The Price of Differential Privacy for Low-Rank Factorization

TL;DR

This paper develops the first differentially private algorithms for low-rank matrix factorization across various realistic settings, achieving optimal accuracy, space, and communication costs, with practical validation.

Contribution

It introduces novel differentially private algorithms for low-rank factorization in multiple settings, with optimal theoretical guarantees and empirical performance.

Findings

Algorithms achieve optimal accuracy and space complexity.

Distributed setting algorithms have communication costs independent of dimension.

Experimental results show superior performance over existing methods.

Abstract

In this paper, we study what price one has to pay to release {\em differentially private low-rank factorization} of a matrix. We consider various settings that are close to the real world applications of low-rank factorization: (i) the manner in which matrices are updated (row by row or in an arbitrary manner), (ii) whether matrices are distributed or not, and (iii) how the output is produced (once at the end of all updates, also known as {\em one-shot algorithms} or continually). Even though these settings are well studied without privacy, surprisingly, there are no private algorithm for these settings (except when a matrix is updated row by row). We present the first set of differentially private algorithms for all these settings. Our algorithms when private matrix is updated in an arbitrary manner promise differential privacy with respect to two stronger privacy guarantees than previously studied, use space and time {\em comparable} to the non-private algorithm, and achieve {\em optimal accuracy}. To complement our positive results, we also prove that the space required by our algorithms is optimal up to logarithmic factors. When data matrices are distributed over multiple servers, we give a non-interactive differentially private algorithm with communication cost independent of dimension. In concise, we give algorithms that incur optimal cost. We also perform experiments to verify that all our algorithms perform well in practice and outperform the best known algorithms until now for large range of parameters. We give experimental results for total approximation error and additive error for varying dimensions, $\alpha$ and $k$.