Counting queries on quantum data reduce to amplitude measurements, enabling differentially private algorithms via repeated measurements or amplitude estimation with proven sensitivity bounds.
Calibrating noise to sensitivity in private data analysis
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For two-layer KANs trained with gradient descent under logistic loss and NTK-separable assumption, polylogarithmic width suffices for 1/T optimization and 1/n generalization rates, while differential privacy requires the same width and yields √d/(nε) utility.
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Answering Counting Queries with Differential Privacy on a Quantum Computer
Counting queries on quantum data reduce to amplitude measurements, enabling differentially private algorithms via repeated measurements or amplitude estimation with proven sensitivity bounds.
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Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks
For two-layer KANs trained with gradient descent under logistic loss and NTK-separable assumption, polylogarithmic width suffices for 1/T optimization and 1/n generalization rates, while differential privacy requires the same width and yields √d/(nε) utility.
- Privacy, Prediction, and Allocation