Counting queries on quantum data reduce to amplitude measurements, enabling differentially private algorithms via repeated measurements or amplitude estimation with proven sensitivity bounds.
Hdmm: Opti- mizing error of high-dimensional statistical queries unde r differential privacy
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ResidualPlanner provides an optimal scalable matrix mechanism for Gaussian noise on marginal queries that optimizes convex loss functions of variances, with ResidualPlanner+ extending support to combined marginal and range/prefix-sum workloads while outperforming HDMM.
PACE-GGM selects poorly approximated covariance entries, measures them privately, and reconstructs the full matrix with a maximum-entropy objective to produce a Gaussian graphical model, yielding lower estimation error than uniform perturbation.
The authors apply the Adaptive Iterative Mechanism to create differentially private synthetic data from the LEMURS wearable and survey dataset and show that epsilon=5 retains useful predictive performance for downstream tasks.
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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.