Latest COVID-19 Information for Purdue University

The Center for Education and Research in Information Assurance and Security (CERIAS)

The Center for Education and Research in
Information Assurance and Security (CERIAS)

Yimin Chen - Virginia Tech

All seminars for Summer 2021 will be held virtually. (No in-person attendance)

Delving into differential privacy and anomaly detection: a meta-learning perspective

Apr 14, 2021

Download: Video Icon MP4 Video Size: 402.5MB  
Watch on Youtube Watch on YouTube


In this talk, we explore security and privacy related to meta-learning, a learning paradigm aiming to learn 'cross-task' knowledge instead of 'single-task' knowledge. For privacy perspective, we conjecture that meta-learning plays an important role in future federated learning and look into federated meta-learning systems with differential privacy design for task privacy protection. For security perspective, we explore anomaly detection for machine learning models. Particularly, we explore poisoning attacks on machine learning models in which poisoning training samples are the anomaly. Inspired from that poisoning samples degrade trained models through overfitting, we exploit meta-training to counteract overfitting, thus enhancing model robustness.

About the Speaker

Yimin Chen
Yimin Chen is now a postdoctoral researcher in Computer Science department in Virginia Tech. Currently his research mainly focuses on differential privacy, anomaly detection, adversarial example, and private learning. Before he worked on security and privacy of mobile computing systems for his PhD study. He obtained a PhD degree from Arizona State University in 2018, a MPhil degree from Chinese University of Hong Kong in 2013, and a BS degree from Peking University in 2010.

Ways to Watch


Watch Now!

Over 500 videos of our weekly seminar and symposia keynotes are available on our YouTube Channel. Also check out Spaf's YouTube Channel. Subscribe today!