Privacy-preserving and yet Robust Collaborative Filtering Recommender as a Service

TL;DR

This paper introduces a novel system for privacy-preserving and robust collaborative filtering recommenders, addressing current shortcomings by proposing new cryptographic protocols and a user-centric recommendation approach, ensuring privacy and robustness.

Contribution

It presents a comprehensive system structure, a new security model, and two cryptographic protocols for privacy-preserving, robust collaborative filtering recommendations.

Findings

Protocols are efficient in experiments

New recommendation retrieval method enhances privacy

Addresses robustness issues in collaborative filtering

Abstract

Collaborative filtering recommenders provide effective personalization services at the cost of sacrificing the privacy of their end users. Due to the increasing concerns from the society and stricter privacy regulations, it is an urgent research challenge to design privacy-preserving and yet robust recommenders which offer recommendation services to privacy-aware users. Our analysis shows that existing solutions fall short in several aspects, including lacking attention to the precise output to end users and ignoring the correlated robustness issues. In this paper, we provide a general system structure for latent factor based collaborative filtering recommenders by formulating them into model training and prediction computing stages, and also describe a new security model. Aiming at pragmatic solutions, we first show how to construct privacy-preserving and yet robust model training stage based on existing solutions. Then, we propose two cryptographic protocols to realize a privacy-preserving prediction computing stage, depending on whether or not an extra proxy is involved. Different from standard Top-k recommendations, we alternatively let the end user retrieve the unrated items whose predictions are above a threshold, as a result of our privacy by design strategy. Experimental results show that our new protocols are quite efficient.