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Hiding collaborative recommendation association rules

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Abstract

The concept of Privacy-Preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. For example, through data mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. There have been two types of privacy concerning data mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected.

For output privacy in hiding association rules, current approaches require hidden rules or patterns to be given in advance [10, 18–21, 24, 27]. This selection of rules would require data mining process to be executed first. Based on the discovered rules and privacy requirements, hidden rules or patterns are then selected manually. However, for some applications, we are interested in hiding certain constrained classes of association rules such as collaborative recommendation association rules [15, 22]. To hide such rules, the pre-process of finding these hidden rules can be integrated into the hiding process as long as the recommended items are given. In this work, we propose two algorithms, DCIS (Decrease Confidence by Increase Support) and DCDS (Decrease Confidence by Decrease Support), to automatically hiding collaborative recommendation association rules without pre-mining and selection of hidden rules. Examples illustrating the proposed algorithms are given. Numerical simulations are performed to show the various effects of the algorithms. Recommendations of appropriate usage of the proposed algorithms based on the characteristics of databases are reported.

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References

  1. Agrawal D, Aggarwal CC (2001) On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the 20th Symposium on Principles of Database Systems, Santa Barbara, California, USA

  2. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data, Washington DC

  3. Agrawal R, Srikant R (2000) Privacy preserving data mining. In ACM SIGMOD Conference on Management of Data, Dallas, Texas, pp 439–450

  4. Atallah E, Bertino E, Elmagarmid A, Ibrahim M, Verykios V (1999) Disclosure limitation of sensitive rules. In: Proceedings of Knowledge and Data Exchange Workshop

  5. Brankovic L, Estivill-Castro V (1999) Privacy issues in knowledge discovery and data mining. Australian Institute of Computer Ethics Conference, Lilydale

  6. Clifton C, Marks D (1996) Security and privacy implications of data mining. In: SIGMOD Workshop on Research Issues on Data Mining and knowledge Discovery

  7. Clifton C (1999) Protecting against data mining through samples. In Proceedings of the Thirteenth Annual IFIP WG 11.3 Working Conference on Database Security

  8. Clifton C (2000) Using sample size to limit exposure to data mining. J Comput Secur 8(4)

  9. Clifton C, Kantarcioglu M, Lin X, Zhu MY (2002) Tools for privacy preserving distributed data mining. SIGKDD Explorations 4(2):1–7

    Article  Google Scholar 

  10. Dasseni E, Verykios V, Elmagarmid A, Bertino E (2001) Hiding association rules by using confidence and support. In: Proceedings of 4th Information Hiding Workshop, Pittsburgh, PA, pp 369–383

  11. Evfimievski A, Srikant R, Agrawal R, Gehrke J (2002) Privacy preserving mining of association rules. In: Proceedings of the 8th ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining, Edmonton, Canada

  12. Evfimievski A (2002) Randomization in privacy preserving data mining. SIGKDD Explorations 4(2), Issue 2:43–48

    Google Scholar 

  13. Evfimievski A, Gehrke J, Srikant R (2003) Limiting Privacy Breaches in Privacy Preserving Data Mining. PODS 2003, San Diego, CA

  14. Kantarcioglu M, Clifton C (2002) Privacy-preserving distributed mining of association rules on horizontally partitioned data. In: ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery

  15. Lin W, Alvarez S, Ruiz C (2002) Efficient adaptive-support association rule mining for recommender systems. Data Mining Knowl Discov 6:83–105

    Article  MathSciNet  Google Scholar 

  16. Lindell Y, Pinkas B (2000) Privacy preserving data mining. In CRYPTO, pp 36–54

  17. O'Leary DE (1991) Knowledge discovery as a threat to database security. In: Piatetsky-Shapiro G, Frawley WJ (eds) Knowledge discovery in databases. AAAI Press/ MIT Press, Menlo Park, CA, pp 507–516

  18. Oliveira S, Zaiane O (2002) Privacy preserving frequent itemset mining. In: Proceedings of IEEE International Conference on Data Mining, November 2002, pp 43–54.

  19. Oliveira S, Zaiane O (2002) A framework for enforcing privacy in mining frequent patterns. Technical report, TR02-13, Computer Science Department, University of Alberta, Canada

  20. Oliveira S, Zaiane O (2003) Algorithms for balancing priacy and knowledge discovery in association rule mining. In: Proceedings of 7th International Database Engineering and Applications Symposium (IDEAS03), Hong Kong

  21. Oliveira S, Zaiane O (2003) Protecting sensitive knowledge by data sanitization. In: Proceedings of IEEE International Conference on Data Mining

  22. O'Mahony M, Hurley N (2004) Nicholas kushmerick and guenole silvestre, collaborative recommendation: A robustness analysis. ACM Trans Internet Technol 4(Issue 4): 344–377

    Google Scholar 

  23. Rizvi SJ, Haritsa JR (2002) Maintaining data privacy in association rule mining. In: Proceedings of the 28th Int'l Conference on Very Large Databases

  24. Saygin Y, Verykios V, Clifton C (2001) Using unknowns to prevent discovery of association rules. SIGMOND Record 30(4):45–54

    Article  Google Scholar 

  25. Vaidya J, Clifton CW (2002) Privacy preserving association rule mining in vertically partitioned data. In: Proceedings of the 8th ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining, Edmonton, Canada

  26. Verykios V, Bertino E, Fovino IG, Provenza LP, Saygin Y, Theodoridis Y (2004) State-of-the-art in privacy preserving data mining. SIGMOD Record 33(1):50–57

    Article  Google Scholar 

  27. Verykios V, Elmagarmid A, Bertino E, Saygin Y, Dasseni E (2004) Association Rules Hiding. IEEE Trans Knowl Data Eng 16(4):434–447

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, New York Institute of Technology, 1855 Broadway, New York, New York, 10023, USA

    Shyue-Liang Wang, Dipen Patel & Ayat Jafari

  2. Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, ROC

    Tzung-Pei Hong

Authors
  1. Shyue-Liang Wang
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  2. Dipen Patel
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  3. Ayat Jafari
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  4. Tzung-Pei Hong
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Correspondence to Shyue-Liang Wang.

Additional information

Leon Wang received his Ph.D. in Applied Mathematics from State University of New York at Stony Brook in 1984. From 1984 to 1987, he was an assistant professor in mathematics at University of New Haven, Connecticut, USA. From 1987 to 1994, he joined New York Institute of Technology as a research associate in the Electromagnetic Lab and assistant/associate professor in the Department of Computer Science. From 1994 to 2001, he joined I-Shou University in Taiwan as associate professor in the Department of Information Management. In 1996, he was the Director of Computing Center. From 1997 to 2000, he was the Chairman of Department of Information Management. In 2001, he was Professor and director of Library, all in I-Shou University. In 2002, he was Associate Professor and Chairman in Information Management at National University of Kaohsiung, Taiwan. In 2003, he rejoined New York Institute of Technology. Dr.Wang has published 33 journal papers, 102 conference papers, and 5 book chapters, in the areas of data mining, machine learning, expert systems, and fuzzy databases, etc. Dr. Wang is a member of IEEE, Chinese Fuzzy System Association Taiwan, Chinese Computer Association, and Chinese Information Management Association.

Ayat Jafari received the Ph.D. degree from City University of New York. He has conducted considerable research in the areas of Computer Communication Networks, Local Area Networks, and Computer Network Security, and published many technical articles. His interests and expertise are in the area of Computer Networks, Signal Processing, and Digital Communications. He is currently the Chairman of the Computer Science and Electrical Engineering Department of New York Institute of Technology.

Tzung-Pei Hong received his B.S. degree in chemical engineering from National Taiwan University in 1985, and his Ph.D. degree in computer science and information engineering from National Chiao-Tung University in 1992. He was a faculty at the Department of Computer Science in Chung-Hua Polytechnic Institute from 1992 to 1994, and at the Department of Information Management in I-Shou University from 1994 to 2001. He was in charge of the whole computerization and library planning for National University of Kaohsiung in Preparation from 1997 to 2000, and served as the first director of the library and computer center in National University of Kaohsiung from 2000 to 2001 and as the Dean of Academic Affairs from 2003 to 2006. He is currently a professor at the Department of Electrical Engineering and at the Department of Computer Science and Information Engineering. His current research interests include machine learning, data mining, soft computing, management information systems, and www applications. Springer

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Wang, SL., Patel, D., Jafari, A. et al. Hiding collaborative recommendation association rules. Appl Intell 27, 67–77 (2007). https://doi.org/10.1007/s10489-006-0031-1

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