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"

50 / 104 papers shown

Title
When Does Differentially Private Learning Not Suffer in High Dimensions? Xuechen Li Daogao Liu Tatsunori Hashimoto Huseyin A. Inan Janardhan Kulkarni Y. Lee Abhradeep Thakurta 43 58 0 01 Jul 2022
Libra: High-Utility Anonymization of Event Logs for Process Mining via Subsampling Gamal Elkoumy Marlon Dumas 25 6 0 27 Jun 2022
Walking to Hide: Privacy Amplification via Random Message Exchanges in Network Hao Wu O. Ohrimenko Anthony Wirth FedML 32 1 0 20 Jun 2022
Analytical Composition of Differential Privacy via the Edgeworth Accountant Hua Wang Sheng-yang Gao Huanyu Zhang Milan Shen Weijie J. Su FedML 36 21 0 09 Jun 2022
Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization Yi Ma T. V. Marinov Tong Zhang 30 8 0 03 Jun 2022
On the Privacy Properties of GAN-generated Samples Zinan Lin Vyas Sekar Giulia Fanti PICV 24 26 0 03 Jun 2022
Differentially Private Shapley Values for Data Evaluation Lauren Watson R. Andreeva Hao Yang Rik Sarkar TDI FAtt FedML 23 6 0 01 Jun 2022
Private Federated Submodel Learning with Sparsification Sajani Vithana S. Ulukus FedML 33 10 0 31 May 2022
Provably Confidential Language Modelling Xuandong Zhao Lei Li Yu Wang MU 39 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 27 5 0 15 Mar 2022
Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms Armando Angrisani Mina Doosti E. Kashefi 24 12 0 07 Mar 2022
Continual and Sliding Window Release for Private Empirical Risk Minimization Lauren Watson Abhirup Ghosh Benedek Rozemberczki Rik Sarkar 27 0 0 07 Mar 2022
Private Quantiles Estimation in the Presence of Atoms Clément Lalanne C. Gastaud Nicolas Grislain Aurélien Garivier Rémi Gribonval 26 8 0 15 Feb 2022
Over-the-Air Ensemble Inference with Model Privacy Selim F. Yilmaz Burak Hasircioglu Deniz Gunduz FedML 40 23 0 07 Feb 2022
Gradient Leakage Attack Resilient Deep Learning Wenqi Wei Ling Liu SILM PILM AAML 32 48 0 25 Dec 2021
On Convergence of Federated Averaging Langevin Dynamics Wei Deng Qian Zhang Yi Ma Zhao Song Guang Lin FedML 35 16 0 09 Dec 2021
Improving Differentially Private SGD via Randomly Sparsified Gradients Junyi Zhu Matthew B. Blaschko 32 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 22 8 0 25 Oct 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 39 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
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 135 120 0 07 Oct 2021
Releasing Graph Neural Networks with Differential Privacy Guarantees Iyiola E. Olatunji Thorben Funke Megha Khosla 42 45 0 18 Sep 2021
Random Sampling Plus Fake Data: Multidimensional Frequency Estimates With Local Differential Privacy Héber H. Arcolezi Jean-François Couchot Bechara al Bouna Xiaokui Xiao 30 27 0 15 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 41 132 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 35 64 0 30 Jul 2021
Optimal Accounting of Differential Privacy via Characteristic Function Yuqing Zhu Jinshuo Dong Yu Wang 18 98 0 16 Jun 2021
Differentially Private Fractional Frequency Moments Estimation with Polylogarithmic Space Lun Wang I. Pinelis D. Song 23 16 0 26 May 2021
A Nearly Instance-optimal Differentially Private Mechanism for Conjunctive Queries Wei Dong K. Yi 24 23 0 12 May 2021
Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation Mohamed Seif Wei-Ting Chang Ravi Tandon FedML 31 45 0 02 Mar 2021
Machine Unlearning via Algorithmic Stability Enayat Ullah Tung Mai Anup B. Rao Ryan Rossi R. Arora 35 103 0 25 Feb 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 94 111 0 25 Feb 2021
Differential Privacy for Government Agencies -- Are We There Yet? Joerg Drechsler 31 20 0 17 Feb 2021
Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy Ajesh Koyatan Chathoth Abhyuday N. Jagannatha Stephen Lee 23 14 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 82 216 0 11 Jan 2021
Privacy Amplification by Decentralization Edwige Cyffers A. Bellet FedML 54 39 0 09 Dec 2020
Federated Model Distillation with Noise-Free Differential Privacy Lichao Sun Lingjuan Lyu FedML 34 106 0 11 Sep 2020
Differentially Private Accelerated Optimization Algorithms Nurdan Kuru cS. .Ilker Birbil Mert Gurbuzbalaban S. Yıldırım 32 23 0 05 Aug 2020
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization Rui Hu Yanmin Gong Yuanxiong Guo FedML 34 55 0 01 Aug 2020
Differentially private cross-silo federated learning Mikko A. Heikkilä A. Koskela Kana Shimizu Samuel Kaski Antti Honkela FedML 29 24 0 10 Jul 2020
Towards practical differentially private causal graph discovery Lun Wang Qi Pang D. Song 31 13 0 15 Jun 2020
Near Instance-Optimality in Differential Privacy Hilal Asi John C. Duchi 26 38 0 16 May 2020
DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications Sameer Wagh Xi He Ashwin Machanavajjhala Prateek Mittal 33 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 30 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 34 83 0 10 Jan 2020
Federated Learning with Bayesian Differential Privacy Aleksei Triastcyn Boi Faltings FedML 19 174 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 23 5 0 28 Oct 2019
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification Oluwaseyi Feyisetan Thomas Drake Borja Balle Tom Diethe 22 10 0 26 Mar 2019
The Privacy Blanket of the Shuffle Model Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 42 236 0 07 Mar 2019