Microdata Protection Through Approximate Microaggregation

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 kanonymity, introduced by Samarati and Sweeney. kanonymity 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 micr...

• Type: Conference
• Year: 2009
• Where: ACSC
• Authors: Xiaoxun Sun, Hua Wang, Jiuyong Li
• Keywords: ACSC 2009, Computer Science, Microdata, Microdata Disclosure, Microdata Protection