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Starts 4 July 2025 16:46

Ends 4 July 2025

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Privacy for AI from NP-Hard Problems - Universal Compute on Encrypted Data

Join this insightful course on cryptographic methods crucial for safeguarding privacy in AI applications. Delve into the intricacies of MultiParty Computation, Threshold Cryptography, and Fully Homomorphic Encryption. This course gives particular attention to overcoming challenges in evaluating complex operations like softmax while maintaining.
Open Compute Project via YouTube

Open Compute Project

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Overview

Join this insightful course on cryptographic methods crucial for safeguarding privacy in AI applications. Delve into the intricacies of MultiParty Computation, Threshold Cryptography, and Fully Homomorphic Encryption.

This course gives particular attention to overcoming challenges in evaluating complex operations like softmax while maintaining data encryption.

Presented by esteemed instructors on YouTube, this course is a vital part of both Artificial Intelligence and Computer Science disciplines, offering an in-depth understanding of universal compute on encrypted data. Ideal for those looking to advance their knowledge in state-of-the-art cryptographic techniques within AI frameworks.

Syllabus

  • Introduction to Privacy in AI
  • Overview of Privacy Concerns in AI
    Importance of Cryptographic Techniques
  • Cryptographic Foundations
  • Overview of Cryptography
    Basic Cryptographic Primitives
  • MultiParty Computation (MPC)
  • Definition and Principles of MPC
    Common Protocols and Applications
    Real-world Use Cases and Challenges
  • Threshold Cryptography
  • Introduction to Threshold Schemes
    Designing Threshold Cryptographic Protocols
    Security and Performance Considerations
  • Fully Homomorphic Encryption (FHE)
  • Principles of FHE
    Current FHE Schemes and Their Implementations
    Evaluating Efficiency and Use Cases
  • Privacy-preserving AI Operations
  • Challenges in Encrypted Domain Computation
    Specific Challenges in Encrypted Softmax Evaluation
    Techniques for Neural Network Evaluation on Encrypted Data
  • Designing Privacy-preserving AI Systems
  • Integrating Cryptographic Methods into AI Workflows
    Overcoming Practical Implementation Barriers
    Case Studies and Real-world Examples
  • Advanced Topics and Emerging Trends
  • Hybrid Approaches in Privacy-preserving AI
    Emerging Cryptographic Techniques for AI
  • Conclusion and Future Directions
  • Summarizing Key Learnings
    Potential Future Developments in AI Privacy
  • Final Project or Assessment
  • Design a Privacy-preserving AI Prototype
    Evaluate and Present a Case Study on Encrypted AI Computations

Subjects

Computer Science