We address the question of whether people have unique signatures - or clickprints - when they browse the Web. The importance of being able to answer this can be significant given applications to electronic commerce in general and in particular online fraud detection, a major problem in electronic commerce costing the economy billions of dollars annually. In this paper we present a general framework and specific formulations of this "unique clickprint determination problem". We explore one specific formulation for which we show how the unique clickprint determination problem can be cast more generally as an aggregation problem. To solve this we develop formal methods to determine the optimal amount of user data that must be aggregated before unique clickprints can be deemed to exist.