Security for AI and Multi-Party Collaboration with Confidential Computing and Web3

Overview

Discover how Confidential Computing enables secure AI workloads and multi-party collaboration through cryptographic attestation, enhancing trust and creating new business opportunities in cloud computing.

Syllabus

• Introduction to Security for AI and Multi-Party Collaboration
Overview of AI security challenges
Importance of secure multi-party collaboration
• Fundamentals of Confidential Computing
Definition and significance
Use cases in AI and cloud environments
• Cryptographic Attestation
Concepts and mechanisms
Role in ensuring trust in AI workloads
• Secure AI Workloads
Techniques to protect AI models and data
Deploying secure artificial intelligence in cloud environments
• Multi-Party Collaboration Fundamentals
Principles and benefits
Challenges in data privacy and security
• Enhancing Trust with Confidential Computing
Attestation processes for stakeholder trust
Leveraging secure enclaves for privacy-preserving computation
• Business Opportunities in Confidential Computing
Emerging business models
Case studies of successful implementations
• Integration with Web3 Technologies
Overview of Web3 and decentralized networks
Synergies between Web3 and confidential computing
• Practical Applications and Hands-On Labs
Setting up a confidential computing environment
Enabling secure AI and multi-party operations on a blockchain
• Conclusion and Future Trends
Future developments in confidential computing
Evolving landscape of AI security and multi-party systems
• Final Project
Designing a secure AI pipeline with multi-party collaboration
Presentation and peer review of projects

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Subjects

Programming