On the anonymization of sparse high-dimensional data

Proceedings - International Conference on Data Engineering

Volumn , Issue , 2008, Pages 715-724

  Ghinita, Gabriel ^a   Tao, Yufei ^b   Kalnis, Panos ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

TECHNOLOGY;

ANONYMIZATION; COMPUTATIONAL OVERHEADS; DATA ENGINEERING; DATA PUBLISHING; HIGH-DIMENSIONAL DATA; INFORMATION LOSSES; INTERNATIONAL CONFERENCES; LOW DIMENSIONALITY; PRIVACY-PRESERVING; REAL-LIFE DATASETS; RELATIONAL DATA; TRANSACTION DATA;

CANNING;

EID: 52649106883     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2008.4497480     Document Type: Conference Paper

References (25)

1. A. Machanavajjhala. J. Gehrke, D. Kifer, and M. Venkitasubramaniam, "1-Diversdty: Privacy Beyond k-Anonymity," in Proc. of ICDE, 2006.
2. R. J. Bayardo and R. Agrawal, "Data Privacy through Optimal k-Anonymization," in Proc. of ICDE. 2005, pp. 217-228.
3. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan, "Incognito: Efficient Full-domain k-Anorivmity," in Proc. of ACM SIGMOD,"2005, pp. 49-60.
4. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan, "Mondrian Multidi-mensional k-Anonymity." in Proc. of ICDE. 2006.
5. C. C. Aggarwal, "On k-Anonymity and the Curse, of Dimensionality." in Proc. of VLDB, 2005. pp. 901-909.
6. X. Xiao and Y. Tao, "Anatomy: Simple and Effective Privacy Preservation," in Proc. of VLDB, 2006.
7. L. Sweeney, "k-Anonymity: A Model for Protecting Privacy," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10. no. 5. pp. 557-570, 2002.
8. D. Kifer and J. Gehrke. "Injecting Utility into Anonymized Datasets," in Proc. of ACM SIGMOD, 2006, pp. 217-228.
9. N. Gibbs, W. Poole, and P. Stockmeyer, "An algorithm for reducing the bandwidth and profile of a sparse matrix." SIAM J. Numerical Analysis, vol. 13, pp. 236-250. 1976.
10. J. K. Reid and J. A. Scott, "Reducing the total bandwidth of a sparse unsymmetric matrix," SIAM J. Matrix Anal. Appl. vol. 28, no. 3, pp. 805-821, 2006.
11. C. Papadimitriou. "The NP-completeness of the bandwidth minimization problem." Computing, vol. 16. pp. 263-270, 1976.
12. E. Cuthill and J. McKee, "Reducing the bandwidth of sparse symmetric matrices," in 24th National ACM Conference, 1969, pp. 157-172.
13. W. M. Chan and A. George, "A linear time implementation of the Reverse Cuthill-McKee algorithm," BIT Numerical Mathematics, vol, 20, no. 1, pp. 8-14, 1980.
14. Z. Zheng, R. Kohavi, and L. Mason, "Real world performance of association rule algorithms,'' in Proc. of KDD, 2001, pp. 401-406.
15. R. Agrawal and R. Srikant, "Privacy preserving data mining," in Proc. of ACM SIGMOD. 2000.
16. Z. Huang, W. Du. and B. Chen. "Deriving private information from randomized data," in Proc. of ACM SIGMOD, 2005.
17. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. "Workload-aware Anonymization," in Proc. of KDD, 2006, pp. 277-286.
18. G. Aggarwal, T. Feder, K. Kenthapadi, S. Khuller, R. Panigrahy, D. Thomas, and A. Zhu, "Achieving Anonymity via Clustering," in Proc. of ACM PODS. 2006, pp. 153-162.
19. J. Xu, W. Wang, J. Pei, X. Wang. B. Shi, and A. Fu, "Utility-Based Anonymization Using Local Receding," in Proc. of SIGKDD, 2006. pp. 20-23.
20. O. Ghinita, P. Karras, P. Kalnis, and N. Mamoulis. "Fast Data Anoriymization with Low Information Loss," in Proc. of VLDB, 2007. pp. 758-769.
21. P. Kalnis, G. Ghinita, K. Mouratidis, and D. Papadias, "Preventing Location-Based Identity Inference in Anonymous Spatial Queries." IEEE TKDE, vol. 19, no. 12, pp. 1719-1733, 2007.
22. Q. Zhang. N. Koudas, D. Srivastava, and T. Yu, "Aggregate Query Answering on Anonymized Tables," in Proc. of ICDE, 2007.
23. M. Atzori, F. Bonclii. F. Giannotti, and D. Pedreschi, "Anonymity preserving pattern discovery," VLDB Journal, 2008 (to appear').
24. V. Verykios. A. Elmagarmid, E. Bertino, Y. Saygin, and E. Dasseni, "Association rule hiding," IEEE TKDE, vol. 16, no. 4, pp. 434-447, 2004.
25. A. Narayanan and V. Shmatikov. "How To Break Anonymity of the Netflix Prize Dataset," http://arxiv.org/abs/cs/0610105.