Poster "differential privacy" Papers
76 papers found • Page 2 of 2
Conference
How Private are DP-SGD Implementations?
Lynn Chua, Badih Ghazi, Pritish Kamath et al.
Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy
Wei-Ning Chen, Berivan Isik, Peter Kairouz et al.
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization
Badih Ghazi, Pritish Kamath, Ravi Kumar et al.
Learning Differentially Private Diffusion Models via Stochastic Adversarial Distillation
Bochao Liu, Pengju Wang, Shiming Ge
Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy
Ziqin Chen, Yongqiang Wang
Making Old Things New: A Unified Algorithm for Differentially Private Clustering
Max Dupre la Tour, Monika Henzinger, David Saulpic
Mean Estimation in the Add-Remove Model of Differential Privacy
Alex Kulesza, Ananda Suresh, Yuyan Wang
Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning
Joon Suk Huh, Kirthevasan Kandasamy
Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning
Chendi Wang, Yuqing Zhu, Weijie Su et al.
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
Saber Malekmohammadi, Yaoliang Yu, YANG CAO
Optimal Differentially Private Model Training with Public Data
Andrew Lowy, Zeman Li, Tianjian Huang et al.
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
Florian Tramer, Gautam Kamath, Nicholas Carlini
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.
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.