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Kang Qian

dblp:35/4311 · DBLP profile ↗
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3ranked-venue papers
0as first author
2since 2021 · last 2024
—ORCID · unresolved

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

Databases, data management, data science and information retrieval · 2 · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1

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 · 50% Blockchain and cryptocurrency security · 50%
Databases, data mining, and information retrieval
1 paper
Spatial and temporal data management · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Spatial and temporal data management › spatial query processing › nearest neighbor query
k-nearest neighbor query
0.712023
Towards Multi-User, Secure, and Verifiable $k$NN Query in Cloud Database · IEEE Trans. Knowl. Data Eng. 2023
Blockchain and cryptocurrency security
verifiable query processing
0.712023
Towards Multi-User, Secure, and Verifiable $k$NN Query in Cloud Database · IEEE Trans. Knowl. Data Eng. 2023
Cloud and datacenter computing › database-as-a-service
outsourced database
0.212023
Towards Multi-User, Secure, and Verifiable $k$NN Query in Cloud Database · IEEE Trans. Knowl. Data Eng. 2023

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

verification strategy · 2.0symmetric encryption · 2.0secure index · 2.0
YearPublicationVenuePosition
2024 Searching Untrusted Clouds Meets Multiple Keys: Privacy-Preserving Spatio-Textual Top-k Query
Ningning Cui, Dong Wang 0057, Yuliang Ma 0001, Mengxiang Wang, Kang Qian, Jianxin Li 0001
DASFAA (4)5
2023 Towards Multi-User, Secure, and Verifiable $k$NN Query in Cloud Database
abstract
With the boom in cloud computing, data outsourcing in location-based services is proliferating and has attracted increasing interest from research communities and commercial applications. Nevertheless, since the cloud server is probably both untrusted and malicious, concerns about data security and result integrity have become on the rise sharply. In addition, in the single-user situation assumed by most existing works, query users can capture query content from each other even though the queries are encrypted, which may incur the leakage of query privacy. Unfortunately, there exists little work that can commendably assure data security and result integrity in the multi-user setting. To this end, in this article, we study the problem of multi-user, secure, and verifiable$k$nearest neighbor query (MSV$k$kNN). To support MSV$k$NN, we first propose a novel unified structure, called verifiable and secure index (VSI). Based on this, we devise a series of secure protocols to facilitate query processing and develop a compact verification strategy. Given an MSV$k$NN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preservingdata privacy,query privacy,result privacy, andaccess patterns privacy; 2) authenticating thecorrectnessandcompletenessof the results; 3) supportingmulti-userwith different keys. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approach are empirically evaluated and demonstrated.
Ningning Cui, Kang Qian, Taotao Cai, Jianxin Li 0001, Xiaochun Yang 0001, Jie Cui 0004, Hong Zhong 0001
IEEE Trans. Knowl. Data Eng.2
2001 Optimising the four-arc approximation to ellipses
Wen-Han Qian, Kang Qian
Comput. Aided Geom. Des.2