New method improves privacy loss accounting for AI algorithms

Researchers have developed a new method for efficiently calculating privacy loss in differentially private algorithms, particularly those involving subsampling and random allocation. This approach, detailed in a recent arXiv paper, offers tighter privacy parameters than previous analyses and simplifies privacy loss accounting by using a notion of Privacy Loss Distribution (PLD) realization. The new tools extend accurate accounting to subsampling, which previously required mechanism-specific analysis, and demonstrate that random allocation performs at least as well as Poisson subsampling, especially for training via DP-SGD. AI

IMPACT This research could lead to more robust privacy guarantees in AI models, particularly those trained with differential privacy techniques.

RANK_REASON The cluster contains a research paper detailing a new method for privacy loss accounting in machine learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Asi et al.
• Choquette-Choo et al.
• Chua et al.
• DP-SGD
• Feldman & Shenfeld
• Hugging Face
• Moshe Shenfeld

• paper
• safety

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