Abstract
Wi-Fi localization has become an essential service for many aspects of life, especially for indoor-environment where GPS-based technology cannot operate. SIL, a new family of Wi-Fi localization algorithms, has been introduced recently. SIL stands out from the rest of the localization techniques thanks to its training-free property. Capable of performing localization without pre-trained data, SIL resolves the costly training-phase commonly presenting in most other Wi-Fi localization algorithms. SIL can either operate independently or use crowd-sourcing to query and share preprocessed location information. The latter saves the bandwidth cost but poses a security threat of user’s location leakage. In this paper, we propose LOF, a framework to secure location anonymity while preserving acceptable-bandwidth-cost for training-free localization algorithms such as SIL.
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References
Le, T., Doan, T., Dinh, H., Nguyen, N.: Instant Search-based Indoor Localization. In: IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, Nevada, USA, pp. 143–148 (2013)
Doan, T.M., Dinh, H.N., Nguyen, N.T.: CGSIL: Collaborative Geo-clustering Search-based Indoor Localization. Accepted in the 16th IEEE International Conference on High Performance Computing and Communications (HPCC), Paris, France (2014)
Le, T.D., et al.: Convert Wi-Fi Signals for Fingerprint Localization Algorithm. In: Proc. IEEE Int. Conf. on Wireless Communication, Networking and Mobile Computing (WiCOM 2011), Wuhan, China, Session 12, pp. 1–5 (2011)
Sweeney, L.: k-Anonymity: A Model for Protecting Privacy. International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems, 557–570 (2002)
Kalnis, P., Ghinita, G., Mouratidis, K., Papadias, D.: Preventing Location-Based Identity Inference in Anonymous Spatial Queries. IEEE Transactions on Knowledge and Data Engineering 19(12), 1719–1733 (2007)
Gedik, B., Liu, L.: A Customizable k-Anonymity Model for Protecting Location Privacy. In: ICDCS, pp. 620–629 (2004)
Zhong, G., Hengartner, U.: A Distributed k-Anonymity Protocol for Location Privacy. In: IEEE Int. Conference on Pervasive Computing and Communications (PerCom), pp. 1–10 (2009)
Gedik, B., Liu, L.: Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms. IEEE Transactions on Mobile Computing 7(1) (2008)
Gkoulalas–Divanis, A., Kalnis, P., Verykios, V.S.: Providing K–Anonymity in Location Based Services. SIGKDD Explorations 12(1)
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Doan, T.M., Dinh, H.N., Nguyen, N.T., Tran, P.T. (2014). Location Obfuscation Framework for Training-Free Localization System. In: Prakash, A., Shyamasundar, R. (eds) Information Systems Security. ICISS 2014. Lecture Notes in Computer Science, vol 8880. Springer, Cham. https://doi.org/10.1007/978-3-319-13841-1_27
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DOI: https://doi.org/10.1007/978-3-319-13841-1_27
Publisher Name: Springer, Cham
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