ON THE SECURITY OF MICROAGGREGATION WITH INDIVIDUAL RANKING: ANALYTICAL ATTACKS

It is shown in this paper how to find interval estimates for the original data based on the microaggregated data, which can be considerably narrower than intervals resulting from subtraction of means, and can be useful to detect lack of security in a microaggRegated data set.

Abstract

Microaggregation is a statistical disclosure control technique. Raw microdata (i.e. individual records) are grouped into small aggregates prior to publication. With fixed-size groups, each aggregate contains k records to prevent disclosure of individual information. Individual ranking is a usual criterion to reduce multivariate microaggregation to univariate case: the idea is to perform microaggregation independently for each variable in the record. Using distributional assumptions, we show in this paper how to find interval estimates for the original data based on the microaggregated data. Such intervals can be considerably narrower than intervals resulting from subtraction of means, and can be useful to detect lack of security in a microaggregated data set. Analytical arguments given in this paper confirm recent empirical results about the unsafety of individual ranking microaggregation.