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New algorithms achieve optimal person-level differential privacy for discrete distributions

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new algorithms for learning discrete distributions while adhering to person-level differential privacy in a batched setting. The algorithms achieve near-optimal guarantees under both pure and approximate differential privacy, improving upon previous methods. A key innovation is a novel degree-6 sum-of-squares estimator that utilizes semidefinite programming and Grothendieck's inequality to ensure privacy. AI

IMPACT Advances theoretical understanding and practical methods for privacy-preserving machine learning, potentially enabling more sensitive data analysis.

RANK_REASON The cluster consists of an academic paper discussing novel algorithms and theoretical bounds for a specific machine learning problem.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New algorithms achieve optimal person-level differential privacy for discrete distributions

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Gautam Kamath, Mahbod Majid, Ankur Moitra, Argyris Mouzakis, Jonathan Ullman ·

    Efficient and Optimal Learning of Discrete Distributions with Person-Level Privacy

    arXiv:2610.31612v1 Announce Type: new Abstract: We study learning a discrete distribution under person-level differential privacy in the batched setting. Given $n$ users, each contributing $m$ i.i.d. samples from an unknown distribution $\mu$ over $[k]$, we seek an estimate of $\…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Efficient and Optimal Learning of Discrete Distributions with Person-Level Privacy

    We study learning a discrete distribution under person-level differential privacy in the batched setting. Given $n$ users, each contributing $m$ i.i.d. samples from an unknown distribution $μ$ over $[k]$, we seek an estimate of $μ$ with total-variation error $α$. We give polynomi…

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