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Joseph Zhang

dblp:249/2614 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Systems and software security › memory safety
address space layout randomization
0.912025
Oreo: Protecting ASLR Against Microarchitectural Attacks · NDSS 2025
Systems and software security
memory safety
0.912025
Oreo: Protecting ASLR Against Microarchitectural Attacks · NDSS 2025
Digital forensics and information hiding › digital forensics
memory forensics
0.812024
AI Psychiatry: Forensic Investigation of Deep Learning Networks in Memory Images · USENIX Security Symposium 2024
Hardware security and side channels
microarchitectural attacks
0.312025
Oreo: Protecting ASLR Against Microarchitectural Attacks · NDSS 2025

Methods — techniques the papers use, named apart from their topics

memory image analysis · 1.5
YearPublicationVenuePosition
2025 Oreo: Protecting ASLR Against Microarchitectural Attacks
Shixin Song, Joseph Zhang, Mengjia Yan 0001
NDSS2
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 Symposium3
2019 A Forensic Qualitative Analysis of Contributions to Wikipedia from Anonymity Seeking Users
abstract
By 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