Abstract
Due to the extensive applicability of Location-Based Services (LBSs) and the Global Navigation Satellite System (GNSS) failure in indoor environments, indoor positioning systems have been widely implemented. Location fingerprinting, in particular, collects the Received Signal Strength (RSS) from users' devices, allowing Location Service Providers (LSPs) to precisely identify their locations. Therefore, LSPs and potential attackers have implicit access to this sensitive data, violating users' privacy. This issue has been addressed in outdoor environments by introducing Geo-indistinguishability (GeoInd), an alternative representation of Differential Privacy (DP). In indoor environments, however, the user lacks their coordinates, posing a new difficulty. This paper presents a novel framework for implementing GeoInd for indoor environments. The proposed framework introduces two distance calculation and RSS generation methods based solely on RSS values. Moreover, involving other participants or trusted third parties is not necessary to protect privacy, regardless of the attackers' prior knowledge. The proposed framework is evaluated in a simulated environment and two experimental settings. The results validate the proposed framework's efficiency, effectiveness, and applicability in indoor environments under the GeoInd setting.
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