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洞察 - Differential privacy optimization
Optimal Noise Mechanism for Differentially Private Queries in Discrete Finite Sets
The paper proposes a novel framework for designing an optimal noise probability mass function (PMF) tailored to discrete and finite query sets, aiming to satisfy privacy constraints while minimizing query distortion.
Optimizing High-Dimensional Differentially Private Linear Models: A Comprehensive Review and Empirical Evaluation
This paper provides a comprehensive review of optimization techniques for high-dimensional differentially private linear models, including linear and logistic regression. The authors implement and empirically evaluate all the reviewed methods, providing insights on their strengths, weaknesses, and performance across various datasets.
