On Location Privacy in Fingerprinting-based Indoor Positioning System: An Encryption Approach

Wenlu Wang, Zhitao Gong, Ji Zhang, Hua Lu, Wei-Shinn Ku

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

Abstract

Due to the inadequacy of GPS signals in indoor spaces, Indoor Positioning Services (IPSs) have drawn great attention. The popular smartphone localization technique relies on a centralized server to achieve localization, allowing the server to acquire a user's location in fine granularity. To ensure the privacy of IPS users, we propose an Encrypted Indoor Positioning Service (EIPS) model that protects users' privacy from the centralized server and maintains localization accuracy simultaneously. Our EIPS model enables users to encrypt and decrypt their query through an Encryption and Decryption Server (EDS) bi-directionally in a commutative way, so the users' locations remain private to both EIPS and EDS. We also propose Query Split, Artificial Dimensions and Columns to prevent Known Plaintext Attack (KPA). Our analytical and experimental evaluations show that our model is resilient to a variety of privacy attacks without loss of efficiency and accuracy.
OriginalsprogEngelsk
TitelProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2019
RedaktørerFarnoush Banaei Kashani, Goce Trajcevski, Ralf Hartmut Güting, Lars Kulik, Shawn D. Newsam
Antal sider10
ForlagAssociation for Computing Machinery
Publikationsdato2019
Sider289-298
ISBN (Elektronisk)978-1-4503-6909-1
DOI
StatusUdgivet - 2019
Udgivet eksterntJa
Begivenhed27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - Chicago, USA
Varighed: 5 nov. 20198 nov. 2019
https://sigspatial2019.sigspatial.org/

Konference

Konference27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
LandUSA
ByChicago
Periode05/11/201908/11/2019
Internetadresse

Emneord

  • Indoor positioning system
  • Privacy-preserving queries

Citer dette

Wang, W., Gong, Z., Zhang, J., Lu, H., & Ku, W-S. (2019). On Location Privacy in Fingerprinting-based Indoor Positioning System: An Encryption Approach. I F. B. Kashani, G. Trajcevski, R. H. Güting, L. Kulik, & S. D. Newsam (red.), Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2019 (s. 289-298). Association for Computing Machinery. https://doi.org/10.1145/3347146.3359081