Privacy-Preserving Trust-based Recommendations on Vertically Distributed Data

Abstract

Providing recommendations on trusts between entities is receiving increasing attention lately. Customers may prefer different online vendors for shopping. Thus, their preferences about various products might be distributed among multiple parties. To provide more accurate and reliable referrals, such companies might decide to collaborate. Due to privacy, legal, and financial reasons, however, they do not want to work jointly. In this paper, we propose a method for providing trust-based predictions on vertically distributed data while preserving data owners' confidentiality. We analyze our scheme in terms of privacy and performance. We also perform experiments for accuracy analysis. Our analyses show that our scheme is secure and able to provide accurate and reliable predictions efficiently.