Safety of GenAI Through the Lens of Security and Cryptography

Übersicht

Immerse yourself in an enlightening session with Somesh Jha from the University of Wisconsin-Madison, focusing on the safety challenges faced by Generative AI. This course examines how security and cryptography frameworks can address safety concerns in AI development.

Available exclusively on YouTube, it is a must-watch for enthusiasts of Artificial Intelligence and Computer Science.

Lehrplan

• Introduction to Generative AI
Fundamental concepts of Generative AI
Overview of current applications and challenges
• Key Concepts in Security and Cryptography
Basic principles of computer security
Cryptographic techniques and protocols
• Security Risks in Generative AI
Attack vectors specific to GenAI
Case studies of security breaches in AI systems
• Cryptographic Methods for Securing GenAI
Encryption techniques for protecting AI models and data
Digital signatures and authentication for AI systems
• Data Privacy and GenAI
Differential privacy in AI applications
Privacy-preserving machine learning techniques
• Risk Assessment in GenAI Systems
Identifying and evaluating potential security risks
Developing risk mitigation strategies
• Adversarial Attacks and Defenses in GenAI
Types of adversarial attacks on AI models
Defense mechanisms and robust model design
• Secure Design of GenAI Models
Principles of secure software development for AI
Best practices for securing AI model lifecycle
• Regulatory and Ethical Considerations
Compliance with data protection laws
Ethical implications of GenAI deployment
• Emerging Trends in AI Safety and Security
Advances in cryptography related to AI
Future directions for GenAI safety research
• Case Studies and Applications
Real-world applications and implications of secured GenAI systems
Lessons learned from past incidents
• Conclusion and Future Directions
Summary of key learnings
Potential future developments in GenAI security
• Final Project/Assessment
Practical exercise or project to apply course concepts
Evaluation criteria and project presentation guidelines

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Fachgebiete

Computer Science