Counting queries on quantum data reduce to amplitude measurements, enabling differentially private algorithms via repeated measurements or amplitude estimation with proven sensitivity bounds.
Hdmm: Optimizing error of high-dimensional statistical queries under differential privacy
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
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.
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.
citing papers explorer
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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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ResidualPlanner+: a scalable matrix mechanism for marginals and beyond
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.
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Aim High, Stay Private: Differentially Private Synthetic Data Enables Public Release of Behavioral Health Information with High Utility
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.