
The author explores the optimal mechanisms for differential privacy in various statistical problems using K-norm and elliptic Gaussian noise.


coremsg

Private-Efficient-and-Optimal-K-Norm-and-Elliptic-Gaussian-Noise-For-Sum-Count-and-Vote-Analysis


Private, Efficient, and Optimal K-Norm and Elliptic Gaussian Noise For Sum, Count, and Vote Analysis



R1SMG mechanism achieves (ε,δ)-DP with lower noise magnitude.



Revisiting the Gaussian Mechanism for Differential Privacy: Less is More



Efficiently implementing optimal K-norm mechanisms for Sum, Count, and Vote with Elliptic Gaussian Noise.



Private, Efficient, and Optimal K-Norm and Elliptic Gaussian Noise for Sum, Count, and Vote
