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Differentially Private Robust Low-Rank Approximation
Raman Arora · Vladimir braverman · Jalaj Upadhyay

Wed Dec 05 07:45 AM -- 09:45 AM (PST) @ Room 517 AB #145
In this paper, we study the following robust low-rank matrix approximation problem: given a matrix $A \in \R^{n \times d}$, find a rank-$k$ matrix $B$, while satisfying differential privacy, such that $ \norm{ A - B }_p \leq \alpha \mathsf{OPT}_k(A) + \tau,$ where $\norm{ M }_p$ is the entry-wise $\ell_p$-norm and $\mathsf{OPT}_k(A):=\min_{\mathsf{rank}(X) \leq k} \norm{ A - X}_p$. It is well known that low-rank approximation w.r.t. entrywise $\ell_p$-norm, for $p \in [1,2)$, yields robustness to gross outliers in the data. We propose an algorithm that guarantees $\alpha=\widetilde{O}(k^2), \tau=\widetilde{O}(k^2(n+kd)/\varepsilon)$, runs in $\widetilde O((n+d)\poly~k)$ time and uses $O(k(n+d)\log k)$ space. We study extensions to the streaming setting where entries of the matrix arrive in an arbitrary order and output is produced at the very end or continually. We also study the related problem of differentially private robust principal component analysis (PCA), wherein we return a rank-$k$ projection matrix $\Pi$ such that $\norm{ A - A \Pi }_p \leq \alpha \mathsf{OPT}_k(A) + \tau.$

Author Information

Raman Arora (Johns Hopkins University)
Vladimir braverman (Johns Hopkins University)
Jalaj Upadhyay (Johns Hopkins University)

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