14 citations · 49 across the 26 of their papers we have counts for
6 papers · 1 filter
Differentially Private CutMix for Split Learning with Vision Transformer
Seungeun Oh, Jihong Park, Sihun Baek +5
Recently, vision transformer (ViT) has started to outpace the conventional CNN in computer vision tasks. Considering privacy-preserving distributed learning with ViT, federated lea…
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric
Saiteja Utpala, Praneeth Vepakomma, Nina Miolane
Differential privacy has become crucial in the real-world deployment of statistical and machine learning algorithms with rigorous privacy guarantees. The earliest statistical queri…
Private independence testing across two parties
Praneeth Vepakomma, Mohammad Mohammadi Amiri, Clément L. Canonne +2
We introduce -test, a privacy-preserving algorithm for testing statistical independence between data distributed across multiple parties. Our algorithm relies on privately estim…
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning
Sihun Baek, Jihong Park, Praneeth Vepakomma +3
This article seeks for a distributed learning solution for the visual transformer (ViT) architectures. Compared to convolutional neural network (CNN) architectures, ViTs often have…
Decouple-and-Sample: Protecting sensitive information in task agnostic data release
Abhishek Singh, Ethan Garza, Ayush Chopra +3
We propose sanitizer, a framework for secure and task-agnostic data release. While releasing datasets continues to make a big impact in various applications of computer vision, its…
Effects of Privacy-Inducing Noise on Welfare and Influence of Referendum Systems
Suat Evren, Praneeth Vepakomma
Social choice functions help aggregate individual preferences while differentially private mechanisms provide formal privacy guarantees to release answers of queries operating on s…