Representation Magnitude has a Liability to Privacy Vulnerability

Autor:	Fang, Xingli, Kim, Jung-Eun
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Cryptography and Security Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	The privacy-preserving approaches to machine learning (ML) models have made substantial progress in recent years. However, it is still opaque in which circumstances and conditions the model becomes privacy-vulnerable, leading to a challenge for ML models to maintain both performance and privacy. In this paper, we first explore the disparity between member and non-member data in the representation of models under common training frameworks. We identify how the representation magnitude disparity correlates with privacy vulnerability and address how this correlation impacts privacy vulnerability. Based on the observations, we propose Saturn Ring Classifier Module (SRCM), a plug-in model-level solution to mitigate membership privacy leakage. Through a confined yet effective representation space, our approach ameliorates models' privacy vulnerability while maintaining generalizability. The code of this work can be found here: \url{https://github.com/JEKimLab/AIES2024_SRCM} Comment: Accepted in the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.16164 Zobrazit plný text záznamu View this record from Arxiv