Deep Learning with Differential Privacy

1 July 2016

Papers citing "Deep Learning with Differential Privacy"

50 / 1,123 papers shown

Title
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments Kaixiang Zhao Lincan Li Kaize Ding Neil Zhenqiang Gong Yue Zhao Yushun Dong AAML 52 0 0 22 Feb 2025
RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models Tanqiu Jiang Changjiang Li Fenglong Ma Ting Wang 72 0 0 18 Feb 2025
Does Training with Synthetic Data Truly Protect Privacy? Yunpeng Zhao Jie Zhang 82 0 0 18 Feb 2025
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning Saber Malekmohammadi Yaoliang Yu Yang Cao FedML 88 6 0 17 Feb 2025
Privacy-Preserving Dataset Combination Keren Fuentes Mimee Xu Irene Chen 48 0 0 09 Feb 2025
Learning with Differentially Private (Sliced) Wasserstein Gradients David Rodríguez-Vítores Clément Lalanne Jean-Michel Loubes FedML 48 0 0 03 Feb 2025
On the Impact of Noise in Differentially Private Text Rewriting Stephen Meisenbacher Maulik Chevli Florian Matthes 63 0 0 31 Jan 2025
Differential Privacy with Higher Utility by Exploiting Coordinate-wise Disparity: Laplace Mechanism Can Beat Gaussian in High Dimensions Gokularam Muthukrishnan Sheetal Kalyani 87 0 0 28 Jan 2025
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling Kaiyuan Zhang Siyuan Cheng Guangyu Shen Bruno Ribeiro Shengwei An Pin-Yu Chen Xinming Zhang Ninghui Li 160 1 0 28 Jan 2025
A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning Abdulkadir Korkmaz Praveen Rao FedML 42 0 0 22 Jan 2025
TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data P. Tiwald Ivona Krchova Andrey Sidorenko Mariana Vargas-Vieyra Mario Scriminaci Michael Platzer 54 1 0 21 Jan 2025
Episodic memory in AI agents poses risks that should be studied and mitigated Chad DeChant 70 2 0 20 Jan 2025
Flash: A Hybrid Private Inference Protocol for Deep CNNs with High Accuracy and Low Latency on CPU H. Roh Jinsu Yeo Yeongil Ko Gu-Yeon Wei David Brooks Woo-Seok Choi 89 2 0 20 Jan 2025
GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Jiadong Lou Xu Yuan Rui Zhang Xingliang Yuan Neil Gong N. Tzeng AAML 50 1 0 19 Jan 2025
Understanding and Mitigating Membership Inference Risks of Neural Ordinary Differential Equations Sanghyun Hong Fan Wu A. Gruber Kookjin Lee 47 0 0 12 Jan 2025
Structure-Preference Enabled Graph Embedding Generation under Differential Privacy Sen Zhang Qingqing Ye Haibo Hu 54 0 0 08 Jan 2025
Lossless Privacy-Preserving Aggregation for Decentralized Federated Learning Xiaoye Miao Bin Li Yangyang Wu Meng Xi Xinkui Zhao 36 0 0 08 Jan 2025
Disentangling data distribution for Federated Learning Xinyuan Zhao Hanlin Gu Lixin Fan Qiang Yang Yuxing Han OOD FedML 49 0 0 31 Dec 2024
Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Supriya Manna Niladri Sett 192 0 0 30 Dec 2024
Balls-and-Bins Sampling for DP-SGD Lynn Chua Badih Ghazi Charlie Harrison Ethan Leeman Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 88 4 0 21 Dec 2024
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators Tejumade Afonja Hui-Po Wang Raouf Kerkouche Mario Fritz SyDa 121 2 0 03 Dec 2024
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Sangyeon Yoon Wonje Jeung Albert No 93 0 0 02 Dec 2024
Noise-Aware Differentially Private Variational Inference Talal Alrawajfeh Joonas Jälkö Antti Honkela 35 0 0 25 Oct 2024
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies Liwen Wang Sheng Chen Linnan Jiang Shu Pan Runze Cai Sen Yang Fei Yang 52 3 0 24 Oct 2024
From Gradient Clipping to Normalization for Heavy Tailed SGD Florian Hübler Ilyas Fatkhullin Niao He 45 5 0 17 Oct 2024
Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond Shanshan Han 87 1 0 09 Oct 2024
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 42 7 0 08 Oct 2024
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD Thomas Steinke Milad Nasr Arun Ganesh Borja Balle Christopher A. Choquette-Choo Matthew Jagielski Jamie Hayes Abhradeep Thakurta Adam Smith Andreas Terzis 34 7 0 08 Oct 2024
Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy Shuangqing Xu Yifeng Zheng Zhongyun Hua FedML 21 2 0 04 Oct 2024
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang Zhiqi Bu Borja Balle Mingyi Hong Meisam Razaviyayn Vahab Mirrokni 78 2 0 04 Oct 2024
Undesirable Memorization in Large Language Models: A Survey Ali Satvaty Suzan Verberne Fatih Turkmen ELM PILM 94 7 0 03 Oct 2024
PFGuard: A Generative Framework with Privacy and Fairness Safeguards Soyeon Kim Yuji Roh Geon Heo Steven Euijong Whang 39 0 0 03 Oct 2024
Differentially Private Parameter-Efficient Fine-tuning for Large ASR Models Hongbin Liu Lun Wang Om Thakkar Abhradeep Thakurta Arun Narayanan 37 0 0 02 Oct 2024
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy Kristian Schwethelm Johannes Kaiser Jonas Kuntzer Mehmet Yigitsoy Daniel Rueckert Georgios Kaissis 42 0 0 01 Oct 2024
On the Implicit Relation Between Low-Rank Adaptation and Differential Privacy Saber Malekmohammadi G. Farnadi 32 2 0 26 Sep 2024
SDBA: A Stealthy and Long-Lasting Durable Backdoor Attack in Federated Learning Minyeong Choe Cheolhee Park Changho Seo Hyunil Kim SILM AAML FedML 36 0 0 23 Sep 2024
Training Large ASR Encoders with Differential Privacy Geeticka Chauhan Steve Chien Om Thakkar Abhradeep Thakurta Arun Narayanan 33 1 0 21 Sep 2024
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation Bochao Liu Jianghu Lu Pengju Wang Junjie Zhang Dan Zeng Zhenxing Qian Shiming Ge 30 1 0 19 Sep 2024
Score Forgetting Distillation: A Swift, Data-Free Method for Machine Unlearning in Diffusion Models Tianqi Chen Shujian Zhang Mingyuan Zhou DiffM 83 4 0 17 Sep 2024
Rewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions Siqiao Mu Diego Klabjan MU 50 3 0 15 Sep 2024
Generated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data Atilla Akkus Mingjie Li Junjie Chu Junjie Chu Michael Backes Sinem Sav Sinem Sav SILM SyDa 48 1 0 12 Sep 2024
NetDPSyn: Synthesizing Network Traces under Differential Privacy Danyu Sun Joann Qiongna Chen Chen Gong Tianhao Wang Zhou Li 64 1 0 08 Sep 2024
Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks Tran Viet Khoa Mohammad Abu Alsheikh Yibeltal Alem D. Hoang FedML 34 3 0 08 Sep 2024
Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation Shiming Ge Bochao Liu Pengju Wang Yong Li Dan Zeng FedML 44 9 0 04 Sep 2024
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates Puning Zhao Xiaogang Xu Zhe Liu Chong Wang Rongfei Fan Qingming Li 50 1 0 19 Aug 2024
A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs H. B. McMahan Zheng Xu Yanxiang Zhang FedML 58 6 0 16 Aug 2024
Better Gaussian Mechanism using Correlated Noise Christian Janos Lebeda 44 2 0 13 Aug 2024
Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey V. T. Truong Luan Ba Dang Long Bao Le DiffM MedIm 58 17 0 06 Aug 2024
Differentially Private Block-wise Gradient Shuffle for Deep Learning Zilong Zhang FedML 45 0 0 31 Jul 2024
Private Collaborative Edge Inference via Over-the-Air Computation Selim F. Yilmaz Burak Hasircioglu Li Qiao Deniz Gunduz FedML 67 1 0 30 Jul 2024