Minhao Jin

I am a PhD student at Princeton University advised by Prof. Maria Apostolaki. I’m generally interested in the intersection of Computer Networks, Machine Learning, Security and Privacy.

I obtained my MS degree at Carnegie Mellon University where I was advised by Prof. Vyas Sekar and Prof. Giulia Fanti. My prior research primarily focused on privacy-preserving data sharing for networking traces. I completed my BS degree at Shanghai Jiao Tong University.

Selected Publications

SIGCOMM

Practical GAN-Based Synthetic IP Header Trace Generation Using NetShare

Yucheng Yin , Zinan Lin , Minhao Jin , and 2 more authors

In Proceedings of the ACM SIGCOMM 2022 Conference , 2022

Abs PDF Code

We explore the feasibility of using Generative Adversarial Networks (GANs) to automatically learn generative models to generate synthetic packet- and flow header traces for networking tasks (e.g., telemetry, anomaly detection, provisioning). We identify key fidelity, scalability, and privacy challenges and tradeoffs in existing GAN-based approaches. By synthesizing domain-specific insights with recent advances in machine learning and privacy, we identify design choices to tackle these challenges. Building on these insights, we develop an end-to-end framework, NetShare. We evaluate NetShare on six diverse packet header traces and find that: (1) across all distributional metrics and traces, it achieves 46% more accuracy than baselines and (2) it meets users’ requirements of downstream tasks in evaluating accuracy and rank ordering of candidate approaches.