Data Min Knowl Disc (2008) 16:165–196 DOI 10.1007/s10618-007-0078-6 A recursive search algorithm for statistical disclosure assessment Anna M. Manning · David J. Haglin · John A. Keane Received: 27 March 2006 / Accepted: 6 June 2007 / Published online: 10 July 2007 Springer Science+Business Media, LLC 2007 Abstract 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 data- sets of significantly more columns to be addressed. The ability of SUDA2 to identify the boundaries of the search space for MSUs is clearly demonstrated. Keywords Unique itemset · Search space · Algorithm · Recursion · Statistical disclosure Responsible editor: Hannu Toivonen. A. M. Manning (B ) · J. A. Keane School of Computer Science, University of Manchester, Oxford Rd., Manchester, M13 9PL, UK e-mail: anna@manchester.ac.uk J. A. Keane e-mail: john.keane@manchester.ac.uk D. J. Haglin Department of Computer and Information Sciences, Minnesota State University, 273 Wissink Hall, Mankato, MN 56001, USA e-mail: david.haglin@mnsu.edu