Security, privacy, and robustness for trustworthy AI systems: A review

This review article provides a comprehensive exploration of the key pillars of trustworthy AI: security privacy and robustness. The article delved into security measures both traditional and cutting edge identifying emerging threats and challenges in ever ever-evolving landscape of artificial intelligence (AI) the discussion extends to advanced encryption techniques and imperative privacy preservation, emphasizing the ethical consideration inherent in safeguarding user data. The robustness and adversarial attack on AI, present techniques for the robustness model and ensure model interpretability and explainability through AI. The exploration of federated learning (FL) elucidates its conceptual foundations and intricate interplay between security, privacy, and collaborative model training. Differential privacy (DP) outlines insights into its application, and challenges. The ethical consideration section scrutinized bias and fairness in AI. The article concludes with an examination of emerging technologies in AI security and privacy anticipating challenges. This review article serves as a comprehensive guide to navigating the complex terrain of trustworthy AI.