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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1122

Title: Approximation algorithms for optimizing privacy and utility
Authors: Khaitan, P
Korra, S B
Jena, S K
Majhi, B
Keywords: privacy
approximation algorithm
anonymity
diversity
closeness
utility
Issue Date: Dec-2009
Publisher: IEEE
Citation: Proceedings of 2nd International Conference on Computer Science and its Applications CSA 2009, Dec 10-12, 2009, South Korea, P 59-64
Series/Report no.: IEEE Proceedings;Vol. I
Abstract: There are two major aspects of Data Privacy –identity disclosure and attribute disclosure. A number of algorithms have been developed to protect identity disclosure. However, many of these algorithms have not been much successful in preventing attribute disclosure. The standard implementation techniques for the commonly used privacy models are NP-hard. They are time-consuming and often unnecessary. This paper investigates the existing algorithms for preserving privacy by preventing identity disclosure. Improved approximation algorithms are proposed to prevent identity disclosure and attribute disclosure. The existing approximation algorithm for k-anonymity is extended by applying simulated annealing to reduce attribute disclosure. The concepts of l-diversity and closeness are used for minimizing attribute disclosure in polynomial time. An improved algorithm for calculating the value of t-closeness is also proposed. A new parameter is introduced to anonymize the dataset considering b...
Description: Copyright belongs to the proceedings publisher IEEE
URI: http://www.stanford.edu/~pranavkh/Privacy%20Preservation.pdf
http://hdl.handle.net/2080/1122
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