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New framework enables efficient learning of distributions under local differential privacy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for learning univariate distributions under local differential privacy (LDP). This framework offers sample-efficient LDP learners for various distribution classes, including Gaussian Mixture Models (GMMs), and provides agnostic and computationally efficient guarantees. The work also includes a new lower bound for learning GMMs under LDP, improving upon existing bounds. AI

IMPACT This research could lead to more robust and privacy-preserving machine learning models, particularly in scenarios where data sensitivity is high.

RANK_REASON Academic paper on a new learning framework for differential privacy. [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 framework enables efficient learning of distributions under local differential privacy

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Academic paper on a new learning framework for differential privacy. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mohammad Afzali, Hassan Ashtiani, Sivan Sabato ·

    Efficient Agnostic Learning of Univariate Densities Under Local Differential Privacy

    arXiv:2610.21033v1 Announce Type: cross Abstract: We propose a general framework for non-interactive locally differentially private (LDP) learning of structured univariate distributions that can be approximated by piece-wise polynomials. This gives the first sample-efficient LDP …

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