Fast and Memory Efficient Differentially Private-SGD via JL Projections

5 February 2021

Papers citing "Fast and Memory Efficient Differentially Private-SGD via JL Projections"

7 / 7 papers shown

Title
On Differential Privacy and Adaptive Data Analysis with Bounded Space Itai Dinur Uri Stemmer David P. Woodruff Samson Zhou 16 12 0 11 Feb 2023
Private Ad Modeling with DP-SGD Carson E. Denison Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Krishnagiri Narra Amer Sinha A. Varadarajan Chiyuan Zhang 27 14 0 21 Nov 2022
How to Make Your Approximation Algorithm Private: A Black-Box Differentially-Private Transformation for Tunable Approximation Algorithms of Functions with Low Sensitivity Jeremiah Blocki Elena Grigorescu Tamalika Mukherjee Samson Zhou 61 11 0 07 Oct 2022
Differentially Private Optimization on Large Model at Small Cost Zhiqi Bu Yu-Xiang Wang Sheng Zha George Karypis 30 52 0 30 Sep 2022
Faster Privacy Accounting via Evolving Discretization Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi 55 14 0 10 Jul 2022
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions Vadym Doroshenko Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi 16 40 0 10 Jul 2022
Efficient Per-Example Gradient Computations Ian Goodfellow 186 74 0 07 Oct 2015