Demo Data from EDBT 2026 / reviewers in the wild / expert
Lizhong Bai
dblp:340/0467
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0001-8435-9616ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 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
1 paper |
Privacy and data protection · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Cloud and datacenter computing · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Indexing and storage engines · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection
privacy-preserving query processing |
0.7 | 1 | 2023 | Privacy-Preserving Top-$k$k Spatial Keyword Queries in Fog-Based Cloud Computing · IEEE Trans. Serv. Comput. 2023 |
Privacy and data protection
spatial keyword query |
0.7 | 1 | 2023 | Privacy-Preserving Top-$k$k Spatial Keyword Queries in Fog-Based Cloud Computing · IEEE Trans. Serv. Comput. 2023 |
Cloud and datacenter computing › edge and fog computing
fog computing |
0.7 | 1 | 2023 | Privacy-Preserving Top-$k$k Spatial Keyword Queries in Fog-Based Cloud Computing · IEEE Trans. Serv. Comput. 2023 |
Indexing and storage engines
spatial index |
0.2 | 1 | 2023 | Privacy-Preserving Top-$k$k Spatial Keyword Queries in Fog-Based Cloud Computing · IEEE Trans. Serv. Comput. 2023 |
Methods — techniques the papers use, named apart from their topics
pruning · 2.0formal security analysis · 2.0IR-tree indexing · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Privacy-Preserving Top-$k$k Spatial Keyword Queries in Fog-Based Cloud ComputingabstractWith the popularity of location based services, spatial keyword query has become an important application. In order to save the storage and computational costs, most data owners will outsource the data to the cloud server, but this will lead to two problems, such as privacy leakage and heavy network bandwidth burden. To solve above problems, we propose a Privacy-preserving top-k Spatial Keyword queries based on Fog computing, namly PSKF. To further improve the search efficiency, we use the IR-tree to build the index and store it in the cloud server. Each fog server also saves a different subtree of the IR-tree, so that we can decide which fog server to participate in the query by pruning. Formal security analysis shows that our proposed PSKF achieves Indistinguishability under Known-Plaintext Attacks (IND-KPA), and extensive experiments demonstrate that our proposed scheme is efficient and feasible in practical applications. Xinghua Li 0001, Lizhong Bai, Yinbin Miao, Siqi Ma 0001, Jianfeng Ma 0001, Ximeng Liu, Kim-Kwang Raymond Choo |
IEEE Trans. Serv. Comput. | 2 |