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 cryptographic algorithm, as a robust solution for securing ML models. We propose an NTRU-based framework that enhances ML security by enabling privacy-preserving model training and inference through homomorphic encryption. Our approach ensures data confidentiality while mitigating key security threats, such as unauthorized model extraction and adversarial manipulation. Performance evaluations demonstrate that NTRU offers a favourable trade-off between security and computational efficiency compared to conventional cryptographic schemes. The results highlight the potential of NTRU in securing ML applications against both classical and quantum-era threats, paving the way for scalable and secure ML deployments in sensitive domains.