Gaussian mechanism is asymptotically optimal for high-dimensional DP additive noise; new Spherical Generalized Gamma family outperforms it and the ℓ2 mechanism in some low-dimensional cases with tight composition.
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Trade-off functions between two distributions are finitely testable if and only if their Neyman-Pearson rejection regions are attainable by a VC-class of sets.
Using the shuffle index, the authors formulate and solve an optimization problem for post-shuffle minimax-optimal unbiased mean estimation, yielding an asymptotically optimal mechanism whose privacy-utility tradeoff approaches the central Gaussian mechanism in the high-privacy regime.
DP4SQL enables customizable differentially private SQL for relational databases by supporting flexible policies for record existence, contents, partially public data, and varying protection levels across data parts.
A practical federated recommender allows user control over personalization versus diversity, with users showing preference for personalization in a live deployment.
CAPS provides an iterative differentially private synthesis method that outperforms one-shot baselines on authentic educational real-world data.
Sufficient conditions using the Wasserstein metric of order 1 are derived to calibrate Laplace noise for pufferfish privacy in multi-user aggregated queries, with relaxations for binary data that reduce noise while preserving indistinguishability.
CA-ADP adjusts differential privacy noise per mini-batch class composition to improve F-scores by 3.3-8.5% over standard DP on three fall-detection datasets while claiming formal (ε,δ) guarantees.
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Class-Aware Adaptive Differential Privacy in Deep Learning for Sensor-Based Fall Detection
CA-ADP adjusts differential privacy noise per mini-batch class composition to improve F-scores by 3.3-8.5% over standard DP on three fall-detection datasets while claiming formal (ε,δ) guarantees.