Faculty
Best paper award at NDSS 2019
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.
USENIX-Security
USENIX Security Symposium 2023USENIX Security Symposium 2023
USENIX-Security
USENIX SecurityUSENIX Security
AAAI
AAAI 2023AAAI 2023
SP
2023 IEEE Symposium on Security and Privacy (SP)44th IEEE Symposium on Security and Privacy (S&P '23)
NeurIPS
NeurIPS 2022NeurIPS 2022
CCS
CCS 2022CCS 2022
CCS
CCS 2022CCS 2022
CCS
CCS 2022CCS 2022
CCS
CCS 2022CCS 2022
CCS
ACMThe 29th ACM Conference on Computer and Communications Security (CCS)
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.