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Adresse

Stuhlsatzenhaus 5
66123 Saarbrücken (Germany)

Awards

Best paper award at NDSS 2019

Kurzbiografie

Dr. Yang Zhang ist Faculty am CISPA. Seine Forschung konzentriert sich auf Trustworthy Machine Learning (Privacy, Safety und Security). Außerdem arbeitet er an der Messung und dem Verständnis von Fehlinformationen und unsicheren Inhalten wie hasserfüllten Memes im Internet. Im Laufe der Jahre hat er zahlreiche Paper auf Spitzenkonferenzen in Informatik, einschließlich CCS, NDSS, Oakland und USENIX Security veröffentlicht. Seine Arbeit hat 2019 den NDSS Distinguished Paper Award und 2022 den CCS Best Paper Award Runner-up erhalten. 

CV: Letzte vier Stationen

Seit 2020
Faculty am CISPA Helmholtz-Zentrum für Informationssicherheit
2019 - 2020
Forschungsgruppenleiter am CISPA Helmholtz-Zentrum für Informationssicherheit
2017 - 2018
Postdoctoral Researcher - Host: Michael Backes - CISPA, Universität des Saarlandes
2012 - 2016
Ph.D. in Computer Science an der Universität in Luxembourg, highest honor

Veröffentlichungen von Yang Zhang

Jahr 2023

Konferenz / Medium

USENIX-Security
USENIX Security Symposium 2023USENIX Security Symposium 2023

Konferenz / Medium

USENIX-Security
USENIX SecurityUSENIX Security

Konferenz / Medium

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

Jahr 2022

Konferenz / Medium

NeurIPS
NeurIPS 2022NeurIPS 2022

Konferenz / Medium

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

Lehre von 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.