Privacy Backdoors: Enhancing Membership Inference through Poisoning
Pre-trained Models

Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models

1 April 2024

Sanghyun Hong

Nicholas Carlini

Papers citing "Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models"

6 / 6 papers shown

Title
Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions Hao Du Shang Liu Lele Zheng Yang Cao Atsuyoshi Nakamura Lei Chen AAML 114 3 0 21 Dec 2024
Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage Md. Rafi Ur Rashid Jing Liu T. Koike-Akino Shagufta Mehnaz Ye Wang MU SILM 36 3 0 30 Aug 2024
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification Yuxin Wen Jonas Geiping Liam H. Fowl Micah Goldblum Tom Goldstein FedML 79 92 0 01 Feb 2022
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models Liam H. Fowl Jonas Geiping Steven Reich Yuxin Wen Wojtek Czaja Micah Goldblum Tom Goldstein FedML 71 56 0 29 Jan 2022
When the Curious Abandon Honesty: Federated Learning Is Not Private Franziska Boenisch Adam Dziedzic R. Schuster Ali Shahin Shamsabadi Ilia Shumailov Nicolas Papernot FedML AAML 69 181 0 06 Dec 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 267 1,812 0 14 Dec 2020