Track 4  Security and Privacy for Distributed Computing and Learning

Track Chair: Songze Li, PhD, Assistant Professor

The thrust of Internet of Things, Hong Kong University of Science and Technology (Guangzhou), Guangzhou 510006, China

Email: songzeli@ust.hk

 

Track Co-Chair: Jinbao Zhu, PhD, Post-doctoral Fellow

The thrust of Internet of Things, Hong Kong University of Science and Technology (Guangzhou), Guangzhou 510006, China

Email: jbzhu@ust.hk

 

Scope

With the rapid development and wide deployment of distributed information systems, including distributed caching and content delivery networks, distributed storage and computation systems, distributed/federated learning systems, and blockchain systems, the concerns about privacy and security of data delivery and computation have become increasingly crucial in modern society.

This track will focus on identifying security vulnerabilities of current distributed computing systems, and development of provably secure protocols, with a focus on leveraging techniques from information theory, coding theory, and cryptography. Prospective authors are invited to submit original research contributions on topics including, but not limited to:

Model/data poisoning, and backdoor attacks in ML systems

Security and privacy in distributed and federated learning systems

Differential privacy and privacy-preserving computation

Coded distributed computation for privacy and security

Distributed private information and function retrieval

Secure multi-party computation, secret sharing, and verifiable computation

Blockchain security