A framework for efficient data anonymization under privacy and accuracy constraints

ACM Transactions on Database Systems

Volumn 34, Issue 2, 2009, Pages

  Ghinita, Gabriel ^a   Karras, Panagiotis ^a   Kalnis, Panos ^a   Mamoulis, Nikos ^b  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Anonymity; Privacy

Indexed keywords

ANONYMITY; ANONYMIZATION; CONSTRAINED PROBLEM; DATA ANONYMIZATION; EXECUTION TIME; EXPERIMENTAL EVALUATION; INFORMATION LOSS; K-ANONYMITY; K-ANONYMIZATION; LINEAR TIME; MAPPING TECHNIQUES; MICRODATA; ONE-DIMENSIONAL PROBLEM; OPTIMAL SOLUTIONS; PRIVACY; PRIVACY AND ACCURACY; PRIVACY PRESERVING; SENSITIVE INFORMATIONS;

ONE DIMENSIONAL;

DATA PRIVACY;

EID: 68649129111     PISSN: 03625915     EISSN: 15574644     Source Type: Journal    
DOI: 10.1145/1538909.1538911     Document Type: Article

References (28)

1. AGGARWAL, G., FEDER, T., KENTHAPADI, K., KHULLER, S., PANIGRAHY, R., THOMAS, D., AND ZHU, A. 2006. Achieving anonymity via clustering. In Proceedings of ACM Conference on Principles of Database Systems (PODS), 153-162.
2. AGGARWAL, G., FEDER, T., KENTHAPADI, K., MOTWANI, R., PANIGRAHY, R., THOMAS, D., AND ZHU, A. 2005. Approximation algorithms for k-anonymity. J. Privacy Technol. Article number: 20051120001.
3. BAYARDO, R. J. AND AGRAWAL, R. 2005. Data privacy through optimal k-anonymization. In Proceedings of the International Conference on Data Engineering (ICDE), 217-228.
4. BYUN, J.-W., KAMRA, A., BERTINO, E., AND LI, N. 2007. Efficient k -anonymization using clustering techniques. In Proceedings of the International Conference on Database Systems for Advanced Applications (DASFAA), 188-200.
5. FROOMKIN, A. 2000. The death of privacy. Stanford Law Rev. 52, 5, 1461-1543.
6. GHINITA, G., KARRAS, P., KALNIS, P., AND MAMOULIS, N. 2007. Fast data anonymization with low information loss. In Proceedings of International Conference on Very Large Data Bases (VLDB), 758-769.
7. HARINARAYAN, V., RAJARAMAN, A., AND ULLMAN, J. D. 1996. Implementing data cubes efficiently. In Proceedings of ACM International Conference on Management of Data (SIGMOD), 205-216.
8. IWUCHUKWU, T. AND NAUGHTON, J. F. 2007. k-Anonymization as spatial indexing: toward scalable and incremental anonymization. In Proceedings of International Conference on Very Large Data Bases (VLDB), 746-757.
9. IYENGAR, V. S. 2002. Transforming data to satisfy privacy constraints. In Proceedings of International Conference on Knowledge Discovery and Data Mining (KDD), 279-288.
10. KIFER, D. AND GEHRKE, J. 2006. Injecting utility into anonymized datasets. In Proceedings of ACM International Conference on Management of Data (SIGMOD), 217-228.
11. LEFEVRE, K., DEWITT, D. J., AND RAMAKRISHNAN, R. 2005. Incognito: Efficient full-domain k-anonymity. In Proceedings of ACM International Conference on Management of Data (SIGMOD), 49-60.
12. LEFEVRE, K., DEWITT, D. J., AND RAMAKRISHNAN, R. 2006a. Mondrian multidimensional k-anonymity. In Proceedings of International Conference on Data Engineering (ICDE).
13. LEFEVRE, K., DEWITT, D. J., AND RAMAKRISHNAN, R. 2006b. Workload-aware anonymization. In Proceedings of International Conference on Knowledge Discovery and Data Mining (KDD), 277-286.
14. LI, N., LI, T., AND V ENKATASUBRAMANIAN, S. 2007. t-Closeness: Privacy beyond k-anonymity and l-diversity. In Proceedings of International Conference on Data Engineering (ICDE), 106-115.
15. MACHANAVAJJHALA, A., GEHRKE, J., KIFER, D., AND VENKITASUBRAMANIAM, M. 2006. lDiversity: Privacy beyond k-anonymity. In Proceedings of International Conference on Data Engineering (ICDE).
16. MEYERSON, A. AND WILLIAMS, R. 2004. On the complexity of optimal k-anonymity. In Proceedings of ACM Conference on Principles of Database Systems (PODS), 223-228.
17. MOON, B., JAGADISH, H., AND FALOUTSOS, C. 2001. Analysis ofthe clustering properties of the Hilbert space-filling curve. IEEE Trans. Knowl. Data Eng. 13, 1, 124-141.
18. MUTHUKRISHNAN, S. AND SUEL, T. 2005. Approximation algorithms for array partitioning problems. J. Algo. 54, 1, 85-104.
19. PARK, H. AND SHIM, K. 2007. Approximate algorithms for k-anonymity. In Proceedings of ACM International Conference on Management of Data (SIGMOD), 67-78.
20. SAMARATI, P. 2001. Protecting respondents' identities in micro-data release. IEEE Trans. Knowl. Data Eng. 13, 6, 1010-1027.
21. SWEENEY, L. 2002. k-Anonymity: A model for protecting privacy. Int. J. Uncertainty, Fuzziness Knowl.-Based Syst. 10, 5, 557-570.
22. WONG, R., FU, A., PEI, J., WANG, K., WAN, S., AND LO., C. 2006. Multidimensional k-anonymization by linear clustering using space-filling curves. Tech. rep. 2006-27, Simon Fraser University. March.
23. XIAO, X. AND TAO, Y. 2006a. Anatomy: Simple and effective privacy preservation. In Proceedings of International Conference on Very Large Data Bases (VLDB), 139-150.
24. XIAO, X. AND TAO, Y. 2006b. Personalized privacy preservation. In Proceedings of ACM International Conference on Management of Data (SIGMOD), 229-240.
25. XIAO, X. AND TAO, Y. 2007. m-Invariance: Towards privacy preserving re-publication of dynamic datasets. In Proceedings of ACM International Conference on Management of Data (SIGMOD), 689-700.
26. XU, J., WANG, W., PEI, J., WANG, X., SHI, B., AND FU, A. 2006. Utility-based anonymization using local recoding. In Proceedings of International Conference on Knowledge Discovery and Data Mining (KDD), 785-790.
27. ZHANG, Q., KOUDAS, N., SRIVASTAVA, D., AND YU, T. 2007. Aggregate query answering onanonymized tables. In Proceedings of International Conference on Data Engineering (ICDE), 116-125.
28. ZHANG, R., KALNIS, P., OOI, B.C., AND TAN, K.-L. 2005. Generalized multidimensional data mapping and query processing. ACM Trans. Datab. Syst. 30, 3, 661-697.