A new algorithm for finding minimal sample uniques for use in statistical disclosure assessment

We present SUDA2, a recursive algorithm for finding minimal sample uniques (MSUs). SUDA2 uses a novel method for representing the search space for MSUs and new observations about the properties of MSUs to prune and traverse this space. Experimental comparisons with previous work demonstrate that SUDA2 is not only several orders of magnitude faster but is also capable of identifying the boundaries of the search space, enabling datasets of larger numbers of columns than before to be addressed.