid privacy preserving algorithm for large databases international. J Intell Inform Technol 2(1):68鈥?1 CrossRef
21. Oliveira S, Zaiane O (2003) Privacy preserving clustering by data transformation. In: Proceedings of the 18th Brazilian Symposium on Databases (SBBD 2003), pp 304鈥?18
22. Saygin Y, Verykios S, Elmagarmid K (2002) Privacy preserving association rule mining. In: Proceedings of 12th International Workshop on Research Issues in data engineering: engineering e-commerce/e-business systems (RIDE鈥?2), San Jose, pp 151鈥?58
23. Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. In: Proceedings of ACM SIGMOD International Conference on Management of Data, Montreal, Canada, pp 1鈥?2
24. Stanley R, Oliveira M, Zaiane O (2003) Protecting sensitive knowledge by data sanitization. In: Proceedings of Third IEEE International Conference on data mining (ICDM鈥?3), Florida, pp 613鈥?16
25. Stanley R, Oliveira M, Zaiane R (2002) Privacy preserving frequent itemset mining. In: Proceedings of the IEEE international conference on privacy, security and data mining (PSDM鈥?2), Maebashi, pp 43鈥?4
26. Vaidya J, Clifton C (2004) Privacy-preserving data mining: why, how, and when. IEEE Security Priv 2(6):19鈥?7
27. Verykios S, Elmagarmid K, Bertino E, Saygin Y, Dasseni E (2004) Association rule hiding. IEEE Trans Knowl Data Eng 16(4):434鈥?47 CrossRef
28. Verykios S, Bertino E, Provenza I, Saygin Y, Theodoridis Y (2004) State-of-the-Art in privacy preserving data mining. ACM SIGMOD Record 33(1):50鈥?7 CrossRef
29. Wu Y, Chiang C, Chen A (2007) Hiding sensitive association rules with limited side effects. IEEE Trans Knowl Data Eng 19(1):29鈥?2
30. Zaki MJ (2000) Generating non-redundant association rules. In: The proceedings of The Sixth ACM SIGKDD International Conference on knowledge discovery and data mining, pp 34鈥?3 - 作者单位：M. Rajalakshmi (1)
T. Purusothaman (2)
1. Department of Computer Science and Engineering and Information Technology, Coimbatore Institute of Technology, Coimbatore, Tamilnadu, India
2. Department of Computer Science and Engineering and Information Technology, Government College of Technology, Coimbatore, Tamilnadu, India - ISSN：1868-808X
文摘
Developments in leaps and bounds in communication and database technology have made resource sharing easy and simple. Data mining is widely used as a business intelligence tool in order to derive meaningful information from centralized or distributed data sources. Recent advances in data mining algorithms have increased the security risks by revealing the sensitive information contained in a data source. When multiple parties are allowed to mine a centralized data source, sensitive information of one party may become easily accessible by another party which is not secured. This proposed work deals with ways and means to hide sensitive information of various parties thus enabling access to legitimate information while securing the sensitive information of other parties. A new data structure called Reduced Transaction Table (RTT) has been proposed to locate transactions pertaining to the sensitive information without multiple scans of the data source thereby reducing the time complexity. The performance of the proposed method is empirically verified and found that it achieves better performance than the existing one.