Secure Two-Party Rank Correlation Computations for Recommender Systems

Recommendation systems are active information filtering systems that consist of a processor that can provide recommendations to requesting users (based on the personal ratings that were submitted by all users). In order to produce accurate and personalized recommendations, databases from different agencies can be merged together as a central database. However, due to competition and the possibility of disclosing business strategies, some agencies might not want to disclose the rating information of their customers. In this paper, we propose three secure protocols to compute rank correlation coefficients (Spearman's Rho and Kendall's Tau) for recommender systems. We utilize a semantically secure homomorphic cryptosystem and a ciphertext comparison approach in our protocol design.