Abstract
With the widespread deployment of IoT (Internet of Things) devices, WiFi fingerprint-based localization is becoming one of the most promising techniques for indoor localization. A client is able to obtain its location by providing its measured fingerprint (vector of WiFi signal strengths) to the service provider who maps the fingerprint against the database and returns the result back to the client. However, traditional applications of WiFi fingerprint-based localization may disclose the client’s location privacy and often incur high consumption of communication and computing resources. In this paper, we focus on implementing a privacy-preserving framework with high efficiency and accuracy for WiFi fingerprint-based localization. Firstly, to reduce computational overhead at the server side, we introduce a clustering algorithm called k-means++ in offline phase. Besides, we explore the correlation of the fingerprint and propose a Pearson correlation based distance computation method, which achieves better accuracy than traditional Euclidean distance. Finally, we secure the overall computation by adapting a series of secure multi-party computing primitives. Theoretical analysis is carried out to prove the security of our scheme. Experiments on real-world datasets indicate that our scheme achieves better practicality and efficiency compared with existing methods. Compared to existing work PriWFL and PPWFL, our scheme reduces the average distance error by approximately \(4.5\%\) and \(2.9\%\) under a query time of less than 0.2s.
This work was supported in part by the National Nature Science Foundation of China (No. 62102429, 62102422, 62072466, 61872372), and the NUDT Grants (No. ZK19-38).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lui, G., Gallagher, T., Li, B., Dempster, A.G., Rizos, C.: Differences in RSSI readings made by different Wi-Fi chipsets: a limitation of WLAN localization. In: 2011 International Conference on Localization and GNSS (ICL-GNSS), pp. 53–57 (2011). https://doi.org/10.1109/ICL-GNSS.2011.5955283
Sun, Y.L., Xu, Y.B.: Error estimation method for matrix correlation-based Wi-Fi indoor localization. KSII Trans. Internet Inf. Syst. 7(11), 2657–2675 (2013). https://doi.org/10.3837/tiis.2013.11.006
Chang, N., Rashidzadeh, R., Ahmadi, M.: Robust indoor positioning using differential Wi-Fi access points. IEEE Trans. Consum. Electron. 56(3), 1860–1867 (2010). https://doi.org/10.1109/TCE.2010.5606338
Shokri, R., Theodorakopoulos, G., Le Boudec, J.Y., Hubaux, J.P.: Quantifying location privacy. In: 2011 IEEE Symposium on Security and Privacy, pp. 247–262 (2011). https://doi.org/10.1109/SP.2011.18
Yang, Z., Järvinen, K.: The death and rebirth of privacy-preserving WiFi fingerprint localization with Paillier encryption. In: IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. pp. 1223–1231 (2018). https://doi.org/10.1109/INFOCOM.2018.8486221
Yang, D., Fang, X., Xue, G.: Truthful incentive mechanisms for k-anonymity location privacy. In: 2013 Proceedings IEEE INFOCOM, pp. 2994–3002 (2013). https://doi.org/10.1109/INFCOM.2013.6567111
Zheng, X., Cai, Z.: Privacy-preserved data sharing towards multiple parties in industrial IoTs. IEEE J. Sel. Areas Commun. 38(5), 968–979 (2020)
Li, H., Sun, L., Zhu, H., Lu, X., Cheng, X.: Achieving privacy preservation in WiFi fingerprint-based localization. In: IEEE INFOCOM 2014 - IEEE Conference on Computer Communications. pp. 2337–2345 (2014). https://doi.org/10.1109/INFOCOM.2014.6848178
Beaver, D.: Efficient multiparty protocols using circuit randomization. In: Feigenbaum, J. (ed.) CRYPTO 1991. LNCS, vol. 576, pp. 420–432. Springer, Heidelberg (1992). https://doi.org/10.1007/3-540-46766-1_34
Xia, Z., Gu, Q., Xiong, L., Zhou, W., Weng, J.: Privacy-preserving image retrieval based on additive secret sharing. ArXiv abs/2009.06893 (2020)
Zheng, Z., Chen, Y., He, T., Li, F., Chen, D.: Weight-RSS: a calibration-free and robust method for WLAN-based indoor positioning. Int. J. Distrib. Sens. Netw. 11(4), 573582 (2015)
Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: SODA ’07 (2007)
Biber, D.: Pearson correlation coefficients for all linguistic features, p. 270–279. Cambridge University Press (1988). https://doi.org/10.1017/CBO9780511621024.013
Bogdanov, D., Laur, S., Willemson, J.: Sharemind: a framework for fast privacy-preserving computations. In: Jajodia, S., Lopez, J. (eds.) ESORICS 2008. LNCS, vol. 5283, pp. 192–206. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88313-5_13
Huang, K., Liu, X., Fu, S., Guo, D., Xu, M.: A lightweight privacy-preserving CNN feature extraction framework for mobile sensing. IEEE Trans. Dependable Secure Comput. 18(3), 1441–1455 (2021). https://doi.org/10.1109/TDSC.2019.2913362
Wu, W., Fu, S., Luo, Y.: Practical privacy protection scheme in WiFi fingerprint-based localization. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 699–708 (2020). https://doi.org/10.1109/DSAA49011.2020.00080
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, X., Luo, Y., Xu, M., fu, S., Chen, Y. (2022). Privacy-preserving WiFi Fingerprint Localization Based on Spatial Linear Correlation. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13471. Springer, Cham. https://doi.org/10.1007/978-3-031-19208-1_33
Download citation
DOI: https://doi.org/10.1007/978-3-031-19208-1_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19207-4
Online ISBN: 978-3-031-19208-1
eBook Packages: Computer ScienceComputer Science (R0)