Geometric data perturbation for privacy preserving outsourced data mining

Knowledge and Information Systems

Volumn 29, Issue 3, 2011, Pages 657-695

  Chen, Keke ^a   Liu, Ling ^b  

^a Wright State University   (United States)

^b Georgia Institute of Technology   (United States)

Author keywords

Data mining algorithms; Data perturbation; Geometric data perturbation; Privacy evaluation; Privacy preserving data mining

Indexed keywords

EID: 81155123637     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-010-0362-4     Document Type: Article

References (41)

1. Aggarwal CC, Yu PS (2004) A condensation approach to privacy preserving data mining. In: Proceedings of international conference on extending database technology (EDBT), vol 2992. Springer, Heraklion, pp 183-199.
2. Agrawal D, Aggarwal CC (2002) On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of ACM conference on principles of database systems (PODS). ACM, Madison.
3. Agrawal R, Srikant R, (2000) Privacy-preserving data mining. In: Proceedings of ACM SIGMOD conference. ACM, Dallas.
4. Amazon (n. d.) Applications hosted on amazon clouds. http://aws. amazon. com/solutions/case-studies/.
5. Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2009) Above the clouds: a berkeley view of cloud computing. Technical report, University of Berkerley.
6. Bhatia R (1997) Matrix analysis. Springer, Berlin.
7. Bruening PJ, Treacy BC (2009) Privacy, security issues raised by cloud computing. BNA Privacy Security Law Report 8(10).
8. Chen K, Liu L (2005) A random rotation perturbation approach to privacy preserving data classification In: Proceedings of international conference on data mining (ICDM). IEEE, Houston.
9. Chen K, Liu L, Sun G (2007) Towards attack-resilient geometric data perturbation. In: SIAM data mining conference.
10. Clifton C (2003) Tutorial: Privacy-preserving data mining, In Proceedings of ACM SIGKDD Conference.
11. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge.
12. Evfimievski A, Gehrke J, Srikant R (2003) Limiting privacy breaches in privacy preserving data mining. In: Proceedings of ACM conference on principles of database systems (PODS).
13. Evfimievski A, Srikant R, Agrawal R, Gehrke J (2002) Privacy preserving mining of association rules. In: Proceedings of ACM SIGKDD conference.
14. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5): 1189-1232.
15. Fung BC, Wang K, Chen R, Yu PS (2010) Privacy-preserving data publishing: a survey on recent developments. ACM Comput Surv 42(4): 1-53.
16. Gallier J (2000) Methods and applications for computer science and engineering. Springer, New York.
17. Google (n. d.) Google appengine gallery. http://appgallery. appspot. com/.
18. Guo S, Wu X (2007) Deriving private information from arbitrarily projected data. In: Proceedings of the 11th European conference on principles and practice of knowledge Discovery in databases (PKDD07). Warsaw, Poland.
19. Guo S, Wu X, Li Y (2008) Determining error bounds for spectral filtering based reconstruction methods in privacy preserving data mining. Knowl Inform Syst 17(2): 217-240.
20. Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer, Berlin.
21. Huang Z, Du W, Chen B, (2005) Deriving private information from randomized data. In: Proceedings of ACM SIGMOD conference.
22. Hyvarinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley, London.
23. Jain A, Murty M, Flynn P (1999) Data clustering: a review. ACM Comput Surv 31: 264-323.
24. Jiang T (2006) How many entries in a typical orthogonal matrix can be approximated by independent normals. Ann Prob 34(4): 1497-1529.
25. Johnson WB, Lindenstrauss J (1984) Extensions of lipshitz mapping into hilbert space. Contemp Math 26: 189-206.
26. Kargupta H, Datta S, Wang Q, Sivakumar K (2003) On the privacy preserving properties of random data perturbation techniques. In: Proceedings of international conference on data mining (ICDM).
27. LeFevre K, DeWitt DJ, Ramakrishnan R (2006) Mondrain multidimensional k-anonymity. In: Proceedings of IEEE international conference on data engineering (ICDE).
28. Lehmann EL, Casella G (1998) Theory of point estimation. Springer, Berlin.
29. Lindell Y, Pinkas B (2000) Privacy preserving data mining. J Cryptol 15(3): 177-206.
30. Liu K, Giannella C, Kargupta H (2006) An attacker's view of distance preserving maps for privacy preserving data mining In: European conference on principles and practice of knowledge discovery in databases (PKDD). Berlin, Germany.
31. Liu K, Kargupta H, Ryan J (2006) Random projection-based multiplicative data perturbation for privacy preserving distributed data mining. IEEE Trans Knowl Data Eng 18(1): 92-106.
32. Luo H, Fan J, Lin X, Zhou A, Bertino E (2009) A distributed approach to enabling privacy-preserving model-based classifier training. Knowl Inform Syst 20(2): 157-185.
33. McLachlan G, Peel D (2000) Finite mixture models. Wiley, London.
34. Oliveira SRM, Zaïane OR (2004) Privacy preservation when sharing data for clustering. In: Proceedings of the international workshop on secure data management in a connected world. Toronto, Canada, pp 67-82.
35. Oliveira SR, Zaiane OR (2010) Privacy preserving clustering by data transformation. J Inform Data Manag (JIDM) 1(1): 67-82.
36. Sadun L (2001) Applied linear algebra: the decoupling principle. Prentice Hall, Englewood Cliffs.
37. Stewart G (1980) The efficient generation of random orthogonal matrices with an application to condition estimation. SIAM J Num Anal 17: 403-409.
38. Sweeney L (2002) k-anonymity: a model for protecting privacy. Int J Uncert Fuzz Knowl Based Syst 10(5): 557-570.
39. Teng Z, Du W (2009) A hybrid multi-group approach for privacy-preserving data mining. Knowl Inform Syst 19(2): 133-157.
40. Vaidya J, Clifton C (2003) Privacy preserving k-means clustering over vertically partitioned data. In: Proceedings of ACM SIGKDD conference.
41. Vempala SS (2005) The random projection method. American Mathematical Society, Providence.