PRIMO: Private Regression in Multiple Outcomes

• URL: http://arxiv.org/abs/2303.04195v2
• Date: Wed, 15 Jan 2025 15:06:56 GMT
• Title: PRIMO: Private Regression in Multiple Outcomes
• Authors: Seth Neel,
• Abstract summary: We introduce a new private regression setting we call Private Regression in Multiple Outcomes (PRIMO)<n>PRIMO is inspired by the common situation where a data analyst wants to perform a set of $l$ regressions while preserving privacy.<n>We find that even for values of $l$ far smaller than the theory would predict, our projection-based method improves the accuracy relative to the variant that doesn't use the projection.
• Score: 2.900810893770134
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a new private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired by the common situation where a data analyst wants to perform a set of $l$ regressions while preserving privacy, where the features $X$ are shared across all $l$ regressions, and each regression $i \in [l]$ has a different vector of outcomes $y_i$. Naively applying existing private linear regression techniques $l$ times leads to a $\sqrt{l}$ multiplicative increase in error over the standard linear regression setting. We apply a variety of techniques including sufficient statistics perturbation (SSP) and geometric projection-based methods to develop scalable algorithms that outperform this baseline across a range of parameter regimes. In particular, we obtain no dependence on l in the asymptotic error when $l$ is sufficiently large. Empirically, on the task of genomic risk prediction with multiple phenotypes we find that even for values of $l$ far smaller than the theory would predict, our projection-based method improves the accuracy relative to the variant that doesn't use the projection.

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