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Deep Learning with Differential Privacy
v1v2 (latest)

Deep Learning with Differential Privacy

1 July 2016
Martín Abadi
Andy Chu
Ian Goodfellow
H. B. McMahan
Ilya Mironov
Kunal Talwar
Li Zhang
    FedMLSyDa
ArXiv (abs)PDFHTML

Papers citing "Deep Learning with Differential Privacy"

50 / 2,789 papers shown
Title
Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient
  Similarity to Reduce Communication in Distributed and Federated Learning
Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning
Dmitry Bylinkin
Kirill Degtyarev
Aleksandr Beznosikov
FedML
84
0
0
22 Sep 2024
Training Large ASR Encoders with Differential Privacy
Training Large ASR Encoders with Differential Privacy
Geeticka Chauhan
Steve Chien
Om Thakkar
Abhradeep Thakurta
Arun Narayanan
100
1
0
21 Sep 2024
Unlocking Memorization in Large Language Models with Dynamic Soft
  Prompting
Unlocking Memorization in Large Language Models with Dynamic Soft Prompting
Zhepeng Wang
Runxue Bao
Yawen Wu
Jackson Taylor
Cao Xiao
Feng Zheng
Weiwen Jiang
Shangqian Gao
Yanfu Zhang
PILM
75
11
0
20 Sep 2024
Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG
  Representative Learning
Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learning
Xiaowen Fu
Bingxin Wang
Xinzhou Guo
Guoqing Liu
Yang Xiang
89
2
0
20 Sep 2024
CorBin-FL: A Differentially Private Federated Learning Mechanism using
  Common Randomness
CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness
Hojat Allah Salehi
Md Jueal Mia
S. Sandeep Pradhan
M. Hadi Amini
Farhad Shirani
FedML
119
0
0
20 Sep 2024
Data Poisoning and Leakage Analysis in Federated Learning
Data Poisoning and Leakage Analysis in Federated Learning
Wenqi Wei
Tiansheng Huang
Zachary Yahn
Anoop Singhal
Margaret Loper
Ling Liu
FedMLSILM
59
0
0
19 Sep 2024
Privacy-Preserving Student Learning with Differentially Private
  Data-Free Distillation
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation
Bochao Liu
Jianghu Lu
Pengju Wang
Junjie Zhang
Dan Zeng
Zhenxing Qian
Shiming Ge
74
1
0
19 Sep 2024
Score Forgetting Distillation: A Swift, Data-Free Method for Machine Unlearning in Diffusion Models
Score Forgetting Distillation: A Swift, Data-Free Method for Machine Unlearning in Diffusion Models
Tianqi Chen
Shujian Zhang
Mingyuan Zhou
DiffM
209
7
0
17 Sep 2024
Rewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions
Rewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions
Siqiao Mu
Diego Klabjan
MU
164
6
0
15 Sep 2024
A Statistical Viewpoint on Differential Privacy: Hypothesis Testing,
  Representation and Blackwell's Theorem
A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem
Weijie J. Su
99
2
0
14 Sep 2024
Generated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data
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
SILMSyDa
140
4
0
12 Sep 2024
Controllable Synthetic Clinical Note Generation with Privacy Guarantees
Controllable Synthetic Clinical Note Generation with Privacy Guarantees
Tal Baumel
Andre Manoel
Daniel Jones
Shize Su
Huseyin A. Inan
Aaron
Bornstein
Robert Sim
35
1
0
12 Sep 2024
CipherDM: Secure Three-Party Inference for Diffusion Model Sampling
CipherDM: Secure Three-Party Inference for Diffusion Model Sampling
Xin Zhao
Xiaojun Chen
Xinyu Chen
He Li
Tingyu Fan
Zhendong Zhao
88
1
0
09 Sep 2024
NetDPSyn: Synthesizing Network Traces under Differential Privacy
NetDPSyn: Synthesizing Network Traces under Differential Privacy
Danyu Sun
Joann Qiongna Chen
Chen Gong
Tianhao Wang
Zhou Li
102
1
0
08 Sep 2024
Balancing Security and Accuracy: A Novel Federated Learning Approach for
  Cyberattack Detection in Blockchain Networks
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
68
3
0
08 Sep 2024
Benchmarking Estimators for Natural Experiments: A Novel Dataset and a
  Doubly Robust Algorithm
Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm
R. Teal Witter
Christopher Musco
88
0
0
06 Sep 2024
Learning Privacy-Preserving Student Networks via
  Discriminative-Generative Distillation
Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation
Shiming Ge
Bochao Liu
Pengju Wang
Yong Li
Dan Zeng
FedML
106
11
0
04 Sep 2024
$S^2$NeRF: Privacy-preserving Training Framework for NeRF
S2S^2S2NeRF: Privacy-preserving Training Framework for NeRF
Bokang Zhang
Yanglin Zhang
Zhikun Zhang
Jinglan Yang
Lingying Huang
Junfeng Wu
89
2
0
03 Sep 2024
Unveiling the Vulnerability of Private Fine-Tuning in Split-Based
  Frameworks for Large Language Models: A Bidirectionally Enhanced Attack
Unveiling the Vulnerability of Private Fine-Tuning in Split-Based Frameworks for Large Language Models: A Bidirectionally Enhanced Attack
Guanzhong Chen
Zhenghan Qin
Mingxin Yang
Yajie Zhou
Tao Fan
Tianyu Du
Zenglin Xu
AAML
135
7
0
02 Sep 2024
Is Difficulty Calibration All We Need? Towards More Practical Membership
  Inference Attacks
Is Difficulty Calibration All We Need? Towards More Practical Membership Inference Attacks
Yu He
Boheng Li
Yao Wang
Mengda Yang
Juan Wang
Hongxin Hu
Xingyu Zhao
124
12
0
31 Aug 2024
Analyzing Inference Privacy Risks Through Gradients in Machine Learning
Analyzing Inference Privacy Risks Through Gradients in Machine Learning
Zhuohang Li
Andrew Lowy
Jing Liu
T. Koike-Akino
K. Parsons
Bradley Malin
Ye Wang
FedML
92
2
0
29 Aug 2024
Revisit Micro-batch Clipping: Adaptive Data Pruning via Gradient
  Manipulation
Revisit Micro-batch Clipping: Adaptive Data Pruning via Gradient Manipulation
Lun Wang
68
1
0
29 Aug 2024
VFLIP: A Backdoor Defense for Vertical Federated Learning via
  Identification and Purification
VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification
Yungi Cho
Woorim Han
Miseon Yu
Younghan Lee
Ho Bae
Y. Paek
AAMLFedML
78
2
0
28 Aug 2024
Learning Differentially Private Diffusion Models via Stochastic
  Adversarial Distillation
Learning Differentially Private Diffusion Models via Stochastic Adversarial Distillation
Bochao Liu
Pengju Wang
Shiming Ge
87
1
0
27 Aug 2024
Towards Case-based Interpretability for Medical Federated Learning
Towards Case-based Interpretability for Medical Federated Learning
Laura Latorre
Liliana Petrychenko
Regina Beets-Tan
T. Kopytova
Wilson Silva
MedIm
66
0
0
24 Aug 2024
DOPPLER: Differentially Private Optimizers with Low-pass Filter for
  Privacy Noise Reduction
DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction
Xinwei Zhang
Zhiqi Bu
Mingyi Hong
Meisam Razaviyayn
74
5
0
24 Aug 2024
LLM-PBE: Assessing Data Privacy in Large Language Models
LLM-PBE: Assessing Data Privacy in Large Language Models
Qinbin Li
Junyuan Hong
Chulin Xie
Jeffrey Tan
Rachel Xin
...
Dan Hendrycks
Zhangyang Wang
Bo Li
Bingsheng He
Dawn Song
ELMPILM
131
24
0
23 Aug 2024
Understanding Data Reconstruction Leakage in Federated Learning from a
  Theoretical Perspective
Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective
Zifan Wang
Binghui Zhang
Meng Pang
Yuan Hong
Binghui Wang
FedML
91
0
0
22 Aug 2024
Differentially Private Stochastic Gradient Descent with Fixed-Size
  Minibatches: Tighter RDP Guarantees with or without Replacement
Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement
Jeremiah Birrell
Reza Ebrahimi
R. Behnia
Jason L. Pacheco
116
1
0
19 Aug 2024
Calibrating Noise for Group Privacy in Subsampled Mechanisms
Calibrating Noise for Group Privacy in Subsampled Mechanisms
Yangfan Jiang
Xinjian Luo
Yin Yang
Xiaokui Xiao
97
3
0
19 Aug 2024
Differential Private Stochastic Optimization with Heavy-tailed Data:
  Towards Optimal Rates
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
Puning Zhao
Xiaogang Xu
Zhe Liu
Chong Wang
Rongfei Fan
Qingming Li
88
1
0
19 Aug 2024
WPN: An Unlearning Method Based on N-pair Contrastive Learning in
  Language Models
WPN: An Unlearning Method Based on N-pair Contrastive Learning in Language Models
Guitao Chen
Yunshen Wang
Hongye Sun
Guang Chen
MU
75
2
0
18 Aug 2024
The Power of Bias: Optimizing Client Selection in Federated Learning
  with Heterogeneous Differential Privacy
The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy
Jiating Ma
Yipeng Zhou
Qi Li
Quan Z. Sheng
Laizhong Cui
Jiangchuan Liu
FedML
77
2
0
16 Aug 2024
A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs
A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs
H. B. McMahan
Zheng Xu
Yanxiang Zhang
FedML
131
8
0
16 Aug 2024
Towards Realistic Synthetic User-Generated Content: A Scaffolding
  Approach to Generating Online Discussions
Towards Realistic Synthetic User-Generated Content: A Scaffolding Approach to Generating Online Discussions
K. Balog
John Palowitch
Barbara Ikica
Filip Radlinski
Hamidreza Alvari
Mehdi Manshadi
SyDa
81
2
0
15 Aug 2024
Casper: Prompt Sanitization for Protecting User Privacy in Web-Based
  Large Language Models
Casper: Prompt Sanitization for Protecting User Privacy in Web-Based Large Language Models
Chun Jie Chong
Chenxi Hou
Z. Yao
S. Talebi
83
7
0
13 Aug 2024
Better Gaussian Mechanism using Correlated Noise
Better Gaussian Mechanism using Correlated Noise
Christian Janos Lebeda
109
4
0
13 Aug 2024
Privacy in Federated Learning
Privacy in Federated Learning
Jaydip Sen
Hetvi Waghela
Sneha Rakshit
FedML
56
6
0
12 Aug 2024
Deep Learning with Data Privacy via Residual Perturbation
Deep Learning with Data Privacy via Residual Perturbation
Wenqi Tao
Huaming Ling
Zuoqiang Shi
Bao Wang
73
2
0
11 Aug 2024
Attacks and Defenses for Generative Diffusion Models: A Comprehensive
  Survey
Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey
V. T. Truong
Luan Ba Dang
Long Bao Le
DiffMMedIm
132
23
0
06 Aug 2024
Mission Impossible: A Statistical Perspective on Jailbreaking LLMs
Mission Impossible: A Statistical Perspective on Jailbreaking LLMs
Jingtong Su
Mingyu Lee
SangKeun Lee
95
14
0
02 Aug 2024
Privacy-Preserving Split Learning with Vision Transformers using
  Patch-Wise Random and Noisy CutMix
Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix
Yang Jin
Sihun Baek
Lei Zhang
Hyelin Nam
Praneeth Vepakomma
Ramesh Raskar
Mehdi Bennis
Seong-Lyun Kim
90
3
0
02 Aug 2024
Differentially Private Block-wise Gradient Shuffle for Deep Learning
Differentially Private Block-wise Gradient Shuffle for Deep Learning
Zilong Zhang
FedML
129
0
0
31 Jul 2024
Private Collaborative Edge Inference via Over-the-Air Computation
Private Collaborative Edge Inference via Over-the-Air Computation
Selim F. Yilmaz
Burak Hasircioglu
Li Qiao
Deniz Gunduz
FedML
135
1
0
30 Jul 2024
Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing
Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing
Eugenio Lomurno
Matteo Matteucci
74
4
0
30 Jul 2024
Strong Copyright Protection for Language Models via Adaptive Model
  Fusion
Strong Copyright Protection for Language Models via Adaptive Model Fusion
Javier Abad
Konstantin Donhauser
Francesco Pinto
Fanny Yang
103
5
0
29 Jul 2024
Blockchain for Large Language Model Security and Safety: A Holistic
  Survey
Blockchain for Large Language Model Security and Safety: A Holistic Survey
Caleb Geren
Amanda Board
Gaby G. Dagher
Tim Andersen
Jun Zhuang
119
7
0
26 Jul 2024
Granularity is crucial when applying differential privacy to text: An
  investigation for neural machine translation
Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation
Doan Nam Long Vu
Timour Igamberdiev
Ivan Habernal
86
0
0
26 Jul 2024
Explaining the Model, Protecting Your Data: Revealing and Mitigating the
  Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference
Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference
Catherine Huang
Martin Pawelczyk
Himabindu Lakkaraju
AAML
69
1
0
24 Jul 2024
Synthetic Trajectory Generation Through Convolutional Neural Networks
Synthetic Trajectory Generation Through Convolutional Neural Networks
Jesse Merhi
Erik Buchholz
S. Kanhere
88
0
0
24 Jul 2024
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