Demo Data from SIGIR 2026 / reviewers in the wild / expert
Guozhang He
dblp:349/7122
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 2 · 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
2 papers |
Privacy and data protection · 88% Hardware security and side channels · 12% | |
| Databases, data mining, and information retrieval
1 paper |
Spatial and temporal data management · 50% Query processing and optimization · 50% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection
privacy-preserving machine learning |
1.0 | 1 | 2026 | Collusion-Resistant Privacy-Preserving Outsourced Training Under Single Cloud With Semi-Honest TEE · IEEE Trans. Dependable Secur. Comput. 2026 |
Query processing and optimization › secure query processing
encrypted query processing |
0.7 | 1 | 2023 | Efficient and Privacy-Preserving Arbitrary Polygon Range Query Scheme Over Dynamic and Time-Series Location Data · IEEE Trans. Inf. Forensics Secur. 2023 |
Spatial and temporal data management › spatial query processing
spatial range query |
0.7 | 1 | 2023 | Efficient and Privacy-Preserving Arbitrary Polygon Range Query Scheme Over Dynamic and Time-Series Location Data · IEEE Trans. Inf. Forensics Secur. 2023 |
Privacy and data protection
location privacy |
0.7 | 1 | 2023 | Efficient and Privacy-Preserving Arbitrary Polygon Range Query Scheme Over Dynamic and Time-Series Location Data · IEEE Trans. Inf. Forensics Secur. 2023 |
Privacy and data protection
privacy-preserving query processing |
0.7 | 1 | 2023 | Efficient and Privacy-Preserving Arbitrary Polygon Range Query Scheme Over Dynamic and Time-Series Location Data · IEEE Trans. Inf. Forensics Secur. 2023 |
Hardware security and side channels
trusted execution environments |
0.3 | 1 | 2026 | Collusion-Resistant Privacy-Preserving Outsourced Training Under Single Cloud With Semi-Honest TEE · IEEE Trans. Dependable Secur. Comput. 2026 |
Methods — techniques the papers use, named apart from their topics
symmetric homomorphic encryption · 1.3minimum bounded rectangle · 1.3secure computation · 1.0TEE · 1.0quadtree index · 0.7quad-tree index · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Collusion-Resistant Privacy-Preserving Outsourced Training Under Single Cloud With Semi-Honest TEE
Wei Xu 0042, Hui Zhu 0001, Guozhang He, Jingqiang Lin 0001, Jiaqi Zhao 0005, Rongxing Lu, Dengguo Feng |
IEEE Trans. Dependable Secur. Comput. | 3 |
| 2023 | Efficient and Privacy-Preserving Arbitrary Polygon Range Query Scheme Over Dynamic and Time-Series Location DataabstractLocation-based services (LBSs) provide enhanced functionality of mobile applications and convenience for mobile users, which plays a more and more remarkable role in people’s daily life. In LBSs, spatial range query is an essential tool for users to find interesting points in a specific region. However, during spatial range query, it is necessary for data owners and query users to exchange their location data, and the leakage of private location information has drawn significant attention in both governmental and social aspects. Meanwhile, most existing location privacy protection schemes only focus on achieving regular geometry range query over static location datasets. In this paper, we present an efficient and privacy-preserving arbitrary polygon range query scheme, named EPAPRQ. With EPAPRQ, the arbitrary and fine-grained polygon range query can be executed over a dynamic and time-series location dataset with privacy protection. Specifically, in EPAPRQ, an arbitrary polygon range query algorithm is first introduced with sub-range query technique. Then, to protect the private location information of the data owner and query users, a series privacy-preserving data computation protocols are constructed with a symmetric homomorphic encryption algorithm, and a ciphertext-based location dataset updating strategy is also designed. Finally, we propose a double filtration method through combining the quadtree index structure and minimum bounded rectangle, which is able to greatly improve the query efficiency over ciphertexts. Detailed security analysis shows that the sensitive location information in EPAPRQ can be well protected. Furthermore, we evaluate the performance of EPAPRQ in the real map, and the results demonstrate that EPAPRQ is indeed efficient. Fengwei Wang, Hui Zhu 0001, Guozhang He, Rongxing Lu, Yandong Zheng, Hui Li 0006 |
IEEE Trans. Inf. Forensics Secur. | 3 |