Towards optimal k-anonymization

Data and Knowledge Engineering

Volumn 65, Issue 1, 2008, Pages 22-39

  Li, Tiancheng ^a   Li, Ninghui ^a  

^a Purdue University   (United States)

Author keywords

Anonymization; Generalization; Privacy

Indexed keywords

ALGORITHMS; INFORMATION DISSEMINATION; OPTIMAL CONTROL SYSTEMS; RESEARCH AND DEVELOPMENT MANAGEMENT;

ENUMERATION ALGORITHMS; HIGHER-QUALITY ANONYMIZATIONS;

DATA MINING;

EID: 39749201160     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2007.06.015     Document Type: Article

References (21)

1. C. Aggarwal, On k-anonymity and the curse of dimensionality, in: Proceedings of the 31st International Conference on Very Large Data Bases (VLDB), 2005.
2. G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy, D. Thomas, A. Zhu, Anonymizing tables, in: Proceedings of International Conference on Database Theory (ICDT), 2005.
3. G. Aggarwal, T. Feder, K. Kenthapadi, S. Khuller, R. Panigrahy, D. Thomas, A. Zhu, Achieving anonymity via clustering, in: Proceedings of the 25th ACM Symposium on Principles of Database Systems (PODS), 2006.
4. R. J. Bayardo, R. Agrawal, Data privacy through optimal k-anonymization, in: Proceedings of the 21st International Conference on Data Engineering (ICDE), 2005.
5. J. Domingo-Ferrer, V. Torra, Disclosure protection methods and information loss for microdata, in: Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, 2001, pp. 91-110.
6. Domingo-Ferrer J., and Torra V. Ordinal, continuous and heterogeneous k-anonymity through microaggregation. Data Mining and Knowledge Discovery 11 2 (2005) 195-212
7. B.C.M. Fung, K. Wang, P.S. Yu, Top-down specialization for information and privacy preservation, in: Proceedings of the 21st International Conference on Data Engineering (ICDE), 2005.
8. V. S. Iyengar, Transforming data to satisfy privacy constraints, in: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2002.
9. K. LeFevre, D. DeWitt, R. Ramakrishnan, Incognito: efficient full-domain k-anonymity, in: Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 2005.
10. K. LeFevre, D. DeWitt, R. Ramakrishnan, Mondrian multidimensional k-anonymity, in: Proceedings of the 22nd International Conference on Data Engineering (ICDE), 2006.
11. N. Li, T. Li, S. Venkatasubramanian, t-closeness: privacy beyond k-anonymity and ℓ-diversity, in: Proceedings of the 23rd International Conference on Data Engineering (ICDE), 2007.
12. A. Machanavajjhala, J. Gehrke, D. Kifer, M. Venkitasubramaniam, ℓ-diversity: privacy beyond k-anonymity, in: Proceedings of the 22nd International Conference on Data Engineering (ICDE), 2006.
13. A. Meyerson, R. Williams, On the complexity of optimal k-anonymity, in: Proceedings of the 23rd ACM Symposium on Principles of Database Systems (PODS), 2004.
14. Rota G.C. The number of partitions of a set. American Mathematical Monthly 71 5 (1964) 498-504
15. R. Rymon, Search through systematic set enumeration, in: Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning (KR-02), 1992.
16. Samarati P. Protecting respondents privacy in microdata release. IEEE Transaction on Knowledge and Data Engineering 13 6 (2001) 1010-1027
17. P. Samarati, L. Sweeney, Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression, Technical Report SRI-CSL-98-04, SRI Computer Science Laboratory, 1998.
18. Sweeney L. Achieving k-anonymity privacy protection using generalization and suppression. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10 5 (2002) 571-588
19. Sweeney L. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10 5 (2002) 557-570
20. K. Wang, P.S. Yu, S. Chakraborty, Bottom-up generalization: a data mining solution to privacy protection, in: Proceedings of the 4th International Conference on Data Mining (ICDM), 2004.
21. Webb G.I. Opus: an efficient admissible algorithm for unordered search. Journal of Artificial Intelligence Research (1995) 431-465