Satisfying privacy requirements before data anonymization

Computer Journal

Volumn 55, Issue 4, 2012, Pages 422-437

  Sun, Xiaoxun ^a   Wang, Hua ^b   Li, Jiuyong ^c   Zhang, Yanchun ^d  

^a Australian Council for Educational Research   (Australia)

^b UNIVERSITY OF SOUTHERN QUEENSLAND   (Australia)

^c UNIVERSITY OF SOUTH AUSTRALIA   (Australia)

^d VICTORIA UNIVERSITY   (Australia)

Author keywords

data anonymization; system security; www privacy

Indexed keywords

COMPUTATION COMPLEXITY; DATA ANONYMIZATION; DATA SETS; DATA SIZE; EXECUTION TIME; NON-TRIVIAL; PRIVACY REQUIREMENTS; REAL LIFE DATASETS; SATISFACTION PROBLEM; SPACE OVERHEAD; STANDARD DEVIATION; SYSTEM SECURITY;

HEURISTIC METHODS; RATING; SURVEYS;

DATA PRIVACY;

EID: 84859300639     PISSN: 00104620     EISSN: 14602067     Source Type: Journal    
DOI: 10.1093/comjnl/bxr028     Document Type: Article

References (33)

1. Sweeney, L. 2002 k-Anonymity: A model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 10, 557-570.
2. Machanavajjhala, A., Gehrke, J., Kifer, D. and Venkitasubramaniam, M. (2006) l-Diversity: Privacy Beyond k-Anonymity. ICDE, Atlanta, GA, USA.
3. Li, N., Li, T. and Venkatasubramanian, S. (2007) t-Closeness: Privacy Beyond k-Anonymity and l-Diversity. ICDE, Istanbul, Turkey, pp. 106-115
4. Sun, X.,Wang, H. and Li, J. (2009) Injecting Purposes and Trust into Data Anonymization. CIKM, Hong Kong, China.
5. Hansell, S.AOL removes search data on vast group of web users. New York Times, Aug 8, 2006.
6. Samarati, P. and Sweeney, L. (1998) Generalizing Data to Provide Anonymity when Disclosing Information. PODS, Seattle, WA, USA.
7. Samarati, P. and Sweeney, L. (1998) Protecting privacy when disclosing information: K-anonymity and its enforcement through generalization and suppression. Technical Report SRI-CSL-98-04, SRI Computer Science Laboratory.
8. Samarati, P. (2001) Protecting respondents' identities in microdata release. IEEE Trans. Knowl. Data Eng., 13, 1010-1027.
9. Aggarwal, C. (2005) On k-Anonymity and the Curse of Dimensionality. VLDB, Trondheim, Norway.
10. Bayardo, R. and Agrawal, R. (2005) Data Privacy Through Optimal k-Anonymization. ICDE, Tokyo, Japan.
11. Fung, B., Wang, K. and Yu, P. (2005) Top-down Specialization for Information and Privacy Preservation. ICDE, Tokyo, Japan.
12. Iyengar, V. (2002) Transforming Data to Satisfy Privacy Constraints. SIGKDD, Edmonton, Alberta, Canada.
13. LeFevre, K., DeWitt, D. and Ramakrishnan, R. (2005) Incognito: Efficient Full-domain k-Anonymity. SIGMOD, Baltimore, MD, USA.
14. LeFevre, K., DeWitt, D. and Ramakrishnan, R. (2006) Mondrian Multidimensional k-Anonymity. ICDE, Atlanta, GA, USA.
15. Li, J., Tao,Y. and Xiao, X. (2008) Preservation of Proximity Privacy in Publishing Numerical Sensitive Data. ACM Conference on Management of Data (SIGMOD), Vancouver, BC, Canada.
16. Ghinita, G., Tao,Y. and Kalnis, P. (2008) On the Anonymization of Sparse High-Dimensional Data. Proceedings of International Conference on Data Engineering (ICDE), April 2008, Cancun, Mexico.
17. Xu,Y.,Wang, K., Fu, A. andYu, P. (2008) Anonymization Transaction Databases for Publication. KDD, Las Vegas, NV, USA.
18. Hafner, K. (2006) And if you liked the movie, a Netflix contest may reward you handsomely. New York Times, Oct 2, 2006.
19. Narayanan, A. and Shmatikov, V. (2008) Robust de-anonymization of large sparse datasets. IEEE Symposium on Secur. and Priv. 2008: 111-125, Oakland, CA, USA.
20. Xiao, X. and Tao, Y. (2006) Anatomy: Simple and Effective Privacy Preservation. VLDB, Seoul, Korea.
21. Frankowski, D., Cosley, D., Sen, S., Terveen, L. and Riedl, J. (2006). You Are What You Say: Privacy Risks of Public Mentions. SIGIR, Seattle,WA, USA. pp. 565-572.
22. Sun, X., Wang, H., Li, J. and Pei, J. (2010) Publish anonymous survey rating data. Accepted by Data Min. Knowl. Discov.
23. Agrawal, R. and Srikant, R. (2000) Privacy-Preserving Data Mining. SIGMOD, Dallas, TX, USA.
24. Agrawal, D. and Aggarwal, C. (2001) On The Design and Qualification of Privacy Preserving Data Mining Algorithm. Proc. Symposium on Principles of Database Systems (PODS), pp. 247-255, Santa Barbara, CA, USA.
25. Evfimievski, R., Srikant, R., Agrawal, R. and Gehrke, J. (2002) Privacy preserving mining of association rules. SIGKDD, Edmonton, Alberta, Canada.
26. Atzori, M., Bonchi, F., Giannotti, F. and Pedreschi, D. (2005) Blocking Anonymity Threats Raised by Frequent Itemset Mining. ICDM, Houston, TX, USA.
27. Atzori, M., Bonchi, F., Giannotti, F. and Pedreschi, D. (2005) k-Anonymous Patterns. PKDD, Porto, Portugal.
28. Atzori, M., Bonchi, F., Giannotti, F. and Pedreschi, D. (2008) Anonymity preserving pattern discovery. VLDB J., 17, 703-727.
29. Verykios, V., Elmagarmid, A., Bertino, E., Dasseni, E. and Saygin, Y. (2004). Association rule hiding. IEEE Trans. Knowl. Data Eng., 16, 434-447.
30. Backstrom, L.,Dwork, C. and Kleinberg, J. (2007) WhereforeArt Thou R3579x?: Anonymized Social Networks, Hidden Patterns, and Structural Steganography. WWW, Banff, Alberta, Canada.
31. Huang, X., Xiang, Y., Chonka, A., Zhou, J. and Deng, R. (2010) A Generic Framework for Three-Factor Authentication: Preserving Security and Privacy in Distributed Systems. IEEE Transactions on Parallel and Distributed Systems, November 09. http://doi.ieeecomputersociety.org/10.1109/TPDS.2010.206
32. Hamming, R. (1980). Coding and Information Theory. Prentice Hall, Englewood Cliffs, NJ.
33. Friedman, J., Bentley, J. and Finkel, R. (1977). An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw., 3, 209-226.