Comparisons of K-anonymization and randomization schemes under linking attacks

Zhouxuan Teng, Wenliang Du

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Recently K-anonymity has gained popularity as a privacy quantification against linking attacks, in which attackers try to identify a record with values of some identifying attributes. If attacks succeed, the identity of the record will be revealed and potential confidential information contained in other attributes of the record will be disclosed. K-anonymity counters this attack by requiring that each record must be indistinguishable from at least K - 1 other records with respect to the identifying attributes. Randomization can also be used for protection against linking attacks. In this paper, we compare the performance of K-anonymization and randomization schemes under linking attacks. We present a new privacy definition that can be applied to both k-anonymization and randomization. We compare these two schemes in terms of both utility and risks of privacy disclosure, and we promote to use R-U confidentiality map for such comparisons. We also compare various randomization schemes.

Original languageEnglish (US)
Title of host publicationProceedings - Sixth International Conference on Data Mining, ICDM 2006
Pages1091-1096
Number of pages6
DOIs
StatePublished - Dec 1 2006
Event6th International Conference on Data Mining, ICDM 2006 - Hong Kong, China
Duration: Dec 18 2006Dec 22 2006

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other6th International Conference on Data Mining, ICDM 2006
CountryChina
CityHong Kong
Period12/18/0612/22/06

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Teng, Z., & Du, W. (2006). Comparisons of K-anonymization and randomization schemes under linking attacks. In Proceedings - Sixth International Conference on Data Mining, ICDM 2006 (pp. 1091-1096). [4053159] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2006.40