Archimedes Hosts Mini-Course on Data Privacy with resounding success!

ArchimedesAI and Data Science Research Hub Hosts Mini-Course on Data Privacy! The Archimedes Research Institute recently held an intensive mini-course on data privacy and secure computation on May 13-14. The event took place at the Archimedes AI and Data Science Research Hub, located within the Athena Research Center, and featured both in-person and online participation.

The mini-course, titled "Cutting-edge Topics in Data Privacy, Privacy-Preserving and Secure Computation, and Privacy-Preserving and Secure Data Science," attracted a diverse group of researchers, students, and industry professionals. Esteemed professors Srini Devadas from MIT and Katerina Sotiraki from Yale University, who is also a lead researcher at Archimedes, led the sessions.

The first day of the mini-course provided a comprehensive overview of data privacy and privacy-preserving computation. Professor Devadas presented on the foundational principles of data privacy, followed by a session on differential privacy, an important concept for ensuring data security in various applications. In the afternoon, the course covered advanced topics such as multi-party computation and fully homomorphic encryption, allowing computations on encrypted data without compromising privacy. The day concluded with a session on zero-knowledge proofs, a method enabling one party to prove to another that a statement is true without revealing any additional information.

The second day focused on the practical applications of the privacy theories discussed on the first day. Professor Sotiraki led the morning sessions, starting with PAC (Probably Approximately Correct) Privacy Theory and its practice, providing a framework for understanding and implementing privacy-preserving techniques in real-world scenarios. In the afternoon, the course addressed adaptive data analysis, which ensures the reliability of data insights even when data is reused. The mini-course concluded with a session on secure aggregated statistics and federated learning, highlighting methods for analyzing data across multiple locations while maintaining privacy.

The mini-course received positive feedback from attendees, who appreciated the depth and clarity of the lectures and the expertise of the instructors. The hybrid format facilitated broader participation, with attendees joining from various parts of the world, contributing to a productive exchange of ideas.

This event underscores the Archimedes Research Institute's commitment to advancing knowledge in data privacy and secure computation. It represents a step towards educating and empowering the next generation of researchers and practitioners in the field of data science.

The Archimedes Research Institute looks forward to hosting future events that continue to serve the academic community in AI and data science.