Optimized Tradeoffs for Private Prediction with Majority Ensembling
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913588242808832 |
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| author | Jiang, Shuli Qiuyi Zhang Joshi, Gauri |
| author_facet | Jiang, Shuli Qiuyi Zhang Joshi, Gauri |
| contents | We study a classical problem in private prediction, the problem of computing an $(mε, δ)$-differentially private majority of $K$ $(ε, Δ)$-differentially private algorithms for $1 \leq m \leq K$ and $1 > δ\geq Δ\geq 0$. Standard methods such as subsampling or randomized response are widely used, but do they provide optimal privacy-utility tradeoffs? To answer this, we introduce the Data-dependent Randomized Response Majority (DaRRM) algorithm. It is parameterized by a data-dependent noise function $γ$, and enables efficient utility optimization over the class of all private algorithms, encompassing those standard methods. We show that maximizing the utility of an $(mε, δ)$-private majority algorithm can be computed tractably through an optimization problem for any $m \leq K$ by a novel structural result that reduces the infinitely many privacy constraints into a polynomial set. In some settings, we show that DaRRM provably enjoys a privacy gain of a factor of 2 over common baselines, with fixed utility. Lastly, we demonstrate the strong empirical effectiveness of our first-of-its-kind privacy-constrained utility optimization for ensembling labels for private prediction from private teachers in image classification. Notably, our DaRRM framework with an optimized $γ$ exhibits substantial utility gains when compared against several baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17965 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimized Tradeoffs for Private Prediction with Majority Ensembling Jiang, Shuli Qiuyi Zhang Joshi, Gauri Machine Learning Cryptography and Security We study a classical problem in private prediction, the problem of computing an $(mε, δ)$-differentially private majority of $K$ $(ε, Δ)$-differentially private algorithms for $1 \leq m \leq K$ and $1 > δ\geq Δ\geq 0$. Standard methods such as subsampling or randomized response are widely used, but do they provide optimal privacy-utility tradeoffs? To answer this, we introduce the Data-dependent Randomized Response Majority (DaRRM) algorithm. It is parameterized by a data-dependent noise function $γ$, and enables efficient utility optimization over the class of all private algorithms, encompassing those standard methods. We show that maximizing the utility of an $(mε, δ)$-private majority algorithm can be computed tractably through an optimization problem for any $m \leq K$ by a novel structural result that reduces the infinitely many privacy constraints into a polynomial set. In some settings, we show that DaRRM provably enjoys a privacy gain of a factor of 2 over common baselines, with fixed utility. Lastly, we demonstrate the strong empirical effectiveness of our first-of-its-kind privacy-constrained utility optimization for ensembling labels for private prediction from private teachers in image classification. Notably, our DaRRM framework with an optimized $γ$ exhibits substantial utility gains when compared against several baselines. |
| title | Optimized Tradeoffs for Private Prediction with Majority Ensembling |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2411.17965 |