Geometry of Sensitivity: Twice Sampling and Hybrid Clipping in Differential Privacy with Optimal Gaussian Noise and Application to Deep Learning

Geometry of Sensitivity: Twice Sampling and Hybrid Clipping in Differential Privacy with Optimal Gaussian Noise and Application to Deep Learning

We study the fundamental problem of the construction of optimal randomization in Differential Privacy (DP). Depending on the clipping strategy or additional properties of the processing function, the corresponding sensitivity set theoretically determines the necessary randomization to produce the re...

Full description

Bibliographic Details
Main Authors: Xiao, Hanshen, Wan, Jun, Devadas, Srinivas
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:English
Published: ACM|Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security 2023
Online Access:https://hdl.handle.net/1721.1/153139

Internet

https://hdl.handle.net/1721.1/153139

Similar Items