Membership Inference Attack: A Middleware-Based Approach for Privacy Preservation and Attack Mitigation in Machine Learning Systems

Abstract

This article explores the use of middleware as a robust solution to mitigate membership inference attacks (MIA) in machine learning (ML) systems. These attacks allow an attacker to deduce whether a specific data point was part of a model’s training set, compromising data confidentiality and privacy. The proposed approach focuses on the use of middleware that implements data randomization techniques, prediction obfuscation, dynamic regularization, and real-time monitoring to prevent such attacks. The results reveal that this middleware architecture provides an additional layer of security, minimizing the risk of data exposure while maintaining model accuracy. This research offers a novel perspective on using middleware for mitigating membership inference attacks, providing valuable insights into machine learning security.