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Deep Learning with Differential Privacy

1 July 2016

Papers citing "Deep Learning with Differential Privacy"

50 / 2,788 papers shown

Title
Federated learning with differential privacy and an untrusted aggregator Kunlong Liu Trinabh Gupta 106 1 0 17 Dec 2023
Privacy-Aware Document Visual Question Answering Rubèn Pérez Tito Khanh Nguyen Marlon Tobaben Raouf Kerkouche Mohamed Ali Souibgui ... Lei Kang Ernest Valveny Antti Honkela Mario Fritz Dimosthenis Karatzas 86 13 0 15 Dec 2023
Decaffe: DHT Tree-Based Online Federated Fake News Detection Cheng-Wei Ching Liting Hu 43 3 0 15 Dec 2023
Greedy Shapley Client Selection for Communication-Efficient Federated Learning Pranava Singhal Shashi Raj Pandey P. Popovski FedML 59 4 0 14 Dec 2023
On Mask-based Image Set Desensitization with Recognition Support Qilong Li Ji Liu Yifan Sun Chongsheng Zhang Dejing Dou CVBM 86 4 0 14 Dec 2023
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey Yichen Wan Youyang Qu Wei Ni Yong Xiang Longxiang Gao Ekram Hossain AAML 108 43 0 14 Dec 2023
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance Ilana Sebag Muni Sreenivas Pydi Jean-Yves Franceschi Alain Rakotomamonjy Mike Gartrell Jamal Atif Alexandre Allauzen 129 3 0 13 Dec 2023
Black-box Membership Inference Attacks against Fine-tuned Diffusion Models Yan Pang Tianhao Wang 86 20 0 13 Dec 2023
Layered Randomized Quantization for Communication-Efficient and Privacy-Preserving Distributed Learning Guangfeng Yan Tan Li Tian-Shing Lan Kui Wu Linqi Song 67 7 0 12 Dec 2023
Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey Haotian Zhang S. D. Semujju Zhicheng Wang Xianwei Lv Kang Xu ... Jing Wu Zhuo Long Wensheng Liang Xiaoguang Ma Ruiyan Zhuang UQCV AI4TS AI4CE 107 4 0 11 Dec 2023
Classification with Partially Private Features Zeyu Shen A. Krishnaswamy Janardhan Kulkarni Kamesh Munagala 102 4 0 11 Dec 2023
Optimal Unbiased Randomizers for Regression with Label Differential Privacy Ashwinkumar Badanidiyuru Badih Ghazi Pritish Kamath Ravi Kumar Ethan Leeman Pasin Manurangsi A. Varadarajan Chiyuan Zhang 116 4 0 09 Dec 2023
Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approach Seyed Mahmoud Sajjadi Mohammadabadi Syed Zawad Feng Yan Lei Yang FedML 71 7 0 09 Dec 2023
DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming Shuya Feng Meisam Mohammady Han Wang Xiaochen Li Zhan Qin Yuan Hong 78 9 0 07 Dec 2023
Diffence: Fencing Membership Privacy With Diffusion Models Yuefeng Peng Ali Naseh Amir Houmansadr AAML 91 1 0 07 Dec 2023
SoK: Unintended Interactions among Machine Learning Defenses and Risks Vasisht Duddu S. Szyller Nadarajah Asokan AAML 175 2 0 07 Dec 2023
Privacy-preserving quantum federated learning via gradient hiding Changhao Li Niraj Kumar Zhixin Song Shouvanik Chakrabarti Marco Pistoia FedML 89 20 0 07 Dec 2023
Making Translators Privacy-aware on the User's Side Ryoma Sato 59 2 0 07 Dec 2023
Low-Cost High-Power Membership Inference Attacks Sajjad Zarifzadeh Philippe Liu Reza Shokri 142 44 0 06 Dec 2023
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance Haichao Sha Ruixuan Liu Yi-xiao Liu Hong Chen 137 2 0 06 Dec 2023
All Rivers Run to the Sea: Private Learning with Asymmetric Flows Yue Niu Ramy E. Ali Saurav Prakash Salman Avestimehr FedML 80 2 0 05 Dec 2023
Scaling Laws for Adversarial Attacks on Language Model Activations Stanislav Fort 79 16 0 05 Dec 2023
Hot PATE: Private Aggregation of Distributions for Diverse Task Edith Cohen Benjamin Cohen-Wang Xin Lyu Jelani Nelson Tamas Sarlos Uri Stemmer 125 4 0 04 Dec 2023
PAC Privacy Preserving Diffusion Models Qipan Xu Youlong Ding Xinxi Zhang Jie Gao Hao Wang DiffM 87 0 0 02 Dec 2023
FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation Fanfei Meng Lele Zhang Yu Chen Yuxin Wang FedML 122 4 0 30 Nov 2023
AnonPSI: An Anonymity Assessment Framework for PSI Bo Jiang Jian Du Qiang Yan 78 8 0 29 Nov 2023
The Symmetric alpha-Stable Privacy Mechanism Christopher Zawacki Eyad H. Abed 42 2 0 29 Nov 2023
Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions Alexander Boudewijn Andrea Filippo Ferraris D. Panfilo Vanessa Cocca Sabrina Zinutti Karel De Schepper Carlo Rossi Chauvenet 87 3 0 29 Nov 2023
Grounding Foundation Models through Federated Transfer Learning: A General Framework Yan Kang Tao Fan Hanlin Gu Xiaojin Zhang Lixin Fan Qiang Yang AI4CE 205 19 0 29 Nov 2023
Survey on AI Ethics: A Socio-technical Perspective Dave Mbiazi Meghana Bhange Maryam Babaei Ivaxi Sheth Patrik Kenfack 99 5 0 28 Nov 2023
FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings Jean Ogier du Terrail Quentin Klopfenstein Honghao Li Imke Mayer Nicolas Loiseau Mohammad Hallal Félix Balazard M. Andreux 82 2 0 28 Nov 2023
FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting Meenatchi Sundaram Muthu Selva Annamalai Igor Bilogrevic Emiliano De Cristofaro 96 1 0 28 Nov 2023
Edge AI for Internet of Energy: Challenges and Perspectives Yassine Himeur A. Sayed A. Alsalemi F. Bensaali Abbes Amira 139 33 0 28 Nov 2023
Using Decentralized Aggregation for Federated Learning with Differential Privacy H. Saleh Y. El-Sonbaty Ana Fernández Vilas M. Fernández-Veiga Nashwa El-Bendary FedML 73 3 0 27 Nov 2023
DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer Junyuan Hong Jiachen T. Wang Chenhui Zhang Zhangheng Li Yue Liu Zhangyang Wang 136 39 0 27 Nov 2023
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach Xinwei Zhang Zhiqi Bu Zhiwei Steven Wu Mingyi Hong 76 7 0 24 Nov 2023
DP-NMT: Scalable Differentially-Private Machine Translation Timour Igamberdiev Doan Nam Long Vu Felix Künnecke Zhuo Yu Jannik Holmer Ivan Habernal 98 7 0 24 Nov 2023
Privacy-Preserving Algorithmic Recourse Sikha Pentyala Sanjay Kariyappa Anh Totti Nguyen Freddy Lecue Daniele Magazzeni 84 5 0 23 Nov 2023
Weight fluctuations in (deep) linear neural networks and a derivation of the inverse-variance flatness relation Markus Gross A. Raulf Christoph Räth 141 0 0 23 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 77 17 0 23 Nov 2023
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning Tre' R. Jeter Truc D. T. Nguyen Raed Alharbi My T. Thai AAML 72 0 0 23 Nov 2023
Privacy-Preserving Load Forecasting via Personalized Model Obfuscation Shourya Bose Yu Zhang Kibaek Kim 71 3 0 21 Nov 2023
Zero redundancy distributed learning with differential privacy Zhiqi Bu Justin Chiu Ruixuan Liu Sheng Zha George Karypis 102 8 0 20 Nov 2023
Can we infer the presence of Differential Privacy in Deep Learning models' weights? Towards more secure Deep Learning Daniel Jiménez-López Daniel Nuria Rodríguez Barroso Nuria M. V. Luzón M. Victoria Francisco Herrera Francisco AAML 66 0 0 20 Nov 2023
Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations Elaheh Jafarigol Theodore Trafalis Talayeh Razzaghi Mona Zamankhani FedML 75 1 0 17 Nov 2023
From Principle to Practice: Vertical Data Minimization for Machine Learning Robin Staab Nikola Jovanović Mislav Balunović Martin Vechev 94 7 0 17 Nov 2023
Trustworthy Large Models in Vision: A Survey Ziyan Guo Li Xu Jun Liu MU 145 0 0 16 Nov 2023
Privacy Threats in Stable Diffusion Models Thomas Cilloni Charles Fleming Charles Walter 75 3 0 15 Nov 2023
Are Normalizing Flows the Key to Unlocking the Exponential Mechanism? Robert A. Bridges Vandy J. Tombs Christopher B. Stanley 86 1 0 15 Nov 2023
Sparsity-Preserving Differentially Private Training of Large Embedding Models Badih Ghazi Yangsibo Huang Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 71 2 0 14 Nov 2023