Efficient Privacy-Preserving Collaborative Filtering Based on the Weighted Slope One Predictor
Rating-based Collaborative Filtering (CF) predicts the rating that a user will give to an item, derived from the ratings of other items given by other users. Such CF schemes utilise either user neighbourhoods (i.e. user-based CF) or item neighbourhoods (i.e. item-based CF). In this paper, the authors present a privacy-preserving item-based CF scheme through the use of an additively homomorphic public-key cryptosystem on the weighted Slope One predictor; and show its applicability on both horizontal and vertical partitions, and include a discussion on arbitrary partitions as well. They present an evaluation of their proposed scheme in terms of communication and computation complexity, performance of cryptographic primitives and performance of a single-partition, single machine implementation in 64-bit Java.