conftrace_

privacy amplification

29 papers

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Co-occurring keywords

differential privacy (1016) federated learning (1333) stochastic gradient descent (1091) shuffle model (11) renyi differential privacy (12) local differential privacy (73) gaussian differential privacy (7) personalized privacy (3) privacy profile (2) distributed mean estimation (9)

Papers

Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity JMLR 2025
Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification NIPS 2024
Differentially Private Graph Diffusion with Applications in Personalized PageRanks NIPS 2024
Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions AAAI 2024
A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility AAAI 2024
Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement NIPS 2024
Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model AAAI 2023
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning ICML 2023
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation NIPS 2023
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy NIPS 2023
(Amplified) Banded Matrix Factorization: A unified approach to private training NIPS 2023
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy NIPS 2023
Privacy Amplification by Decentralization AISTATS 2022
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss NIPS 2022
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) NIPS 2022
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy NIPS 2022
Privacy Amplification via Shuffling for Linear Contextual Bandits ALT 2022
Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning NIPS 2022
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging NIPS 2022
Privacy Amplification by Subsampling in Time Domain AISTATS 2022
FLAME: Differentially Private Federated Learning in the Shuffle Model AAAI 2021
Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning NIPS 2021
Variational Bayes in Private Settings (VIPS) (Extended Abstract) IJCAI 2020
Privacy Amplification via Random Check-Ins NIPS 2020
Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases NIPS 2019