"differential privacy" Papers
67 papers found • Page 2 of 2
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
Florian Tramer, Gautam Kamath, Nicholas Carlini
Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions
T-H. Hubert Chan, Hao Xie, Mengshi ZHAO
Privacy-Preserving Instructions for Aligning Large Language Models
Da Yu, Peter Kairouz, Sewoong Oh et al.
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems
Roie Reshef, Kfir Levy
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses
Changyu Gao, Andrew Lowy, Xingyu Zhou et al.
Privately Learning Smooth Distributions on the Hypercube by Projections
Clément Lalanne, Sébastien Gadat
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages
Hilal Asi, Vitaly Feldman, Jelani Nelson et al.
Proactive DP: A Multiple Target Optimization Framework for DP-SGD
Marten van Dijk, Nhuong Nguyen, Toan N. Nguyen et al.
Profile Reconstruction from Private Sketches
Hao WU, Rasmus Pagh
Provable Privacy with Non-Private Pre-Processing
Yaxi Hu, Amartya Sanyal, Bernhard Schölkopf
Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation
Weiming Liu, Xiaolin Zheng, Chaochao Chen et al.
Replicable Learning of Large-Margin Halfspaces
Alkis Kalavasis, Amin Karbasi, Kasper Green Larsen et al.
Rethinking DP-SGD in Discrete Domain: Exploring Logistic Distribution in the Realm of signSGD
Jonggyu Jang, Seongjin Hwang, Hyun Jong Yang
Shifted Interpolation for Differential Privacy
Jinho Bok, Weijie Su, Jason Altschuler
The Privacy Power of Correlated Noise in Decentralized Learning
Youssef Allouah, Anastasiia Koloskova, Aymane Firdoussi et al.
Unveiling Privacy, Memorization, and Input Curvature Links
Deepak Ravikumar, Efstathia Soufleri, Abolfazl Hashemi et al.
ViP: A Differentially Private Foundation Model for Computer Vision
Yaodong Yu, Maziar Sanjabi, Yi Ma et al.