Scientific papers on semantics and aggregation procedures for SDC of qualitative variables

In this paper we consider a first attempt to deal with re-identification of individuals when the variables in the two data files to be matched are not exactly the same but "similar".

this paper is to stimulate interaction and increase synergy between researchers in both fields

Record linkage is used to establish links between those records that while belonging to two different files correspond to the same individual. Classical approaches assume that the two files contain some common variables, that are the ones used to link the records. Recently, we introduced a new approach to link records among files when such common variables are not available. In this approach, reidentification is based on the so-called structural information. In this paper we study the use of OWA operators for extracting such structural information and, thus, allowing re-identification.

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. Keywords: Microdata Protection, Privacy; 1