Demo Data from SIGIR 2026 / reviewers in the wild / expert
Haiqing Qiu
dblp:361/2111
· DBLP profile ↗
3ranked-venue papers
0as first author
3since 2021 · last 2025
0009-0001-9978-249XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 3 · 3 since 2021
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.
| Software engineering, system software, and programming languages
1 paper |
Program analysis · 77% Operating systems · 23% | |
| Network and information security
1 paper |
Systems and software security · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security
vulnerability discovery |
0.7 | 1 | 2023 | KRover: A Symbolic Execution Engine for Dynamic Kernel Analysis · CCS 2023 |
Program analysis
symbolic execution |
0.7 | 1 | 2023 | KRover: A Symbolic Execution Engine for Dynamic Kernel Analysis · CCS 2023 |
Operating systems › system security › operating system security
kernel security |
0.2 | 1 | 2023 | KRover: A Symbolic Execution Engine for Dynamic Kernel Analysis · CCS 2023 |
Methods — techniques the papers use, named apart from their topics
symbolic execution · 1.3dynamic analysis · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | PRISM: To Fortify Widget Based User-App Data Exchanges Using Android Virtualization Framework
YingTat Ng, Haiqing Qiu, Xuhua Ding |
AsiaCCS | 3 |
| 2023 | KRover: A Symbolic Execution Engine for Dynamic Kernel AnalysisabstractWe present KRover, a novel kernel symbolic execution engine catered for dynamic kernel analysis such as vulnerability analysis and exploit generation. Different from existing symbolic execution engines, KRover operates directly upon a live kernel thread's virtual memory and weaves symbolic execution into the target's native executions. KRover is compact as it neither lifts the target binary to an intermediary representation nor uses QEMU or dynamic binary translation. Benchmarked against S2E, our performance experiments show that KRover is up to 50 times faster but with one tenth to one quarter of S2E memory cost. As shown in our four case studies, KRover is noise free, has the best-possible binary intimacy and does not require prior kernel instrumentation. Moreover, a user can develop her kernel analyzer that not only uses KRover as a symbolic execution library but also preserves its independent capabilities of reading/writing/controlling the target runtime. Namely, the resulting analyzer on top of KRover integrates symbolic reasoning and conventional dynamic analysis and reaps the benefits of their reinforcement to each other. Pansilu Pitigalaarachchi, Xuhua Ding, Haiqing Qiu, Haoxin Tu, Jiaqi Hong, Lingxiao Jiang |
CCS | 3 |
| 2023 | DScope: To Reliably and Securely Acquire Live Data from Kernel-Compromised ARM Devices
Haiqing Qiu, Xuhua Ding |
ESORICS (4) | 2 |