AI Security and Privacy
Summary
AI Security and Privacy is a critical area of research and development in the field of artificial intelligence, addressing the paramount concerns of protecting both AI models and the data they process. This multifaceted domain encompasses challenges related to model security, such as safeguarding deep neural networks against poisoning and evasion attacks, as well as data privacy issues, including the protection of sensitive information used in training and inference. Researchers and practitioners in this field employ a wide range of techniques, from adversarial training and model obfuscation to differential privacy and homomorphic encryption, to create robust and secure AI systems. The ultimate goal is to develop AI technologies that can maintain their integrity, efficiency, and functionality while simultaneously preserving the privacy and security of the individuals and organizations whose data they handle. As AI continues to permeate various aspects of society, the importance of AI Security and Privacy will only grow, driving ongoing innovation in defensive strategies and privacy-preserving methodologies.