Demo Data from SIGIR 2026 / reviewers in the wild / expert
Joseph Zhang
dblp:249/2614
· DBLP profile ↗
3ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 2 · 2 since 2021Human-computer interaction and ubiquitous computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
2 papers |
Systems and software security · 63% Digital forensics and information hiding · 28% Hardware security and side channels · 10% | |
| Artificial intelligence
1 paper |
Deep learning architectures and training · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security › memory safety
address space layout randomization |
0.9 | 1 | 2025 | Oreo: Protecting ASLR Against Microarchitectural Attacks · NDSS 2025 |
Systems and software security
memory safety |
0.9 | 1 | 2025 | Oreo: Protecting ASLR Against Microarchitectural Attacks · NDSS 2025 |
Digital forensics and information hiding › digital forensics
memory forensics |
0.8 | 1 | 2024 | AI Psychiatry: Forensic Investigation of Deep Learning Networks in Memory Images · USENIX Security Symposium 2024 |
Hardware security and side channels
microarchitectural attacks |
0.3 | 1 | 2025 | Oreo: Protecting ASLR Against Microarchitectural Attacks · NDSS 2025 |
Methods — techniques the papers use, named apart from their topics
memory image analysis · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Oreo: Protecting ASLR Against Microarchitectural Attacks
Shixin Song, Joseph Zhang, Mengjia Yan 0001 |
NDSS | 2 |
| 2024 | AI Psychiatry: Forensic Investigation of Deep Learning Networks in Memory Images
David Oygenblik, Carter Yagemann, Joseph Zhang, Arianna Mastali, Jeman Park 0001, Brendan Saltaformaggio |
USENIX Security Symposium | 3 |
| 2019 | A Forensic Qualitative Analysis of Contributions to Wikipedia from Anonymity Seeking UsersabstractBy choice or by necessity, some contributors to commons-based peer production sites use privacy-protecting services to remain anonymous. As anonymity seekers, users of the Tor network have been cast both as ill-intentioned vandals and as vulnerable populations concerned with their privacy. In this study, we use a dataset drawn from a corpus of Tor edits to Wikipedia to uncover the character of Tor users' contributions. We build in-depth narrative descriptions of Tor users' actions and conduct a thematic analysis that places their editing activity into seven broad groups. We find that although their use of a privacy-protecting service marks them as unusual within Wikipedia, the character of many Tor users' contributions is in line with the expectations and norms of Wikipedia. However, our themes point to several important places where lack of trust promotes disorder, and to contributions where risks to contributors, service providers, and communities are unaligned. Kaylea Champion, Nora McDonald, Stephanie Bankes, Joseph Zhang, Rachel Greenstadt, Andrea Forte, Benjamin Mako Hill |
Proc. ACM Hum. Comput. Interact. | 4 |