Mixed Differential Privacy in Computer Vision

Mixed Differential Privacy in Computer Vision

22 March 2022

Aditya Golatkar

Alessandro Achille

Papers citing "Mixed Differential Privacy in Computer Vision"

14 / 14 papers shown

Title
NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Rob Romijnders Stefanos Laskaridis Ali Shahin Shamsabadi Hamed Haddadi 69 0 0 25 Apr 2025
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation Chengkun Wei Weixian Li Chen Gong Wenzhi Chen 58 0 0 29 Mar 2025
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance Haichao Sha Ruixuan Liu Yi-xiao Liu Hong Chen 52 1 0 06 Dec 2023
Private Matrix Factorization with Public Item Features Mihaela Curmei Walid Krichene Li Zhang Mukund Sundararajan 37 3 0 17 Sep 2023
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zinan Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 38 19 0 23 May 2023
AI Model Disgorgement: Methods and Choices Alessandro Achille Michael Kearns Carson Klingenberg Stefano Soatto MU 36 11 0 07 Apr 2023
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping Jiyan He Xuechen Li Da Yu Huishuai Zhang Janardhan Kulkarni Y. Lee A. Backurs Nenghai Yu Jiang Bian 30 46 0 03 Dec 2022
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 58 7 0 24 Nov 2022
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing Jie Fu Zhili Chen Xinpeng Ling 27 0 0 14 Nov 2022
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Zhiqi Bu Yu-Xiang Wang Sheng Zha George Karypis 27 69 0 14 Jun 2022
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 134 350 0 13 Oct 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 94 111 0 25 Feb 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 290 1,831 0 14 Dec 2020
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method Simon Lacoste-Julien Mark W. Schmidt Francis R. Bach 128 259 0 10 Dec 2012