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An Efficient Differential Privacy-Based Method for Location Privacy Protection in Location-Based Services

Sensors (Basel, Switzerland), 2023-05, Vol.23 (11), p.5219 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2023 by the authors. 2023 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s23115219 ;PMID: 37299946

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  • Title:
    An Efficient Differential Privacy-Based Method for Location Privacy Protection in Location-Based Services
  • Author: Wang, Bo ; Li, Hongtao ; Ren, Xiaoyu ; Guo, Yina
  • Subjects: Accuracy ; Algorithms ; Centroids ; cluster model ; Clustering ; differential privacy ; Global positioning systems ; GPS ; Identity theft ; Location based services ; location privacy protection ; Methods ; Mobile devices ; Performance degradation ; Privacy ; Privacy, Right of ; Semantics ; Social networks
  • Is Part Of: Sensors (Basel, Switzerland), 2023-05, Vol.23 (11), p.5219
  • Description: Location-based services (LBS) are widely used due to the rapid development of mobile devices and location technology. Users usually provide precise location information to LBS to access the corresponding services. However, this convenience comes with the risk of location privacy disclosure, which can infringe upon personal privacy and security. In this paper, a location privacy protection method based on differential privacy is proposed, which efficiently protects users' locations, without degrading the performance of LBS. First, a location-clustering (L-clustering) algorithm is proposed to divide the continuous locations into different clusters based on the distance and density relationships among multiple groups. Then, a differential privacy-based location privacy protection algorithm (DPLPA) is proposed to protect users' location privacy, where Laplace noise is added to the resident points and centroids within the cluster. The experimental results show that the DPLPA achieves a high level of data utility, with minimal time consumption, while effectively protecting the privacy of location information.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s23115219
    PMID: 37299946
  • Source: DOAJ Directory of Open Access Journals
    Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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