Authenticating location-based skyline queries in arbitrary subspaces

Xin Lin, Jianliang Xu, Haibo Hu, Wang-chien Lee

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

With the ever-increasing use of smartphones and tablet devices, location-based services (LBSs) have experienced explosive growth in the past few years. To scale up services, there has been a rising trend of outsourcing data management to Cloud service providers, which provide query services to clients on behalf of data owners. However, in this data-outsourcing model, the service provider can be untrustworthy or compromised, thereby returning incorrect or incomplete query results to clients, intentionally or not. Therefore, empowering clients to authenticate query results is imperative for outsourced databases. In this paper, we study the authentication problem for location-based arbitrary-subspace skyline queries (LASQs), which represent an important class of LBS applications. We propose a basic Merkle Skyline R-tree method and a novel Partial S4-tree method to authenticate one-shot LASQs. For the authentication of continuous LASQs, we develop a prefetching-based approach that enables clients to compute new LASQ results locally during movement, without frequently contacting the server for query re-evaluation. Experimental results demonstrate the efficiency of our proposed methods and algorithms under various system settings.

Original languageEnglish (US)
Article number6574865
Pages (from-to)1479-1493
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume26
Issue number6
DOIs
StatePublished - Jan 1 2014

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Location based services
Outsourcing
Authentication
Smartphones
Information management
Servers

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

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Authenticating location-based skyline queries in arbitrary subspaces. / Lin, Xin; Xu, Jianliang; Hu, Haibo; Lee, Wang-chien.

In: IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 6, 6574865, 01.01.2014, p. 1479-1493.

Research output: Contribution to journalArticle

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