Fast Learning from Distributed Datasets without Entity Matching

Abstract

Consider the following scenario: two datasets/peers contain the same real-world entities described using partially shared features, e.g. banking and insurance company records of the same customer base. Our goal is to learn a classifier in the cross product space of the two domains, in the hard case in which no shared ID is available — e.g. due to anonymization. Traditionally, the problem is approached by first addressing entity matching and subsequently learning the classifier in a standard manner. We present an end-to-end solution which bypasses matching entities, based on the recently introduced concept of Rademacher observations (rados). Informally, we replace the minimisation of a loss over examples, which requires entity resolution, by the equivalent minimisation of a (different) loss over rados. We show that (i) a potentially exponential-size subset of these rados does not require to perform entity matching, and (ii) the algorithm that provably minimizes the loss over rados has time and space complexities smallerthan the algorithm minimizing the equivalent example loss.Last, we relax a key assumption, that the data is vertically partitioned among peers — in this case, we would not even know the existence of a solution to entity resolution. In this more general setting, experiments validate the possibility of beating even the optimal peer in hindsight.