Privacy-preserving collaborative filtering on arbitrarily partitioned data

Abstract

Data collected for collaborative filtering purposes might be arbitrarily partitioned between two parties, even rival companies. Online vendors might have insufficient user ratings. Scarce data then might cause offering inaccurate and unreliable recommendations. In order to supply trustworthy and dependable predictions, one solution for such companies might be cooperation on partitioned user preference data. However, it is still a challenge to convince e-commerce sites cooperate on partitioned data so that they can provide richer collaborative filtering services, due to privacy concerns. Unless confidentiality is protected, such companies are expected to face with serious legal and financial deadlocks in managerial operations. This study aims to scrutinize how to estimate predictions based on arbitrarily partitioned data configurations between two e-commerce companies without deeply jeopardizing their privacy. Privacy-preserving schemes are proposed to offer numerical or binary recommendations using item-based, trust-based, and naïve Bayesian classifier-based prediction algorithms on arbitrarily partitioned data. Along the study, how two parties ended up with cross partitioned data can provide CF services using hybrid CF algorithm is also investigated. It is shown that each proposed method does not intensely violate data owners' confidentiality. The proposed schemes are also investigated in terms of supplementary computation, communication, and storage overheads. Experimental trials are conducted using real data sets to show how the quality of the predictions improves due to collaboration and privacy measures affect accuracy. All appraisements demonstrate that the proposed solutions are preferable for estimating higher quality predictions efficiently on partitioned data while preserving data holders' privacy.