Foundations of Multi-Party Computation in AI

Foundations of Multi-Party Computation in AI is designed to equip participants with essential knowledge and practical skills in the realm of decentralized data processing. This course delves into the principles and applications of multi-party computation (MPC) within artificial intelligence, emphasizing how these techniques can enhance data privacy and security in collaborative environments. Participants will engage in hands-on projects that simulate real-world scenarios, fostering an interactive learning experience that culminates in the publication of their findings in Cademix Magazine.
The curriculum is structured to provide a comprehensive understanding of the theoretical foundations and practical implementations of MPC. By the end of the course, participants will not only be proficient in the technical aspects of MPC but also capable of applying these concepts to solve complex problems in AI. The course encourages collaboration and innovation, preparing attendees for advanced roles in data science and AI development.
Introduction to Multi-Party Computation: Concepts and Definitions
Overview of Federated Learning and its Relation to MPC
Cryptographic Techniques for Secure Computation
Protocols for Multi-Party Computation: A Comparative Study
Implementing MPC in Machine Learning Models
Case Studies of MPC in Industry Applications
Performance Metrics for Evaluating MPC Solutions
Tools and Frameworks for Developing MPC Systems
Collaborative Project: Designing an MPC-Based AI Solution
Final Project Presentation and Publication in Cademix Magazine

To develop proficiency in multi-party computation techniques and their application in AI, enabling participants to create secure and efficient data processing solutions.

Graduates, job seekers, business professionals, and optionally researchers or consultants.

Basic understanding of AI and data science concepts, familiarity with programming languages such as Python, and knowledge of cryptography fundamentals.