Privacy Threats Unveiled: A Comprehensive Analysis of Membership Inference Attacks on Machine Learning Models and Defense Strategies

Abstract

Membership inference attacks, aiming to determine whether target data belongs to a training dataset through machine learning model exploitation, present an escalating privacy threat within the machine learning landscape. This study initiates from fundamental theories surrounding the attack and defense mechanisms of machine learning models. The paper conducts a thorough analysis of key technical models, elucidating the intricate relationship between attack models and potential privacy risks to ensure data privacy security and advance the realm of machine learning applications. The introduction covers the adversary model of membership inference attacks, encompassing definitions, classifications, and the generation mechanism. Additionally, the paper provides a comprehensive overview and analysis of existing membership inference attack algorithms. Practical applications of membership inference attacks are explored, followed by the categorization and comparison of defense techniques. The study concludes with a comparative analysis of existing attack schemes and their corresponding defense technologies, offering insights into the evolving landscape of membership inference attacks in machine learning. The work not only anticipates future research challenges in data privacy protection but also establishes a theoretical foundation crucial for addressing data privacy leakage, thereby significantly contributing to the progress of machine learning applications.