Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences

4 July 2018

Gilles Barthe

Papers citing "Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences"

38 / 88 papers shown

Title
Private Federated Submodel Learning with Sparsification Sajani Vithana S. Ulukus FedML 26 10 0 31 May 2022
Provably Confidential Language Modelling Xuandong Zhao Lei Li Yu-Xiang Wang MU 21 15 0 04 May 2022
Training a Tokenizer for Free with Private Federated Learning Eugene Bagdasaryan Congzheng Song Rogier van Dalen M. Seigel Áine Cahill FedML 22 5 0 15 Mar 2022
Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms Armando Angrisani Mina Doosti E. Kashefi 21 12 0 07 Mar 2022
Continual and Sliding Window Release for Private Empirical Risk Minimization Lauren Watson Abhirup Ghosh Benedek Rozemberczki Rik Sarkar 21 0 0 07 Mar 2022
Private Quantiles Estimation in the Presence of Atoms Clément Lalanne C. Gastaud Nicolas Grislain Aurélien Garivier Rémi Gribonval 20 7 0 15 Feb 2022
Over-the-Air Ensemble Inference with Model Privacy Selim F. Yilmaz Burak Hasircioglu Deniz Gunduz FedML 32 23 0 07 Feb 2022
Gradient Leakage Attack Resilient Deep Learning Wenqi Wei Ling Liu SILM PILM AAML 27 46 0 25 Dec 2021
On Convergence of Federated Averaging Langevin Dynamics Wei Deng Qian Zhang Yi Ma Zhao-quan Song Guang Lin FedML 30 16 0 09 Dec 2021
Improving Differentially Private SGD via Randomly Sparsified Gradients Junyi Zhu Matthew B. Blaschko 26 5 0 01 Dec 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 19 71 0 01 Nov 2021
DP-XGBoost: Private Machine Learning at Scale Cheng Cheng Wei Dai 19 8 0 25 Oct 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 32 14 0 22 Oct 2021
Combining Differential Privacy and Byzantine Resilience in Distributed SGD R. Guerraoui Nirupam Gupta Rafael Pinot Sébastien Rouault John Stephan FedML 43 4 0 08 Oct 2021
Releasing Graph Neural Networks with Differential Privacy Guarantees Iyiola E. Olatunji Thorben Funke Megha Khosla 32 44 0 18 Sep 2021
Selective Differential Privacy for Language Modeling Weiyan Shi Aiqi Cui Evan Li R. Jia Zhou Yu 20 68 0 30 Aug 2021
Large-Scale Differentially Private BERT Rohan Anil Badih Ghazi Vineet Gupta Ravi Kumar Pasin Manurangsi 36 131 0 03 Aug 2021
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges S. Ulukus Salman Avestimehr Michael C. Gastpar S. Jafar Ravi Tandon Chao Tian FedML 28 64 0 30 Jul 2021
Optimal Accounting of Differential Privacy via Characteristic Function Yuqing Zhu Jinshuo Dong Yu-Xiang Wang 18 98 0 16 Jun 2021
A Nearly Instance-optimal Differentially Private Mechanism for Conjunctive Queries Wei Dong K. Yi 13 23 0 12 May 2021
Machine Unlearning via Algorithmic Stability Enayat Ullah Tung Mai Anup B. Rao Ryan Rossi R. Arora 27 101 0 25 Feb 2021
Differential Privacy for Government Agencies -- Are We There Yet? Joerg Drechsler 26 20 0 17 Feb 2021
Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy Ajesh Koyatan Chathoth Abhyuday N. Jagannatha Stephen Lee 15 13 0 25 Jan 2021
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning Milad Nasr Shuang Song Abhradeep Thakurta Nicolas Papernot Nicholas Carlini MIACV FedML 65 216 0 11 Jan 2021
Privacy Amplification by Decentralization Edwige Cyffers A. Bellet FedML 44 39 0 09 Dec 2020
Federated Model Distillation with Noise-Free Differential Privacy Lichao Sun Lingjuan Lyu FedML 26 106 0 11 Sep 2020
Differentially private cross-silo federated learning Mikko A. Heikkilä A. Koskela Kana Shimizu Samuel Kaski Antti Honkela FedML 23 24 0 10 Jul 2020
Towards practical differentially private causal graph discovery Lun Wang Qi Pang D. Song 23 13 0 15 Jun 2020
Near Instance-Optimality in Differential Privacy Hilal Asi John C. Duchi 24 38 0 16 May 2020
DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications Sameer Wagh Xi He Ashwin Machanavajjhala Prateek Mittal 28 21 0 19 Apr 2020
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$ -Divergences S. Asoodeh Jiachun Liao Flavio du Pin Calmon O. Kosut Lalitha Sankar FedML 14 38 0 16 Jan 2020
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Shuang Song Kunal Talwar Abhradeep Thakurta 21 83 0 10 Jan 2020
Federated Learning with Bayesian Differential Privacy Aleksei Triastcyn Boi Faltings FedML 11 172 0 22 Nov 2019
Improved Differentially Private Decentralized Source Separation for fMRI Data H. Imtiaz Jafar Mohammadi Rogers F. Silva Bradley T. Baker Sergey Plis Anand D. Sarwate Vince D. Calhoun OOD 18 5 0 28 Oct 2019
The Privacy Blanket of the Shuffle Model Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 33 236 0 07 Mar 2019
Subsampled Rényi Differential Privacy and Analytical Moments Accountant Yu-Xiang Wang Borja Balle S. Kasiviswanathan 14 397 0 31 Jul 2018
Sampling Without Compromising Accuracy in Adaptive Data Analysis Benjamin Fish L. Reyzin Benjamin I. P. Rubinstein 47 8 0 28 Sep 2017
Variational Bayes In Private Settings (VIPS) Mijung Park James R. Foulds Kamalika Chaudhuri Max Welling 21 42 0 01 Nov 2016