Protecting the Publishing Identity in Multiple Tuples

Current privacy preserving methods in data publishing always remove the individually identifying attribute first and then generalize the quasi-identifier attributes. They cannot take the individually identifying attribute into account. In fact, tuples will become vulnerable in the situation of multiple tuples per individual. In this paper, we analyze the individually identifying attribute in the privacy preserving data publishing and propose the concept of identity-reserved anonymity. We develop two approaches to meet identity-reserved anonymity requirement. The algorithms are evaluated in an experimental scenario, demonstrating practical applicability of the approaches.

[1]  Pierangela Samarati,et al.  Protecting Respondents' Identities in Microdata Release , 2001, IEEE Trans. Knowl. Data Eng..

[2]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[3]  Latanya Sweeney,et al.  Achieving k-Anonymity Privacy Protection Using Generalization and Suppression , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[4]  Rajeev Motwani,et al.  Anonymizing Tables , 2005, ICDT.

[5]  Roberto J. Bayardo,et al.  Data privacy through optimal k-anonymization , 2005, 21st International Conference on Data Engineering (ICDE'05).

[6]  David J. DeWitt,et al.  Incognito: efficient full-domain K-anonymity , 2005, SIGMOD '05.

[7]  Philip S. Yu,et al.  Top-down specialization for information and privacy preservation , 2005, 21st International Conference on Data Engineering (ICDE'05).

[8]  David J. DeWitt,et al.  Mondrian Multidimensional K-Anonymity , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[9]  Yufei Tao,et al.  Personalized privacy preservation , 2006, Privacy-Preserving Data Mining.

[10]  Yufei Tao,et al.  Anatomy: simple and effective privacy preservation , 2006, VLDB.

[11]  ASHWIN MACHANAVAJJHALA,et al.  L-diversity: privacy beyond k-anonymity , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[12]  Ashwin Machanavajjhala,et al.  Worst-Case Background Knowledge in Privacy , 2006 .

[13]  Ashwin Machanavajjhala,et al.  l-Diversity: Privacy Beyond k-Anonymity , 2006, ICDE.

[14]  Raymond Chi-Wing Wong,et al.  (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing , 2006, KDD '06.

[15]  Ninghui Li,et al.  t-Closeness: Privacy Beyond k-Anonymity and l-Diversity , 2007, 2007 IEEE 23rd International Conference on Data Engineering.