Privacy-Preserving AI: Techniques, Challenges, and Future Directions

Modern AI systems rely heavily on vast datasets, often containing sensitive personal, financial, or medical information. This dependency raises critical concerns about data misuse, leakage, and regulatory compliance. Privacy-preserving AI aims to train and deploy models that retain high performance while ensuring that user data remains confidential. This paper presents a comprehensive analysis of current PPAI methods, their effectiveness, and practical implications for real-world deployment.