Resumen de A recursive search algorithm for statistical disclosure assessment
Anna M. Manning, David J. Haglin, John A. Keane
A new algorithm, SUDA2, is presented which finds minimally unique itemsets i.e., minimal itemsets of frequency one. These itemsets, referred to as Minimal Sample Uniques (MSUs), are important for statistical agencies who wish to estimate the risk of disclosure of their datasets. SUDA2 is a recursive algorithm which uses new observations about the properties of MSUs to prune and traverse the search space. Experimental comparisons with previous work demonstrate that SUDA2 is several orders of magnitude faster, enabling datasets of significantly more columns to be addressed. The ability of SUDA2 to identify the boundaries of the search space for MSUs is clearly demonstrated.
