Faculty
Best paper award at NDSS 2019
I am a faculty member at CISPA Helmholtz Center for Information Security.
Previously, I was a research group leader at CISPA. From January 2017 to December 2018, I was a postdoc with Michael Backes. Prior to that, I obtained my Ph.D. degree from University of Luxembourg on November 2016 under the supervision of Sjouke Mauw and Jun Pang. I obtained my bachelor (2009) and master (2012) degrees from Shandong University, China.
My research interests lie at the intersection of privacy and machine learning. Topics can be broadly categorized into two themes. First, I develop machine learning algorithms to quantify and mitigate privacy risks stemming from a variety of human-generated data, such as social network data, biomedical data, and location data. Second, I investigate novel attack surfaces against machine learning algorithms and develop privacy-enhancing techniques to mitigate the discovered risks. Besides, I also work on social network analysis and algorithmic fairness.
USENIX-Security
USENIX Security Symposium (USENIX Security)USENIX Security Symposium (USENIX Security), 2022.
USENIX-Security
USENIX Security Symposium
USENIX-Security
USENIX Security Symposium
UNSPECIFIED
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops2021 IEEE CVPR Workshop on Fair, Data Efficient and Trusted Computer Vision
WWW
The Web Conference
CCS
ACM SIGSAC Conference on Computer and Communications Security
CCS
Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS '20)ACM Conference on Computer and Communications Security (CCS)
EuroS&P
IEEE European Symposium on Security and Privacy
USENIX-Security
USENIX Security Symposium
NDSS
Proceedings of the Annual Network and Distributed System Security Symposium (NDSS)Annual Network and Distributed System Security Symposium
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.
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.
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.
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.