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New research explores privacy limits in sparse linear models

By PulseAugur Editorial · [1 sources] ·

This paper investigates the limitations of sparse linear regression models when employing shuffled differential privacy (SDP). Researchers established a lower bound for non-interactive SDP protocols, indicating an inherent dimensionality barrier. They also proposed a non-interactive algorithm that approaches this bound. For interactive user-level settings, a two-round algorithm was developed that recovers the support and estimates parameters with improved accuracy, showing near-optimality against a new lower bound for user-level differential privacy. AI

IMPACT This research contributes to understanding the trade-offs between privacy and accuracy in statistical modeling, relevant for developing more robust and secure machine learning systems.

RANK_REASON The cluster contains an academic paper discussing theoretical limits and algorithms for differential privacy in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research explores privacy limits in sparse linear models

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The cluster contains an academic paper discussing theoretical limits and algorithms for differential privacy in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yan Zhang, Jiong Tuo, Meng Ding, Yuheng Ma, Di Wang ·

    On the Limits of Sparse Linear Model with Shuffled Differential Privacy

    arXiv:2610.32822v2 Announce Type: new Abstract: Sparse linear regression is a fundamental problem in high-dimensional statistics, and its private counterparts leave a large gap between the central and the local model of differential privacy. Shuffled differential privacy (SDP) si…

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