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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