privacy

Security, Privacy, and Human

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B4 L5 R5209

Security and privacy systems are often composed of complex components and details. However, users’ experience shouldn’t be as complex. In this seminar, Eman will discuss the human factor in the security and privacy chain. While human privacy perceptions and behaviors have been investigated in Western societies, little is known about these issues in non-Western societies.

Safeguarding the right to be forgotten

1 min read ·
An open-source software can help align artificial intelligence applications in healthcare with data privacy regulations.

Zihang Xiang

Ph.D. Student, Computer Science
My current research is on privacy-preserving data analysis. I am interested in pushing the boundaries of differential privacy via principled approaches in broad machine learning applications.

Yulian Wu

Ph.D. Student, Computer Science
Yulian Wu is a Ph.D. student under the supervision of Professor Francesco Orabona at King Abdullah University of Science and Technology (KAUST). Publications Youming Tao*, Yulian Wu*, Peng Zhao and Di Wang. "Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits." https://arxiv.org/abs/2106.02575. (Equal contributions) Education Profile M.S. in Statistics, East China Normal University, China B.S. in Mathematics and Applied Mathematics, East China Normal University, China

Xiaojie Zhu

Xiaojie Zhu is a staff research scientist at KAUST. Prior to that, he was an assistant professor of cybersecurity in the Department of Computer Science and Information Technology, College of Engineering, Abu Dhabi University. He received his Ph.D. from the Department of Computer Science, University of Oslo, Norway. Xiaojie's research interests are data security and privacy, software security, decentralized trust and infrastructure, and applied cryptography. He has published in prestigious journals such as IEEE TDSC, IEEE TPDS, JNCA, and JOS. One of his articles was included in the 21st annual

Lijie Hu

Ph.D. Student, Computer Science

Di Wang

Assistant Professor, Computer Science
Professor Wang's research addresses issues and societal concerns arising from machine learning, particularly in the areas of privacy, security, safety, fairness, robustness, interpretability and transparency. He aims to develop provable and practical algorithms to address trustworthiness issues in machine learning.