Privacy and security threats and protection in machine learning

Machine learning (ML) has revolutionized various domains, but its adoption raises significant privacy and security concerns. Threats such as adversarial attacks, data poisoning, model inversion, and membership inference jeopardize the confidentiality and integrity of ML models. Traditional cryptographic approaches, while effective, often introduce high computational overhead, making them impractical for real-time applications. This paper explores the use of NTRU, a post-quantum lattice-based cry