Mixed Differential Privacy in Computer Vision

22 March 2022

Papers citing "Mixed Differential Privacy in Computer Vision"

44 / 44 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 65 0 0 29 Mar 2025
Hyperbolic Safety-Aware Vision-Language Models Tobia Poppi Tejaswi Kasarla Pascal Mettes Lorenzo Baraldi Rita Cucchiara VLM MU 71 0 0 15 Mar 2025
Weights Shuffling for Improving DPSGD in Transformer-based Models Jungang Yang Zhe Ji Liyao Xiang 74 0 0 22 Jul 2024
Diffusion Soup: Model Merging for Text-to-Image Diffusion Models Benjamin Biggs Arjun Seshadri Yang Zou Achin Jain Aditya Golatkar Yusheng Xie Alessandro Achille Ashwin Swaminathan Stefano Soatto MoMe DiffM 54 10 0 12 Jun 2024
Clip Body and Tail Separately: High Probability Guarantees for DPSGD with Heavy Tails Haichao Sha Yang Cao Yong Liu Yuncheng Wu Ruixuan Liu Hong Chen 56 2 0 27 May 2024
LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models Juntaek Lim Youngeun Kwon Ranggi Hwang Kiwan Maeng Edward Suh Minsoo Rhu SyDa 38 0 0 12 Apr 2024
Advances in Differential Privacy and Differentially Private Machine Learning Saswat Das Subhankar Mishra 40 4 0 06 Apr 2024
CPR: Retrieval Augmented Generation for Copyright Protection Aditya Golatkar Alessandro Achille Luca Zancato Yu-Xiang Wang Ashwin Swaminathan Stefano Soatto DiffM 59 17 0 27 Mar 2024
Pre-training Differentially Private Models with Limited Public Data Zhiqi Bu Xinwei Zhang Mingyi Hong Sheng Zha George Karypis 79 3 0 28 Feb 2024
Privacy-Preserving Instructions for Aligning Large Language Models Da Yu Peter Kairouz Sewoong Oh Zheng Xu 47 18 0 21 Feb 2024
Oracle-Efficient Differentially Private Learning with Public Data Adam Block Mark Bun Rathin Desai Abhishek Shetty Steven Wu FedML 29 2 0 13 Feb 2024
On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift Pratiksha Thaker Amrith Rajagopal Setlur Zhiwei Steven Wu Virginia Smith 52 2 0 24 Dec 2023
Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection Tuan Hoang Santu Rana Sunil R. Gupta Svetha Venkatesh BDL MU 39 21 0 07 Dec 2023
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance Haichao Sha Ruixuan Liu Yi-xiao Liu Hong Chen 57 1 0 06 Dec 2023
Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models Samuele Poppi Tobia Poppi Federico Cocchi Marcella Cornia Lorenzo Baraldi Rita Cucchiara VLM 27 9 0 27 Nov 2023
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release Jie Fu Qingqing Ye Haibo Hu Zhili Chen Lulu Wang Kuncan Wang Xun Ran 34 14 0 23 Nov 2023
On the accuracy and efficiency of group-wise clipping in differentially private optimization Zhiqi Bu Ruixuan Liu Yu Wang Sheng Zha George Karypis VLM 40 4 0 30 Oct 2023
Private Learning with Public Features Walid Krichene Nicolas Mayoraz Steffen Rendle Shuang Song Abhradeep Thakurta Li Zhang 32 7 0 24 Oct 2023
Coupling public and private gradient provably helps optimization Ruixuan Liu Zhiqi Bu Yu Wang Sheng Zha George Karypis 46 2 0 02 Oct 2023
Private Matrix Factorization with Public Item Features Mihaela Curmei Walid Krichene Li Zhang Mukund Sundararajan 42 3 0 17 Sep 2023
Training Data Protection with Compositional Diffusion Models Aditya Golatkar Alessandro Achille A. Swaminathan Stefano Soatto DiffM 39 11 0 02 Aug 2023
Tangent Transformers for Composition, Privacy and Removal Tian Yu Liu Aditya Golatkar Stefano Soatto 35 8 0 16 Jul 2023
TMI! Finetuned Models Leak Private Information from their Pretraining Data John Abascal Stanley Wu Alina Oprea Jonathan R. Ullman 48 16 0 01 Jun 2023
DPFormer: Learning Differentially Private Transformer on Long-Tailed Data Youlong Ding Xueyang Wu Hongya Wang Weike Pan 60 0 0 28 May 2023
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zinan Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 43 19 0 23 May 2023
SAFE: Machine Unlearning With Shard Graphs Yonatan Dukler Benjamin Bowman Alessandro Achille Aditya Golatkar A. Swaminathan Stefano Soatto MU 33 22 0 25 Apr 2023
Practical Differentially Private and Byzantine-resilient Federated Learning Zihang Xiang Tianhao Wang Wanyu Lin Di Wang FedML 41 23 0 15 Apr 2023
AI Model Disgorgement: Methods and Choices Alessandro Achille Michael Kearns Carson Klingenberg Stefano Soatto MU 48 11 0 07 Apr 2023
Exploring the Benefits of Visual Prompting in Differential Privacy Yizhe Li Yu-Lin Tsai Xuebin Ren Chia-Mu Yu Pin-Yu Chen AAML VPVLM 29 18 0 22 Mar 2023
Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance Xin Gu Gautam Kamath Zhiwei Steven Wu 36 12 0 02 Mar 2023
Why Is Public Pretraining Necessary for Private Model Training? Arun Ganesh Mahdi Haghifam Milad Nasr Sewoong Oh Thomas Steinke Om Thakkar Abhradeep Thakurta Lun Wang 31 37 0 19 Feb 2023
On the Efficacy of Differentially Private Few-shot Image Classification Marlon Tobaben Aliaksandra Shysheya J. Bronskill Andrew Paverd Shruti Tople Santiago Zanella Béguelin Richard Turner Antti Honkela 46 11 0 02 Feb 2023
Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models Florian A. Hölzl Daniel Rueckert Georgios Kaissis 54 4 0 30 Jan 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 62 46 0 03 Dec 2022
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 67 7 0 24 Nov 2022
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing Jie Fu Zhili Chen Xinpeng Ling 37 0 0 14 Nov 2022
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Zhiqi Bu Yu Wang Sheng Zha George Karypis 51 69 0 14 Jun 2022
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent Da Yu Gautam Kamath Janardhan Kulkarni Tie-Yan Liu Jian Yin Huishuai Zhang 41 19 0 06 Jun 2022
Unlocking High-Accuracy Differentially Private Image Classification through Scale Soham De Leonard Berrada Jamie Hayes Samuel L. Smith Borja Balle 40 219 0 28 Apr 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 134 352 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 94 112 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 305 1,840 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 Schmidt Francis R. Bach 130 260 0 10 Dec 2012