Can the utility of anonymized data be used for privacy breaches?

ACM Transactions on Knowledge Discovery from Data

Volumn 5, Issue 3, 2011, Pages

  Wong, Raymond Chi Wing ^a   Fu, Ada Wai Chee ^b   Wang, Ke ^c   Yu, Philip S ^d   Pei, Jian ^c  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^c SIMON FRASER UNIVERSITY   (Canada)

^d UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

Author keywords

diversity; Data publishing; K anonymity; Privacy preservation

Indexed keywords

ANONYMIZATION; BACKGROUND KNOWLEDGE; BENCHMARK DATASETS; DATA PUBLISHING; GROUP-BASED; INDIVIDUAL PRIVACY; K-ANONYMITY; MEDICAL RECORD; PRIVACY BREACHES; PRIVACY MODELS; PRIVACY PRESERVATION; PRIVACY PRESERVING; T-CLOSENESS;

DATA PRIVACY;

EID: 80052013720     PISSN: 15564681     EISSN: 1556472X     Source Type: Journal    
DOI: 10.1145/1993077.1993080     Document Type: Article

References (33)

1. AGGARWAL, C. C. 2008. On unifying privacy and uncertain data models. In Proceedings of the International Conference on Data Engineering (ICDE). 386-395.
2. AGGARWAL, C. C., PEI, J., AND ZHANG, B. 2006. On privacy preservation against adversarial data mining. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). 510-516. (Pubitemid 44535547)
3. AGGARWAL, G., FEDER, T., KENTHAPADI, K., MOTWANI, R., PANIGRAHY, R., THOMAS, D., AND ZHU, A. 2005. Anonymizing tables. In Proceedings of the International Conference on Database Theory (ICDT). 246-258.
4. 106 worlds and beyond: Efficient representation and processing of incomplete information. In Proceedings of the International Conference on Data Engineering (ICDE). 606-615. (Pubitemid 47422064)
5. BLAKE, E. K. C. AND MERZ, C. J. 1998. UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html.
6. BRICKELL, J. AND SHMATIKOV, V. 2008. The cost of privacy: Destruction of data-mining utility in anonymized data publishing. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). 70-78.
7. BURDICK, D., DESHPANDE, P., JAYRAM, T., RAMAKRISHNAN, R., AND VAITHYANATHAN, S. 2005. Olap over uncertain and imprecise data. In Proceedings of the International Conference on Very Large Databases (VLDB). 123-144.
8. BURDICK, D., DOAN, A., RAMAKRISHNAN, R., AND VAITHYANATHAN, S. 2007. Olap over imprecise data with domain constraints. In Proceedings of the International Conference on Very Large Databases (VLDB). 39-50.
9. CHAPRA, S. C. AND CANALE, R. P. 2002. Numerical Methods for Engineers. McGraw-Hill, 4th ed.
10. CHENG, R., CHEN, J., MOKBEL, M., AND CHOW, C. 2008. Probabilistic verifiers: Evaluating constrained nearest-neighbor queries over uncertain data. In Proceedings of the International Conference on Data Engineering (ICDE). 973-982.
11. FUNG, B. C. M., WANG, K., AND YU, P. S. 2005. Top-down specialization for information and privacy preservation. In Proceedings of the International Conference on Data Engineering (ICDE). 205-216. (Pubitemid 41731132)
12. GANTA, S. R., KASIVISWANATHAN, S. P., AND SMITH, A. 2008. Composition attacks and auxiliary information in data privacy. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). 265-273.
13. IMIELINSKI, T. AND LIPSKI, JR., W. 1984. Incomplete information in relational databases. In J. ACM. 761-791.
14. KIFER, D. 2009. Attacks on privacy and Definetti's theorem. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 127-138.
15. KIFER, D. AND GEHRKE, J. 2006. Injecting utility into anonymized datasets. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 217-228. (Pubitemid 46946515)
16. LEFEVRE, K., DEWITT, D. J., AND RAMAKRISHNAN, R. 2005. Incognito: Efficient full-domain kanonymity. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 49-60.
17. LI, N. AND LI, T. 2007. t-closeness: Privacy beyond k-anonymity and ℓ-diversity. In Proceedings of the International Conference on Data Engineering (ICDE). 106-115.
18. LI, T. AND LI, N. 2008. Injector: Mining background knowledge for data anonymization. In Proceedings of the International Conference on Data Engineering (ICDE). 446-455.
19. LI, T., LI, N., AND ZHANG, J. 2009. Modeling and integrating background knowledge in data anonymization. In Proceedings of the International Conference on Data Engineering (ICDE). 6-17.
20. MACHANAVAJJHALA, A., GEHRKE, J., AND KIFER, D. 2006. ℓ-diversity: Privacy beyond k-anonymity. In Proceedings of the International Conference on Data Engineering (ICDE). 24.
21. MARTIN, D. J., KIFER, D., MACHANAVAJJHALA, A., AND GEHRKE, J. 2007. Worst-case background knowledge for privacy-preserving data publishing. In Proceedings of the International Conference on Data Engineering (ICDE). 126-135. (Pubitemid 47422016)
22. NERGIZ, M. E. AND CLIFTON, C. 2007. Thoughts on k-anonymization. In Proceedings of International Conference on Data Engineering Workshops. 622-645. (Pubitemid 47418332)
23. SILVERSTEIN, C., MOTWANI, R., AND BRIN, S. 1997. Beyond market baskets: Generalizing association rules to correlations. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 265-276. (Pubitemid 127456791)
24. SWEENEY, L. 2002. k-anonymity: A model for protecting privacy. Inter. J. Uncertainty, Fuzziness Knowl. Based Syst. 10, 5, 557-570. (Pubitemid 35384857)
25. TOIVONEN, H. 1996. Sampling large databases for association rules. In Proceedings of the International Conference on Very Large Databases (VLDB). 134-145.
26. WANG, K., YU, P. S., AND CHAKRABORTY, S. 2004. Bottom-up generalization: A data mining solution to privacy protection. In Proceedings of the IEEE International Conference on Data Mining (ICDM). 249-256. (Pubitemid 40731038)
27. WONG, R., FU, A., WANG, K., AND PEI, J. 2007. Minimality attack in privacy preserving data publishing. In Proceedings of the International Conference on Very Large Databases (VLDB). 543-554.
28. WONG, R., LI, J., FU, A., AND WANG, K. 2006. (alpha, k)-anonymity: An enhanced k-anonymity model for privacy-preserving data publishing. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). 754-759. (Pubitemid 44535586)
29. WONG, R. C.-W., FU, A. W.-C., WANG, K., XU, Y., PEI, J., AND YU, P. 2010. Probabilistic inference protection on anonymized data. In Proceedings of the IEEE International Conference Data Mining (ICDM). 1127-1132.
30. XIAO, X. AND TAO, Y. 2006. Anatomy: Simple and effective privacy preservation. In Proceedings of the International Conference on Very Large Databases (VLDB). 139-150.
31. XIAO, X. AND TAO, Y. 2007. m-invariance: Towards privacy preserving re-publication of dynamic datasets. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 689-700. (Pubitemid 47630845)
32. ZHANG, Q., KOUDAS, N., SRIVASTAVA, D., AND YU, T. 2007. Aggregate query answering on anonymized tables. In Proceedings of the International Conference on Data Engineering (ICDE). 116-125. (Pubitemid 47422015)
33. ZHU, Z., WANG, G., AND DU, W. 2009. Deriving private information from association rule mining results. In Proceedings of the International Conference on Data Engineering (ICDE). 18-29.