Private Alternating Least Squares: (Nearly) OptimalPrivacy/Utility Trade-off for Matrix Completion

We study the problem of differentially private (DP) matrix completion under user-level privacy. We design an $(\epsilon,\delta)$-joint differentially private variant of the popular Alternating-Least-Squares (ALS) method that achieves: i) (nearly) optimal sample complexity for matrix completion (in terms of number of items, users), and ii) best known privacy/utility trade-off both theoretically, as well as on benchmark data sets. In particular, despite non-convexity of low-rank matrix completion and ALS, we provide the first global convergence analysis of ALS with {\em noise} introduced to ensure DP. For $n$ being the number of users and $m$ being the number of items in the rating matrix, our analysis requires only about $\log (n+m)$ samples per user (ignoring rank, condition number factors) and obtains a sample complexity of $n=\tilde\Omega(m/(\sqrt{\zeta}\cdot \epsilon))$ to ensure relative Frobenius norm error of $\zeta$. This improves significantly on the previous state of the result of $n=\tilde\Omega\left(m^{5/4}/(\zeta^{5}\epsilon)\right)$ for the private-FW method by ~\citet{jain2018differentially}. Furthermore, we extensively validate our method on synthetic and benchmark data sets (MovieLens 10mi, MovieLens 20mi), and observe that private ALS only suffers a 6 percentage drop in accuracy when compared to the non-private baseline for $\epsilon\leq 10$. Furthermore, compared to prior work of~\cite{jain2018differentially}, it is at least better by 10 percentage for all choice of the privacy parameters.

• Machine Intelligence

• Security, Privacy and Abuse Prevention