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New papers explore differential privacy in Gaussian Processes and ML reporting

By PulseAugur Editorial · [3 sources] ·

Two recent arXiv papers explore differential privacy in machine learning, focusing on Gaussian processes and reporting mechanisms. The first paper details how the intrinsic randomness of Gaussian Process posterior sampling can provide differential privacy guarantees, with bounds dependent on regularization and posterior variance. The second paper advocates for using non-asymptotic Gaussian Differential Privacy (GDP) as a more accurate way to communicate privacy guarantees for algorithms like DP-SGD, citing its ability to capture the full privacy profile with minimal error. AI

IMPACT These papers contribute to the theoretical understanding of privacy in machine learning, potentially influencing how privacy guarantees are developed and communicated for future AI systems.

RANK_REASON Two academic papers published on arXiv discussing differential privacy in machine learning contexts.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New papers explore differential privacy in Gaussian Processes and ML reporting

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COVERAGE [3]

  1. arXiv stat.ML TIER_1 English(EN) · Tomasz Maciazek ·

    Differential Privacy of Gaussian Process Posterior Sampling

    arXiv:2606.17995v1 Announce Type: new Abstract: We study the privacy of releasing posterior sample paths from a Gaussian process (GP) when the entire training set including covariates and responses is private. Unlike standard differential-privacy (DP) mechanisms that add external…

  2. arXiv stat.ML TIER_1 English(EN) · Juan Felipe Gomez, Bogdan Kulynych, Georgios Kaissis, Flavio P. Calmon, Jamie Hayes, Borja Balle, Antti Honkela ·

    Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning

    arXiv:2503.10945v3 Announce Type: replace-cross Abstract: Current practices for reporting differential privacy (DP) guarantees for machine learning (ML) algorithms such as DP-SGD provide an incomplete and potentially misleading picture. For instance, if only a single $(\varepsilo…

  3. arXiv stat.ML TIER_1 English(EN) · Tomasz Maciazek ·

    Differential Privacy of Gaussian Process Posterior Sampling

    We study the privacy of releasing posterior sample paths from a Gaussian process (GP) when the entire training set including covariates and responses is private. Unlike standard differential-privacy (DP) mechanisms that add external noise, posterior sampling is random by construc…

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