VLDB 2026 Research / reviewers in the wild / expert
Zehui Wu
dblp:158/3332
· DBLP profile ↗
12ranked-venue papers
2as first author
11since 2021 · last 2025
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 4 · 3 since 2021Artificial intelligence and machine learning · 3 · 1 first-author · 3 since 2021Systems, architecture and hardware · 3 · 1 first-author · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 since 2021Computer networks · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Comparison-Based Automatic Evaluation for Meeting Summarization
Ziwei Gong, Lin Ai, Harsh Deshpande, Alexander Johnson, Emmy Phung, Zehui Wu, Ahmad Emami, Julia Hirschberg |
INTERSPEECH | 6 |
| 2025 | Learning More with Less: Self-Supervised Approaches forLow-Resource Speech Emotion Recognition
Ziwei Gong, Pengyuan Shi, Kaan Donbekci, Lin Ai, Run Chen, David Sasu, Zehui Wu, Julia Hirschberg |
INTERSPEECH | 7 |
| 2025 | Yesterday Once MorE: Facilitating Linux Kernel Bug Reproduction via Reverse FuzzingabstractThe Linux kernel remains vulnerable to numerous bugs, with approximately 65% detected by Syzkaller lacking Proof-of-Concept (PoC), hampering risk mitigation efforts. These bugs, termed irreproducible kernel bugs, highlight the challenge of statefulness issue-related irreproducibility in kernel fuzzing, which is an open research without definitive solutions. Our investigation reveals that suboptimal seed quality distribution in fuzzing is the root obstacle preventing effective tracking of the states leading to crashes. Inspired by this insight, we introduce Reverse Fuzzing (RF), an innovative approach that infers hard-to- reach states by continuously reverse-oriented deriving from subsequently encountered bridge states to increase reproduction probability. RF differentiates between the “trigger” seed, which directly causes crashes, and “activator” seeds, which establish the necessary preconditions, prioritizing exploration around trigger while simultaneously regenerating and maintaining activators during fuzzing, which effectively facilitate to restructure such elusive states from “yesterday”. We implement YOME, a prototype leveraging RF to strike a balance between fuzzing efficiency and effectiveness through customized scheduling and mutation strategies, armed with a refinement mechanism to improve seed quality distribution. Our evaluations validate that YOME reproduce 110% more bugs than previous kernel fuzzers and demonstrate its practicality in real-world scenarios. YOME generated 125 PoCs (30.1% of the total) and uncovered 23 unique bugs, with 40 confirmed and 5 assigned CVEs. Xingwei Li, Yan Kang 0002, Chenggang Wu 0002, Danjun Liu, Jiming Wang, Zehui Wu, Yunchao Wang, Rongkuan Ma |
IEEE Trans. Inf. Forensics Secur. | 7 |
| 2024 | BlockWhisper: A Blockchain-Based Hybrid Covert Communication Scheme with Strong Ability to Evade Detection
Zehui Wu, Yuwei Xu 0001, Ranfeng Huang, Xinhe Fan, Jingdong Xu, Guang Cheng 0001 |
ICA3PP (1) | 1 |
| 2024 | Multimodal Multi-loss Fusion Network for Sentiment AnalysisabstractZehui Wu, Ziwei Gong, Jaywon Koo, Julia Hirschberg. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024. Zehui Wu, Ziwei Gong, Jaywon Koo, Julia Hirschberg |
NAACL-HLT | 1 |
| 2024 | Demystifying the Security Implications in IoT Device Rental Services
Yi He 0020, Yunchao Guan, Ruoyu Lun, Shangru Song, Jianwei Zhuge, Jianjun Chen 0005, Zehui Wu, Hetian Shi, Qi Li 0002 |
USENIX Security Symposium | 9 |
| 2024 | SHFuzz: Service handler-aware fuzzing for detecting multi-type vulnerabilities in embedded devices
Xixing Li, Zehui Wu, Yunchao Wang |
Comput. Secur. | 4 |
| 2024 | A prior knowledge-guided distributionally robust optimization-based adversarial training strategy for medical image classification
Shancheng Jiang, Zehui Wu, Haiqiong Yang, Kun Xiang, Weiping Ding 0001, Zhen-Song Chen 0002 |
Inf. Sci. | 2 |
| 2024 | Harden-IoT: hardening the EoL devices by intercepting the attack vector for future B5G/6G IoT
Xixing Li, Zehui Wu, Linhao He |
Wirel. Networks | 3 |
| 2023 | DarkTrans: A Blockchain-based Covert Communication Scheme with High Channel Capacity and Strong ConcealmentabstractCovert communication technology serves as a crucial tool for safeguarding not only the content of communication but also the identities of the parties involved. In this regard, blockchain emerges as a promising solution due to its decentralized nature, flood propagation of data, and inherent anonymity features. This makes blockchain an ideal candidate for covert communication channels, effectively addressing the weaknesses associated with traditional covert communication methods susceptible to detection, tracing, and interruption. However, the current efforts encounter obstacles like limited practicality, constrained channel capacity, and insufficient concealment capabilities, impeding their broad adoption in real-world scenarios. To address these issues, we propose DarkTrans, a blockchain-based covert communication scheme consisting of an address binary tree and a novel embedding mechanism. The address binary tree as a dynamic label method enables rapid recognition of specific transactions by the recipient, rendering detection by third parties challenging. The embedding mechanism encodes secret messages into transaction values for transmission to augment channel capacity, which can be practically realized within an Ethereum private blockchain. Our experiments with three aspects demonstrate that, compared with the existing scheme, DarkTrans achieves a low embedding time and a high channel capacity. Additionally, Kolmogorov-Smirnov test and sample entropy analysis are conducted to validate the robust concealment of this scheme. Yuwei Xu 0001, Zehui Wu, Jie Cao 0009, Jingdong Xu, Guang Cheng 0001 |
ICPADS | 2 |
| 2022 | Secure analysis on entire software-defined network using coloring distribution modelabstractSummary Software Definition Network (SDN) has three features as separation of control and forwarding, unified management of configuration, and dynamic programming, which have greatly improved flexibility of network deployment and dynamics of network management, as well as efficiency of network transmission. However, its security problem is quite outstanding. This paper proposes a new security defense method based on coloring distribution model, which aims at the shortcomings of the current research that does not change the weak security, certainty, statics, and isomorphism of SDN. Motivated by the idea of moving target defense, our method abstracts network topology of SDN using coloring theory and realizes diversified deployment of controllers and switches, thus improving the security of network itself without changing the structure of SDN. Simulation results show that our method can prevent denial of service (DOS) attacks against controllers and switches and at the same time effectively block the worm, which is one of the most threat of smart city, propagation via switches. Xinhui Zhao, Zehui Wu, Xiaobin Song |
Concurr. Comput. Pract. Exp. | 2 |
| 2016 | SDNGuardian: Secure Your REST NBIs with API-Grained Permission Checking System
Kailei Ren, Zehui Wu |
SecureComm | 3 |