UBC Security & Privacy Group


We are a diverse group of people working on topics related to systems security, cloud security, network security, privacy, ML, and software engineering.

We are always looking to hire talented and motivated students.

Multiple Graduate Student Openings

UBC Security & Privacy Group is looking for multiple graduate students to start in the fall of 2025. Interested students must apply through the university. We are particularly interested by students working on the following topics:

Latest Publications

SafeBPF: Hardware-assisted Defense-in-depth for eBPF Kernel Extensions
The eBPF framework enables execution of user-provided code in the Linux kernel. In the last few years, a large ecosystem of cloud …
PDF Cite
SafeBPF: Hardware-assisted Defense-in-depth for eBPF Kernel Extensions
GFWeb: Measuring the Great Firewall's Web Censorship at Scale
Abstract: Censorship systems such as the Great Firewall (GFW) have been continuously refined to enhance their filtering capabilities. However, most prior studies, and in particular the GFW, have been limited in scope and conducted over short time periods, leading to gaps in our understanding of the GFW’s evolving Web censorship mechanisms over time.
Preprint Cite Project
GFWeb: Measuring the Great Firewall's Web Censorship at Scale
NetShaper: A Differentially Private Network Side-Channel Mitigation System
The widespread adoption of encryption in network protocols has significantly improved the overall security of many Internet …
Preprint PDF Cite Project
NetShaper: A Differentially Private Network Side-Channel Mitigation System
Automatic Generation of Web Censorship Probe Lists
Abstract: Domain probe lists—used to determine which URLs to probe for Web censorship—play a critical role in Internet censorship measurement studies. Indeed, the size and accuracy of the domain probe list limits the set of censored pages that can be detected; inaccurate lists can lead to an incomplete view of the censorship landscape or biased results.
Preprint PDF Cite
Automatic Generation of Web Censorship Probe Lists
Toward Automated DNS Tampering Detection Using Machine Learning
Abstract: DNS manipulation is one of the most prevalent and effective techniques for censoring Internet access and interfering with users’ online activities worldwide. Reliable detection of DNS tampering is crucial, but challenging due to evolving censorship tactics and the lack of complete ground truth data.
PDF Cite
Toward Automated DNS Tampering Detection Using Machine Learning
See all publications