Location Obfuscation Framework for Training-Free Localization System
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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