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Stuhlsatzenhaus 5
66123 Saarbrücken (Germany)


Best paper award at NDSS 2019

Short Bio

Dr. Yang Zhang is Faculty at CISPA. His research concentrates on trustworthy machine learning (privacy, safety, and security). Moreover, he works on measuring and understanding misinformation and unsafe content like hateful memes on the Internet. Over the years, he has published multiple papers at top venues in computer science, including CCS, NDSS, Oakland, and USENIX Security. His work has received the NDSS 2019 distinguished paper award and the CCS 2022 best paper award runner-up.

CV: Last four stations

Since 2020
Faculty at CISPA Helmholtz Center for Information Security
2019 - 2020
Research Group Leader at CISPA Helmholtz Center for Information Security
2017 - 2018
Postdoctoral Researcher - Host: Michael Backes - CISPA, Saarland University
2012 - 2016
Ph.D. in Computer Science at University of Luxembourg, highest honor

Publications by Yang Zhang

Year 2023

Conference / Medium

USENIX Security Symposium 2023USENIX Security Symposium 2023

Conference / Medium

USENIX SecurityUSENIX Security

Conference / Medium

2023 IEEE Symposium on Security and Privacy (SP)44th IEEE Symposium on Security and Privacy (S&P '23)

Year 2022

Conference / Medium

NeurIPS 2022NeurIPS 2022

Conference / Medium

ACMThe 29th ACM Conference on Computer and Communications Security (CCS)

Teaching by Yang Zhang

Winter 2021/22

Privacy of Machine Learning

Machine learning has witnessed tremendous progress during the past decade, and data is the key to such success. However, in many cases, machine learning models are trained on sensitive data, e.g., biomedical records, and such data can be leaked from trained machine learning models. In this seminar, we will cover the newest research papers in this direction.

Summer 2020

Advanced Lecture: Privacy Enhancing Technologies

This course will cover the topic of data privacy from four aspects: social network privacy, location privacy, Machine learning privacy, biomedical privacy.

Summer 2020

Seminar: Data-driven Approaches on Understanding Disinformation

In this seminar, we will look into research that focuses on extracting insights from large corpus of data with the goal to understand emerging socio-technical issues on the Web such as the dissemination of disinformation and hateful content. 

Winter 2019/20

Seminar: Data Privacy

Students will learn, summarize, and present state-of-the-art scientific papers in data privacy. Topics include social network privacy, machine learning privacy, and biomedical data privacy.