Security and Privacy-Preserving for Machine Learning Models: Attacks, Countermeasures, and Future Directions

As machine learning (ML) develops more essential in fields like healthcare, banking, autonomous systems, and smart cities, it encounters growing security and privacy concerns that threaten the integrity and reliability of these systems. This work tackles these difficulties by examining several defensive techniques, classifying security risks as data, model, communication, and entity attacks, and suggesting precise counter-measures to protect ML models. Additionally, it underscores the significance of lightweight and efficient security solutions, especially in resource-limited settings such as IoT and edge computing, where conventional approaches may not be feasible. This work emphasizes the necessity for continuous research in cryptographic and non-cryptographic methods to improve the security and privacy of ML systems, ensuring their secure and efficient implementation in diverse industries. Moreover, it encompasses future research objectives, including emphasizing the development of privacy-preserving techniques, the ability to withstand adversarial attacks, and the creation of scalable solutions to address upcoming issues, such as those presented by quantum computing.