A privacy-preserving clustering approach toward secure and effective data analysis for business collaboration

Computers and Security

Volumn 26, Issue 1, 2007, Pages 81-93

  Oliveira, Stanley R M ^a   Zaïane, Osmar R ^b  

^a EMBRAPA CERRADOS   (Brazil)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Dimensionality reduction; Privacy preserving clustering; Privacy preserving clustering over centralized data; Privacy preserving clustering over vertically partitioned data; Privacy preserving data mining; Random projection

Indexed keywords

COMPUTER SUPPORTED COOPERATIVE WORK; DATA MINING; DATA REDUCTION; REQUIREMENTS ENGINEERING;

DIMENSIONALITY REDUCTION-BASED TRANSFORMATION (DRBT); PRIVACY-PRESERVING CLUSTERING; PRIVACY-PRESERVING CLUSTERING OVER CENTRALIZED DATA; PRIVACY-PRESERVING CLUSTERING OVER VERTICALLY PARTITIONED DATA; PRIVACY-PRESERVING DATA MINING; RANDOM PROJECTION;

DATA PRIVACY;

EID: 33846830000     PISSN: 01674048     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cose.2006.08.003     Document Type: Article

References (43)

1. Achlioptas D. Database-friendly random projections. In: Proceedings of the 20th ACM symposium on principles of database systems. Santa Barbara, CA, USA; May 2001. p. 274-81.
2. Agrawal D, Aggarwal CC. On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of ACM SIGMOD/PODS. Santa Barbara, CA; May 2001. p. 247-55.
3. Agrawal R, Srikant R. Privacy-preserving data mining. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data. Dallas, Texas; May 2000. p. 439-50.
4. Auer J.W. Linear algebra with applications (1991), Prentice-Hall Canada Inc., Scarborough, Ontario, Canada
5. Berry M., and Linoff G. Data mining techniques - for marketing, sales, and customer support (1997), John Wiley and Sons, New York, USA
6. Bingham E, Mannila H. Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. San Francisco, CA, USA; 2001. p. 245-50.
7. Blake C.L., and Merz C.J. UCI repository of machine learning databases (1998), Department of Information and Computer Sciences, University of California, Irvine
8. Caetano TS. Graphical models and point set matching. PhD thesis, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil; July 2004.
9. Dasseni E, Verykios VS, Elmagarmid AK, Bertino E. Hiding association rules by using confidence and support. In: Proceedings of the fourth information hiding workshop. Pittsburg, PA; April 2001. p. 369-83.
10. Estivill-Castro V, Brankovic L. Data swapping: balancing privacy against precision in mining for logic rules. In: Proceedings of data warehousing and knowledge discovery DaWaK-99. Florence, Italy; August 1999. p. 389-98.
11. Evfimievski A, Srikant R, Agrawal R, Gehrke J. Privacy preserving mining of association rules. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining. Edmonton, AB, Canada; July 2002. p. 217-28.
12. Faloutsos C, Lin K-I. FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In: Proceedings of the 1995 ACM SIGMOD international conference on management of data. San Jose, CA, USA; June 1995. p. 163-74.
13. Felty AP, Matwin S. Privacy-oriented data mining by proof checking. In: Proceedings of the sixth European conference on principles of data mining and knowledge discovery (PKDD). Helsinki, Finland; August 2002. p. 138-49.
14. Fern XZ, Brodley CE. Random projection for high dimensional data clustering: a cluster ensemble approach. In: Proceedings of the 20th international conference on machine learning (ICML 2003). Washington DC, USA; August 2003.
15. Fukunaga K. Introduction to statistical pattern recognition. 2nd ed. (1990), Academic Press
16. Han J., and Kamber M. Data mining: concepts and techniques (2001), Morgan Kaufmann Publishers, San Francisco, CA
17. Iyengar VS. Transforming data to satisfy privacy constraints. In: Proceedings of the eighth ACM SIGKDDinternational conference on knowledge discovery and data mining. Edmonton, AB, Canada; July 2002. p. 279-88.
18. Jagadish HV. A retrieval technique for similar shapes. In: Proceedings of the 1991 ACM SIGMOD international conference on management of data. Denver, Colorado, USA; May 1991. p. 208-17.
19. Johnson WB, Lindenstrauss J. Extensions of Lipshitz mapping into Hilbert space. In: Proceedings of the conference in modern analysis and probability. Contemporary mathematics, vol. 26; 1984. p. 189-206.
20. Johnsten T, Raghavan VV. Impact of decision-region based classification mining algorithms on database security. In: Proceedings of 13th annual IFIP WG 11.3 working conference on database security. Seattle, USA; July 1999. p. 177-91.
21. Johnsten T, Raghavan VV. Security procedures for classification mining algorithms. In: Proceedings of 15th annual IFIP WG 11.3 working conference on database and applications security. Niagara on the Lake, Ontario, Canada; July 2001. p. 293-309.
22. Kantarcioǧlu M, Clifton C. Privacy-preserving distributed mining of association rules on horizontally partitioned data. In: Proceedings of the ACM SIGMOD workshop on research issues on data mining and knowledge discovery. Madison, Wisconsin; June 2002.
23. Kaski S. Dimensionality reduction by random mapping. In: Proceedings of the international joint conference on neural networks. Anchorage, Alaska; May 1999. p. 413-18.
24. Kruskal J.B., and Wish M. Multidimensional scaling (1978), Sage Publications, Beverly Hills, CA, USA
25. Larsen B, Aone C. Fast and effective text mining using linear-time document clustering. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining. San Diego, CA, USA; August 1999. p. 16-22.
26. Lindell Y., and Pinkas B. Privacy preserving data mining. Crypto 2000. LNCS 1880 (August 2000), Springer-Verlag, Santa Barbara, CA p. 36-54
27. Lo V.S.Y. The true lift model - a novel data mining approach to response modeling in database marketing. SIGKDD Explorations 4 2 (December 2002) 78-86
28. Macqueen J. Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability vol. 1 (1967), University of California Press, Berkeley 281-297
29. Meregu S, Ghosh J. Privacy-preserving distributed clustering using generative models. In: Proceedings of the third IEEE international conference on data mining (ICDM'03). Melbourne, Florida, USA; November 2003. p. 211-18.
30. Mucsi-Nagy A, Matwin S. Digital fingerprinting for sharing of confidential data. In: Proceedings of the workshop on privacy and security issues in data Mining. Pisa, Italy; September 2004. p. 11-26.
31. Oliveira SRM, Zaïane OR. Protecting sensitive knowledge by data sanitization. In: Proceedings of the third IEEE international conference on data mining (ICDM'03). Melbourne, Florida, USA; November 2003. p. 613-16.
32. Oliveira SRM, Zaïane OR. Privacy-preserving clustering by object similarity-based representation and dimensionality reduction transformation. In: Proceedings of the workshop on privacy and security aspects of data mining (PSADM'04) in conjunction with the fourth IEEE international conference on data mining (ICDM'04). Brighton, UK; November 2004. p. 21-30.
33. Oliveira SRM, Zaïane OR, Saygin Y. Secure association rule sharing. In: Proceedings of the eighth Pacific-Asia conference on knowledge discovery and data mining (PAKDD'04). Sydney, Australia; May 2004. p. 74-85.
34. Papadimitriou CH, Tamaki H, Raghavan P, Vempala S. Latent semantic indexing: a probabilistic analysis. In: Proceedings of the 17th ACM symposium on principles of database systems. Seattle, WA, USA; June 1998. p. 159-68.
35. Rizvi SJ, Haritsa JR. Maintaining data privacy in association rule mining. In: Proceedings of the 28th international conference on very large data bases. Hong Kong, China; August 2002.
36. Samarati P. Protecting respondents' identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13 6 (2001) 1010-1027
37. Saygin Y., Verykios V.S., and Clifton C. Using unknowns to prevent discovery of association rules. SIGMOD Record 30 4 (December 2001) 45-54
38. Sweeney L. k-Anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems 10 5 (2002) 557-570
39. Vaidya J, Clifton C. Privacy preserving association rule mining in vertically partitioned data. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining. Edmonton, AB, Canada; July 2002. p. 639-44.
40. Vaidya J, Clifton C. Privacy-preserving K-means clustering over vertically partitioned data. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining. Washington, DC, USA; August 2003. p. 206-15.
41. Verykios V.S., Elmagarmid A.K., Bertino E., Saygin Y., and Dasseni E. Association rule hiding. IEEE Transactions on Knowledge and Data Engineering 16 4 (2004) 434-447
42. Young F.W. Multidimensional scaling (1987), Lawrence Erlbaum Associates, Hillsdale, New Jersey
43. Zang N, Wang S, Zhao W. A new scheme on privacy preserving association rule mining. In: Proceedings of the 15th European conference on machine learning (ECML) and the eighth European conference on principles and practice of knowledge discovery in databases (PKDD). Pisa, Italy; September 2004.