Deep Learning with Differential Privacy

1 July 2016

Papers citing "Deep Learning with Differential Privacy"

50 / 1,254 papers shown

Title
Are We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems Jiankai Jin Eleanor McMurtry Benjamin I. P. Rubinstein O. Ohrimenko 32 36 0 10 Dec 2021
Improving language models by retrieving from trillions of tokens Sebastian Borgeaud A. Mensch Jordan Hoffmann Trevor Cai Eliza Rutherford ... Simon Osindero Karen Simonyan Jack W. Rae Erich Elsen Laurent Sifre KELM RALM 90 1,049 0 08 Dec 2021
Membership Inference Attacks From First Principles Nicholas Carlini Steve Chien Milad Nasr Shuang Song Andreas Terzis Florian Tramèr MIACV MIALM 38 657 0 07 Dec 2021
Location Leakage in Federated Signal Maps Evita Bakopoulou Justin Ley Jiang Zhang Konstantinos Psounis A. Markopoulou FedML 29 5 0 07 Dec 2021
Differentially Private Exploration in Reinforcement Learning with Linear Representation Paul Luyo Evrard Garcelon A. Lazaric Matteo Pirotta 65 11 0 02 Dec 2021
Improving Differentially Private SGD via Randomly Sparsified Gradients Junyi Zhu Matthew B. Blaschko 35 5 0 01 Dec 2021
Public Data-Assisted Mirror Descent for Private Model Training Ehsan Amid Arun Ganesh Rajiv Mathews Swaroop Indra Ramaswamy Shuang Song Thomas Steinke Vinith Suriyakumar Om Thakkar Abhradeep Thakurta 29 50 0 01 Dec 2021
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning Yangsibo Huang Samyak Gupta Zhao Song Kai Li Sanjeev Arora FedML AAML SILM 34 269 0 30 Nov 2021
Differentially private stochastic expectation propagation (DP-SEP) Margarita Vinaroz Mijung Park 30 1 0 25 Nov 2021
Decentralized Federated Learning through Proxy Model Sharing Shivam Kalra Junfeng Wen Jesse C. Cresswell M. Volkovs Hamid R. Tizhoosh FedML 21 95 0 22 Nov 2021
Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors Ruoxi Sun Minhui Xue Gareth Tyson Tian Dong Shaofeng Li Shuo Wang Haojin Zhu S. Çamtepe Surya Nepal AAML 54 15 0 19 Nov 2021
Differentially Private Federated Learning on Heterogeneous Data Maxence Noble A. Bellet Aymeric Dieuleveut FedML 18 104 0 17 Nov 2021
Privacy-preserving Federated Learning for Residential Short Term Load Forecasting Joaquín Delgado Fernández Sergio Potenciano Menci Chul Min Lee Gilbert Fridgen 40 54 0 17 Nov 2021
Network Generation with Differential Privacy Xu Zheng Nicholas McCarthy Jer Hayes 30 2 0 17 Nov 2021
Fast Yet Effective Machine Unlearning Ayush K Tarun Vikram S Chundawat Murari Mandal Mohan S. Kankanhalli MU 38 175 0 17 Nov 2021
On the Importance of Difficulty Calibration in Membership Inference Attacks Lauren Watson Chuan Guo Graham Cormode Alex Sablayrolles 31 124 0 15 Nov 2021
Eluding Secure Aggregation in Federated Learning via Model Inconsistency Dario Pasquini Danilo Francati G. Ateniese FedML 38 102 0 14 Nov 2021
Distribution-Invariant Differential Privacy Xuan Bi Xiaotong Shen 29 14 0 08 Nov 2021
Bayesian Framework for Gradient Leakage Mislav Balunović Dimitar I. Dimitrov Robin Staab Martin Vechev FedML 32 41 0 08 Nov 2021
Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates Héber H. Arcolezi Jean-François Couchot Bechara al Bouna X. Xiao 30 29 0 08 Nov 2021
Federated Learning Attacks Revisited: A Critical Discussion of Gaps, Assumptions, and Evaluation Setups A. Wainakh Ephraim Zimmer Sandeep Subedi Jens Keim Tim Grube Shankar Karuppayah Alejandro Sánchez Guinea Max Mühlhäuser 38 9 0 05 Nov 2021
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale Karthik Garimella N. Jha Zahra Ghodsi S. Garg Brandon Reagen 38 3 0 04 Nov 2021
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence Tianshi Cao Alex Bie Arash Vahdat Sanja Fidler Karsten Kreis SyDa DiffM 39 71 0 01 Nov 2021
Masked LARk: Masked Learning, Aggregation and Reporting worKflow Joseph J. Pfeiffer Denis Xavier Charles Davis Gilton Young Hun Jung Mehul Parsana Erik Anderson 35 11 0 27 Oct 2021
Differentially Private Federated Bayesian Optimization with Distributed Exploration Zhongxiang Dai K. H. Low Patrick Jaillet FedML 29 41 0 27 Oct 2021
CAFE: Catastrophic Data Leakage in Vertical Federated Learning Xiao Jin Pin-Yu Chen Chia-Yi Hsu Chia-Mu Yu Tianyi Chen FedML 19 147 0 26 Oct 2021
Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection Chunjong Park Anas Awadalla Tadayoshi Kohno Shwetak N. Patel OOD 35 29 0 26 Oct 2021
Fair Sequential Selection Using Supervised Learning Models Mohammad Mahdi Khalili Xueru Zhang Mahed Abroshan FaML 41 20 0 26 Oct 2021
DP-XGBoost: Private Machine Learning at Scale Cheng Cheng Wei Dai 24 8 0 25 Oct 2021
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning Lokesh Nagalapatti Mahdi S. Hosseini FedML 35 75 0 23 Oct 2021
Tight and Robust Private Mean Estimation with Few Users Cheng-Han Chiang Vahab Mirrokni Hung-yi Lee FedML 33 29 0 22 Oct 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 59 14 0 22 Oct 2021
User-Level Private Learning via Correlated Sampling Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 65 13 0 21 Oct 2021
Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover Wei Pu Chao Zhou Yonina C. Eldar M. Rodrigues OOD 32 1 0 20 Oct 2021
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes Chonghua Liao Jiafan He Quanquan Gu 39 17 0 19 Oct 2021
DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy Anda Cheng Jiaxing Wang Xi Sheryl Zhang Qiang Chen Peisong Wang Jian Cheng 44 28 0 16 Oct 2021
AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy L. Du Zhikun Zhang Shaojie Bai Changchang Liu S. Ji Peng Cheng Jiming Chen 96 36 0 14 Oct 2021
Adaptive Differentially Private Empirical Risk Minimization Xiaoxia Wu Lingxiao Wang Irina Cristali Quanquan Gu Rebecca Willett 73 6 0 14 Oct 2021
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 136 355 0 13 Oct 2021
Offset-Symmetric Gaussians for Differential Privacy Parastoo Sadeghi Mehdi Korki 46 8 0 13 Oct 2021
Not all noise is accounted equally: How differentially private learning benefits from large sampling rates Friedrich Dörmann Osvald Frisk L. Andersen Christian Fischer Pedersen FedML 66 25 0 12 Oct 2021
Generalization Techniques Empirically Outperform Differential Privacy against Membership Inference Jiaxiang Liu Simon Oya Florian Kerschbaum MIACV 48 9 0 11 Oct 2021
The Skellam Mechanism for Differentially Private Federated Learning Naman Agarwal Peter Kairouz Ziyu Liu FedML 45 122 0 11 Oct 2021
Aura: Privacy-preserving Augmentation to Improve Test Set Diversity in Speech Enhancement Xavier Gitiaux Aditya Khant Ebrahim Beyrami Chandan K. A. Reddy J. Gupchup Ross Cutler 27 0 0 08 Oct 2021
Combining Differential Privacy and Byzantine Resilience in Distributed SGD R. Guerraoui Nirupam Gupta Rafael Pinot Sébastien Rouault John Stephan FedML 48 4 0 08 Oct 2021
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 138 121 0 07 Oct 2021
The Connection between Out-of-Distribution Generalization and Privacy of ML Models Divyat Mahajan Shruti Tople Amit Sharma OOD 26 7 0 07 Oct 2021
On the Privacy Risks of Deploying Recurrent Neural Networks in Machine Learning Models Yunhao Yang Parham Gohari Ufuk Topcu AAML 40 3 0 06 Oct 2021
Task-aware Privacy Preservation for Multi-dimensional Data Jiangnan Cheng A. Tang Sandeep P. Chinchali 36 7 0 05 Oct 2021
Label differential privacy via clustering Hossein Esfandiari Vahab Mirrokni Umar Syed Sergei Vassilvitskii FedML 31 26 0 05 Oct 2021