Optimal k-anonymity with flexible generalization schemes through bottom-up searching

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2006, Pages 518-523

  Li, Tiancheng ^a   Li, Ninghui ^a  

^a Purdue University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

POPULATION STATISTICS;

BOTTOM UP; CENSUS DATA; COST METRICS; INFORMATION LOSS; K-ANONYMITY; SEARCH STRATEGIES;

DATA MINING;

EID: 76749111107     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/icdmw.2006.127     Document Type: Conference Paper

References (9)

1. R. J. Bayardo and R. Agrawal. Data privacy through optimal k-anonymization. In ICDE, pages 217-228, 2005.
2. V. S. Iyengar. Transforming data to satisfy privacy constraints. In SIGKDD, pages 279-288, 2002.
3. K. LeFevre, D. DeWitt, and R. Ramakrishnan. Incognito: Efficient full-domain k-anonymity. In SIGMOD, pages 49-60, 2005.
4. G.-C. Rota. The number of partitions of a set. In Amer. Math. Monthly, 71(5):498-504, 1964.
5. P. Samarati. Protecting respondent's privacy in microdata release. In IEEE TKDE, 13(6):1010-1027, 2001.
6. P. Samarati and L. Sweeney. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. SRI Technical Report SRICSL-98-04, 1998.
7. L. Sweeney. Achieving k-anonymity privacy protection using generalization and suppression. In Int. J. on Uncertain. Fuzz, 10(6):571-588, 2002.
8. L. Sweeney. K-anonymity: A model for protecting privacy. In Int. J. on Uncertain. Fuzz, 10(5):557-570, 2002.
9. G. I. Webb. Inclusive pruning: a new class of pruning rule for unordered search and its application to classification learning. In Proc. Australasian Computer Science Conference, pages 1-10, 1996.