VLDB 2026 Research / reviewers in the wild / expert
Yue Quan
dblp:185/7237
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2025
0009-0009-7394-4882ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Private Spatial Range Queries Over Outsourced Data: A Survey
Haoyang Wang 0005, Wei Hu 0008, Yue Quan |
IEEE Big Data | 3 |
| 2024 | VDPSRQ: Achieving Verifiable and Dynamic Private Spatial Range Queries over Outsourced Database
Haoyang Wang 0005, Kai Fan 0001, Yue Quan, Fenghua Li 0001, Hui Li 0006 |
TrustCom | 3 |
| 2024 | DMASP: Dynamic Multi-keyword Searchable Encryption for Protected Access and Search Patterns with Differential PrivacyabstractIn recent years, cloud computing services have grown rapidly, with people outsourcing huge amounts of private data to cloud servers. Searchable encryption(SE) facilitates people’s use of data while protecting data privacy. To balance the efficiency, current SE schemes still leak information such as access, search and volume patterns to cloud servers, and most dynamic SE schemes leak forward and backward privacy, and these leaks create serious security threats. In this paper, we propose a dynamic SE scheme DMASP with suppressed leakage, which protects access pattern, search pattern and volume pattern simultaneously. We utilize the differential privacy mechanism to obfuscate databases, and introduce the CKKS algorithm to protect access and volume patterns during queries. Meanwhile, we design a secure structure to index databases efficiently, and protect forward and backward privacy during updates. We rigorously analyse the security of DMASP. Furthermore, comprehensive experimental evaluation show that DMASP can reduce the accuracy of attacks against patterns leakage in real-world databases. Yue Quan, Kai Fan 0001, Haoyang Wang 0005, Hui Li 0006, Yintang Yang |
TrustCom | 1 |