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

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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-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.
Original languageEnglish
Title of host publicationProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2019
EditorsFarnoush Banaei Kashani, Goce Trajcevski, Ralf Hartmut Güting, Lars Kulik, Shawn D. Newsam
Number of pages10
PublisherAssociation for Computing Machinery
Publication date2019
Pages289-298
ISBN (Electronic)978-1-4503-6909-1
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - Chicago, United States
Duration: 5 Nov 20198 Nov 2019
https://sigspatial2019.sigspatial.org/

Conference

Conference27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
CountryUnited States
CityChicago
Period05/11/201908/11/2019
Internet address

Keywords

  • Indoor positioning system
  • Privacy-preserving queries

Cite this

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