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洞見 - Differential Privacy Mechanisms
Private, Efficient, and Optimal K-Norm and Elliptic Gaussian Noise For Sum, Count, and Vote Analysis
The author explores the optimal mechanisms for differential privacy in various statistical problems using K-norm and elliptic Gaussian noise.
Revisiting the Gaussian Mechanism for Differential Privacy: Less is More
R1SMG mechanism achieves (ε,δ)-DP with lower noise magnitude.
Private, Efficient, and Optimal K-Norm and Elliptic Gaussian Noise for Sum, Count, and Vote
Efficiently implementing optimal K-norm mechanisms for Sum, Count, and Vote with Elliptic Gaussian Noise.
