Practical Defences Against Model Inversion Attacks for Split Neural
Networks

Practical Defences Against Model Inversion Attacks for Split Neural Networks

12 April 2021

Pavlos Papadopoulos

Daniele Romanini

Papers citing "Practical Defences Against Model Inversion Attacks for Split Neural Networks"

8 / 8 papers shown

Title
A Taxonomy of Attacks and Defenses in Split Learning Aqsa Shabbir Halil Ibrahim Kanpak Alptekin Küpçü Sinem Sav 41 0 0 09 May 2025
Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems Song Xia Yi Yu Wenhan Yang Meiwen Ding Zhuo Chen Lingyu Duan Alex C. Kot Xudong Jiang 56 2 0 01 Mar 2025
GAN-based Vertical Federated Learning for Label Protection in Binary Classification Yujin Han Leying Guan FedML 25 0 0 04 Feb 2023
Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions Qiang Duan Shijing Hu Ruijun Deng Zhihui Lu FedML 23 61 0 20 Jul 2022
Survey: Leakage and Privacy at Inference Time Marija Jegorova Chaitanya Kaul Charlie Mayor Alison Q. OÑeil Alexander Weir Roderick Murray-Smith Sotirios A. Tsaftaris PILM MIACV 17 71 0 04 Jul 2021
PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN Daniele Romanini A. Hall Pavlos Papadopoulos Tom Titcombe Abbas Ismail Tudor Cebere R. Sandmann Robin Roehm Michael A. Hoeh FedML MU 13 90 0 01 Apr 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 281 1,812 0 14 Dec 2020
FedML: A Research Library and Benchmark for Federated Machine Learning Chaoyang He Songze Li Jinhyun So Xiao Zeng Mi Zhang ... Yang Liu Ramesh Raskar Qiang Yang M. Annavaram Salman Avestimehr FedML 168 564 0 27 Jul 2020