Distributed Differentially Private Matrix Factorization Based on ADMM.

Matrix factorization (MF) is an essential technique to implement intelligent recommender systems widely applied in industry. Privacy and efficiency are two essential issues concerning MF. We leverage two techniques, differential privacy (DP) and distributed computing, to address the two concerns, respectively. (1) Differentially private MF is still challenging since conventional strategies lead to significant error accumulation; we adopt the objective function perturbation technique to tackle such a challenge. (2) We adopt the alternating direction method of multipliers (ADMM) framework to parallelize the factorization to improve performance; to implement this parallelization, we adopt the effective matrix split method and introduce a novel integration strategy for distributed DP based on the post-processing theorem. We identify our work as distributed differentially private MF based on ADMM. The novelty of the work rests in that it is the first to successfully integrate the two innovative techniques to address both privacy and efficiency for MF. We establish the mathematical model and conduct experiments to validate the soundness of our idea. The experimental results based on industrial datasets show that the distributed differentially private MF algorithm provides scalable speedup performance within a limited precision loss while preserving user privacy.