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Choquette-Choo et al.
Choquette-Choo et al.
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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 …
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New sampling-free privacy accounting for differentially private models
Researchers have developed new sampling-free methods to accurately measure privacy guarantees in differentially private model training. Their approach utilizes Rényi divergence and conditional composition to provide str…