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Microdata Protection Through Approximate Microaggregation
Sun, X., Wang, H. and Li, J.
Microdata protection is a hot topic in the field of Statistical Disclosure Control, which has gained special
interest after the disclosure of 658000 queries by the
America Online (AOL) search engine in August 2006.
Many algorithms, methods and properties have been
proposed to deal with microdata disclosure. One of
the emerging concepts in microdata protection is k-anonymity, introduced by Samarati and Sweeney. k-anonymity provides a simple and efficient approach
to protect private individual information and is gaining increasing popularity. k-anonymity requires that
every record in the microdata table released be indistinguishably related to no fewer than k respondents.
In this paper, we apply the concept of entropy
to propose a distance metric to evaluate the amount
of mutual information among records in microdata,
and propose a method of constructing dependency
tree to find the key attributes, which we then use to
process approximate microaggregation. Further, we
adopt this new microaggregation technique to study
k-anonymity problem, and an efficient algorithm is
developed. Experimental results show that the proposed microaggregation technique is efficient and effective in the terms of running time and information
loss. |
Cite as: Sun, X., Wang, H. and Li, J. (2009). Microdata Protection Through Approximate Microaggregation. In Proc. Thirty-Second Australasian Computer Science Conference (ACSC 2009), Wellington, New Zealand. CRPIT, 91. Mans, B., Ed. ACS. 149-156. |
(from crpit.com)
(local if available)
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