Privacy preserving in indoor fingerprint localization and radio map expansion

Article


Ghorashi, S. A., Sazdar, A. M., Alikhani, N. and Khonsari, A. 2020. Privacy preserving in indoor fingerprint localization and radio map expansion. Peer-to-Peer Networking and Applications. 14, p. 121–134. https://doi.org/10.1007/s12083-020-00950-1
AuthorsGhorashi, S. A., Sazdar, A. M., Alikhani, N. and Khonsari, A.
Abstract

People spend most of their life time in indoor environments and in all of these environments, Location Service Providers (LSPs) improve users’ navigation. Preserving privacy in Location Based Services (LBSs) is vital for indoor LBSs and fingerprinting based indoor localization method is an emerging technique in indoor localization. In such systems, LSP may be curious and untrusted. Therefore, it is preferred that user estimates its location by using a Partial Radio Map (PRM) which is achieved by LSP, anonymously. In this paper, a privacy preserving method that uses Bloom filter for preserving anonymity and creating PRM during localization process, is proposed. In this method, LSP cannot recognize user identity, which is anonymized by the anonymizer. The proposed method has lower computational complexity compared with methods that use encryption or clustering concepts. The proposed method also has higher accuracy in localization compared with those that use Bloom filter with one random selected AP. Then, in order to decrease the complexity and to increase the accuracy at the same time, we introduce a method that expands the radio map by authenticated users, without compromising their privacy. We also enhance the performance of this method, using Hilbert curve for preserving the ambiguity of users’ location. After verifying the user’s data, LSP sends a certificate to the authenticated users. This certificate can increase the priority of users in LBS requests. Simulation results and measurements show that the proposed method on average improves the localization accuracy up to 16% compared with existing location privacy methods.

KeywordsPrivacy preserving; Partial radio map; Indoor fingerprint localization; Bloom filter; Hilbert curve
JournalPeer-to-Peer Networking and Applications
Journal citation14, p. 121–134
ISSN1936-6442
Year2020
PublisherSpringer
Accepted author manuscript
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1007/s12083-020-00950-1
Publication dates
Print10 Jul 2020
Publication process dates
Accepted15 Jun 2020
Deposited13 Jul 2020
Copyright holder© 2020 Springer Nature
Copyright informationThis is a post-peer-review, pre-copyedit version of an article published in Peer-to-Peer Networking and Applications. The final authenticated version is available online at: https://doi.org/10.1007/s12083-020-00950-1.
Permalink -

https://repository.uel.ac.uk/item/88352

Download files


Accepted author manuscript
Main_Manuscript-1.pdf
License: Springer Nature terms of use for archived author accepted manuscripts (AAMs) of subscription articles, books and chapters
File access level: Anyone

  • 53
    total views
  • 21
    total downloads
  • 9
    views this month
  • 9
    downloads this month

Export as

Related outputs

Reconfigurable Linear Antenna Arrays for Beam-Pattern Matching in Collocated MIMO Radars
Kavousi Ghafi, E., Ghorashi, S. and Mehrshahi, E. 2021. Reconfigurable Linear Antenna Arrays for Beam-Pattern Matching in Collocated MIMO Radars. IEEE Transactions on Aerospace and Electronic Systems. 57 (5), pp. 2715-2724. https://doi.org/10.1109/TAES.2021.3062173
Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation
Navidan, H., Fard Moshiri, P., Nabati, M., Shahbazian, R., Ghorashi, S., Shah-Mansouri, V. and Windridge, D. 2021. Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation. Computer Networks. 194 (Art. 108149). https://doi.org/10.1016/j.comnet.2021.108149
Joint Coordinate Optimization in Fingerprint-Based Indoor Positioning
Nabati, M., Ghorashi, S. and Shahbazian, R. 2021. Joint Coordinate Optimization in Fingerprint-Based Indoor Positioning. IEEE Communications Letters. 25 (4), pp. 1192-1195. https://doi.org/10.1109/LCOMM.2020.3047352
A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machine
Ezzati Khatab, Z., Hajihoseini Gazestani, A., Ghorashi, S. and Ghavami, M. 2020. A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machine. Signal Processing. 181 (Art. 107915). https://doi.org/10.1016/j.sigpro.2020.107915
Joint Optimization of Power and Location in Full-Duplex UAV Enabled Systems
Gazestani A. H., Ghorashi, S. A., Yang, Z. and Shikh-Bahaei, M. 2020. Joint Optimization of Power and Location in Full-Duplex UAV Enabled Systems. IEEE Systems Journal. https://doi.org/10.1109/JSYST.2020.3036275
Fingerprinting Based Indoor Localization Considering the Dynamic Nature of Wi-Fi Signals
Alikhani, N., Moghtadaiee, V. and Ghorashi, S. 2020. Fingerprinting Based Indoor Localization Considering the Dynamic Nature of Wi-Fi Signals. Wireless Personal Communications. 115 (2), pp. 1445-1464. https://doi.org/10.1007/s11277-020-07636-0
Resource Allocation in Full-Duplex UAV Enabled Multi Small Cell Networks
Hajihoseini Gazestani, A., Ghorashi, S. A., Yang, Z. and Shikh-Bahaei, M 2020. Resource Allocation in Full-Duplex UAV Enabled Multi Small Cell Networks. IEEE Transactions on Mobile Computing. https://doi.org/10.1109/TMC.2020.3017137
A Low-complexity trajectory privacy preservation approach for indoor fingerprinting positioning systems
Sazdar, A. M., Ghorashi, S. A., Moghtadaiee, V., Khonsari, A. and Windridge, D. 2020. A Low-complexity trajectory privacy preservation approach for indoor fingerprinting positioning systems. Journal of Information Security and Applications. 53 (Art. 102515). https://doi.org/10.1016/j.jisa.2020.102515
Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach
Nabati, M., Navidan, H., Shahbazian, R., Ghorashi, S. A. and Windridge, D. 2020. Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach. IEEE Sensors Letters. 4 (Art. 6000204). https://doi.org/10.1109/LSENS.2020.2971555
Power Allocation for D2D Communications Using Max-Min Message-Passing Algorithm
Kazemi Rashed, S, Asvadi, R., Rajabi, S., Ghorashi, S. A. and Martini, M. G. 2020. Power Allocation for D2D Communications Using Max-Min Message-Passing Algorithm. IEEE Transactions on Vehicular Technology. 69 (8), pp. 8443-8458. https://doi.org/10.1109/TVT.2020.2995534
Throughput Improvement by Mode Selection in Hybrid Duplex Wireless Networks
Mousavinasab, B., Gazestani, A. H., Ghorashi, S. A. and Shikh-Bahaei, M. 2020. Throughput Improvement by Mode Selection in Hybrid Duplex Wireless Networks. Wireless Networks. 26, p. 3687–3699. https://doi.org/10.1007/s11276-020-02286-3
New Reconstructed Database for Cost Reduction in Indoor Fingerprinting Localization
Moghatdaiee, V., Ghorashi, S. and Ghavami, G. 2019. New Reconstructed Database for Cost Reduction in Indoor Fingerprinting Localization. IEEE Access. 7, pp. 104462-104477. https://doi.org/10.1109/ACCESS.2019.2932024