The Composition Theorem for Differential Privacy

4 November 2013

Pramod Viswanath

Papers citing "The Composition Theorem for Differential Privacy"

50 / 138 papers shown

Title
Source Anonymity for Private Random Walk Decentralized Learning Maximilian Egger Svenja Lage Rawad Bitar Antonia Wachter-Zeh 31 0 0 11 May 2025
Conformal-DP: Differential Privacy on Riemannian Manifolds via Conformal Transformation Peilin He Liou Tang M. Amin Rahimian James Joshi 31 0 0 29 Apr 2025
Locally Private Nonparametric Contextual Multi-armed Bandits Yuheng Ma Feiyu Jiang Zifeng Zhao Hanfang Yang Y. Yu 42 0 0 11 Mar 2025
Generalization in Federated Learning: A Conditional Mutual Information Framework Ziqiao Wang Cheng Long Yongyi Mao FedML 52 0 0 06 Mar 2025
An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses Hao Liang W. Zhang Xinlei He Kaishun He Hong Xing 47 0 0 25 Feb 2025
Guarding the Privacy of Label-Only Access to Neural Network Classifiers via iDP Verification Anan Kabaha Dana Drachsler-Cohen AAML 48 0 0 23 Feb 2025
Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy Khaoula Chehbouni Martine De Cock Gilles Caporossi Afaf Taik Reihaneh Rabbany G. Farnadi 73 0 0 21 Jan 2025
Differentially Private Online Federated Learning with Correlated Noise Jiaojiao Zhang Linglingzhi Zhu Mikael Johansson FedML 49 1 0 10 Jan 2025
Safeguarding System Prompts for LLMs Zhifeng Jiang Zhihua Jin Guoliang He AAML SILM 105 1 0 10 Jan 2025
Balls-and-Bins Sampling for DP-SGD Lynn Chua Badih Ghazi Charlie Harrison Ethan Leeman Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 80 3 0 21 Dec 2024
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Sangyeon Yoon Wonje Jeung Albert No 85 0 0 02 Dec 2024
The 2020 United States Decennial Census Is More Private Than You (Might) Think Buxin Su Weijie J. Su Chendi Wang 33 3 0 11 Oct 2024
Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data Jie Zhang Debeshee Das Gautam Kamath Florian Tramèr MIALM MIACV 235 16 1 29 Sep 2024
Differentially Private Block-wise Gradient Shuffle for Deep Learning Zilong Zhang FedML 31 0 0 31 Jul 2024
Privacy of the last iterate in cyclically-sampled DP-SGD on nonconvex composite losses Weiwei Kong Mónica Ribero 28 3 0 07 Jul 2024
Noisy Neighbors: Efficient membership inference attacks against LLMs Filippo Galli Luca Melis Tommaso Cucinotta 46 7 0 24 Jun 2024
Differentially Private Graph Diffusion with Applications in Personalized PageRanks Rongzhe Wei Eli Chien P. Li 42 5 0 22 Jun 2024
Private Online Learning via Lazy Algorithms Hilal Asi Tomer Koren Daogao Liu Kunal Talwar 109 0 0 05 Jun 2024
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition C. Lebeda Matthew Regehr Gautam Kamath Thomas Steinke 53 9 0 27 May 2024
HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization Shun Takagi Li Xiong Fumiyuki Kato Yang Cao Masatoshi Yoshikawa 3DH 46 2 0 13 May 2024
Budget Recycling Differential Privacy Bo Jiang Jian Du Sagar Shamar Qiang Yan 18 1 0 18 Mar 2024
Mean Estimation with User-Level Privacy for Spatio-Temporal IoT Datasets V. A. Rameshwar Anshoo Tandon Prajjwal Gupta Aditya Vikram Singh Novoneel Chakraborty Abhay Sharma 18 3 0 29 Jan 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization Xinyu Tang Ashwinee Panda Milad Nasr Saeed Mahloujifar Prateek Mittal 47 18 0 09 Jan 2024
Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective Héber H. Arcolezi Sébastien Gambs 37 1 0 04 Sep 2023
Differentially Private Heavy Hitter Detection using Federated Analytics Karan N. Chadha Junye Chen John C. Duchi Vitaly Feldman H. Hashemi O. Javidbakht Audra McMillan Kunal Talwar FedML 21 7 0 21 Jul 2023
Epsilon*: Privacy Metric for Machine Learning Models Diana M. Negoescu H. González Saad Eddin Al Orjany Jilei Yang Yuliia Lut ... Xinyi Zheng Zachariah Douglas Vidita Nolkha P. Ahammad G. Samorodnitsky 35 2 0 21 Jul 2023
Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection Eli Chien Wei-Ning Chen Chao Pan Pan Li Ayfer Özgür O. Milenkovic 36 12 0 12 Jul 2023
Differential Privacy for Clustering Under Continual Observation Max Dupré la Tour Monika Henzinger David Saulpic 15 1 0 07 Jul 2023
Personalized Privacy Amplification via Importance Sampling Dominik Fay Sebastian Mair Jens Sjölund 57 0 0 05 Jul 2023
A Note On Interpreting Canary Exposure Matthew Jagielski 18 4 0 31 May 2023
Amplification by Shuffling without Shuffling Borja Balle James Bell Adria Gascon FedML 37 2 0 18 May 2023
Differentially-private Continual Releases against Dynamic Databases Ming-Chuan Pan 21 0 0 05 May 2023
Sparse Private LASSO Logistic Regression Amol Khanna Fred Lu Edward Raff Brian Testa 13 3 0 24 Apr 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 26 9 0 11 Apr 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 94 167 0 01 Mar 2023
Don't Look at the Data! How Differential Privacy Reconfigures the Practices of Data Science Jayshree Sarathy Sophia Song Audrey Haque Tania Schlatter Salil P. Vadhan 23 23 0 23 Feb 2023
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks Arun Ganesh Daogao Liu Sewoong Oh Abhradeep Thakurta ODL 27 12 0 20 Feb 2023
Tight Auditing of Differentially Private Machine Learning Milad Nasr Jamie Hayes Thomas Steinke Borja Balle Florian Tramèr Matthew Jagielski Nicholas Carlini Andreas Terzis FedML 35 52 0 15 Feb 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 41 9 0 30 Jan 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 30 18 0 22 Jan 2023
Ranking Differential Privacy Shi Xu W. Sun Guang Cheng 18 1 0 02 Jan 2023
Castell: Scalable Joint Probability Estimation of Multi-dimensional Data Randomized with Local Differential Privacy H. Kikuchi 24 2 0 03 Dec 2022
Private Multi-Winner Voting for Machine Learning Adam Dziedzic Christopher A. Choquette-Choo Natalie Dullerud Vinith M. Suriyakumar Ali Shahin Shamsabadi Muhammad Ahmad Kaleem S. Jha Nicolas Papernot Xiao Wang 42 1 0 23 Nov 2022
Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection Alexander Goldberg Giulia Fanti Nihar B. Shah 10 3 0 23 Nov 2022
Lemmas of Differential Privacy Yiyang Huang C. Canonne 31 1 0 21 Nov 2022
Learning to Generate Image Embeddings with User-level Differential Privacy Zheng Xu Maxwell D. Collins Yuxiao Wang Liviu Panait Sewoong Oh S. Augenstein Ting Liu Florian Schroff H. B. McMahan FedML 30 29 0 20 Nov 2022
A Robust Dynamic Average Consensus Algorithm that Ensures both Differential Privacy and Accurate Convergence Yongqiang Wang 21 4 0 14 Nov 2022
TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data F. Houssiau James Jordon Samuel N. Cohen Owen Daniel Andrew Elliott James Geddes C. Mole Camila Rangel Smith Lukasz Szpruch 28 45 0 12 Nov 2022
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers Moritz Kirschte Sebastian Meiser Saman Ardalan Esfandiar Mohammadi FedML 34 0 0 03 Nov 2022
Revisiting Hyperparameter Tuning with Differential Privacy Youlong Ding Xueyang Wu 18 0 0 03 Nov 2022