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Jingzhou Ye

dblp:368/3587 · DBLP profile ↗
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6ranked-venue papers
4as first author
6since 2021 · last 2026
0009-0006-6564-4386ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 4 · 2 first-author · 4 since 2021Human-computer interaction and ubiquitous computing · 2 · 2 first-author · 2 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.

Network and information security
4 papers
Privacy and data protection · 60% Usable security · 40%
Human-computer interaction and pervasive computing
2 papers
Human-AI interaction · 79% Learning and educational technologies · 21%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 5 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction
generative AI
1.012026
When Designers Meet GenAI: Understanding the Role of Prompt-to-Design Generators in Privacy Dark Patterns · SP 2026
Privacy and data protection › privacy compliance › consent
consent management
1.012026
Understanding and Analyzing Privacy Risks in Mobile Consent-Management Platforms · SP 2026
Privacy and data protection
privacy risk assessment
1.012026
Understanding and Analyzing Privacy Risks in Mobile Consent-Management Platforms · SP 2026
Privacy and data protection
privacy regulation
0.912025
Privacy Law Enforcement Under Centralized Governance: A Qualitative Analysis of Four Years' Special Privacy Rectification Campaigns · USENIX Security Symposium 2025
Learning and educational technologies › active learning
experiential learning
0.312025
From Awareness to Action: The Effects of Experiential Learning on Educating Users about Dark Patterns · CHI 2025

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

generative AI · 2.0qualitative analysis · 1.7experiential learning · 1.7privacy risk analysis · 1.0
YearPublicationVenuePosition
2026 Understanding and Analyzing Privacy Risks in Mobile Consent-Management Platforms
Jingzhou Ye, Fares Alharbi, Luyi Xing, Xueqiang Wang
SP1
2026 When Designers Meet GenAI: Understanding the Role of Prompt-to-Design Generators in Privacy Dark Patterns
Jingzhou Ye, Zhaojie Hu, Yao Li 0006, Xueqiang Wang
SP1
2025 From Awareness to Action: The Effects of Experiential Learning on Educating Users about Dark Patterns
Jingzhou Ye, Yao Li 0006, Wenting Zou, Xueqiang Wang
CHI1
2025 Privacy Law Enforcement Under Centralized Governance: A Qualitative Analysis of Four Years' Special Privacy Rectification Campaigns
Jingzhou Ye, Xueqiang Wang
USENIX Security Symposium3
2024 Seeing is Not Always Believing: An Empirical Analysis of Fake Evidence Generators
abstract
Online scams pose a growing threat to the cyberspace, with cybercriminals frequently using fake evidence, such as identification and financial documents, to illicitly elevate their credibility in online activities. This deceptive trend is fueled by an emerging set of fake evidence generators (FEGens). These FeGensreplicate the output of authoritative sources, such as official bank applications, to automatically generate large quantities of authentic-looking fake evidence. To the best of our knowledge, FeGens,as effective tools for cybercriminals, have not been systematically analyzed in terms of their supply chain, including development, promotion, and delivery, as well as the risks and impacts they pose to end users. In this paper, we present the first systematic empirical analysis of FegEnsand related fake evidence. Our findings shed light on the FegEn ecosystem, particularly the tactics employed by FegEndevelopers and retailers to mimic authoritative sources and promote the use of FeGens. We also evaluate the effectiveness of FeGensand associated risks in cybercrime.
Zhaojie Hu, Jingzhou Ye, Xueqiang Wang
EuroS&P2
2023 Efficient and Reliable Federated Recommendation System in Temporal Scenarios
Jingzhou Ye, Hui Lin 0007, Xiaoding Wang 0001, Chen Dong 0002, Jianmin Liu
GPC (2)1