With the widespread adoption of location-based services and the increasing demand for indoor positioning systems, the need to protect indoor location privacy has become crucial. One metric used to assess a dataset’s resistance against leaking individuals’ information is the Membership Inference Attack (MIA). In this paper, we provide a comprehensive examination of MIA on indoor location privacy, evaluating their effectiveness in extracting sensitive information about individuals’ locations. We investigate the vulnerability of indoor location datasets under white-box and black-box attack settings. Additionally, we analyze MIA results after employing Differential Privacy (DP) to privatize the original indoor location training data. Our findings demonstrate that DP can act as a defense mechanism, especially against black-box MIA, reducing the efficiency of MIA on indoor location models. We conduct extensive experimental tests on three real-world indoor localization datasets to assess MIA in terms of the model architecture, the nature of the data, and the specific characteristics of the training datasets.

Feature selection has become significantly important for data analysis. It selects the most informative features describing the data to filter out the noise, complexity, and over-fitting caused by less relevant features. Accordingly, feature selection improves the predictors’ accuracy, enables them to be trained faster and more cost-effectively, and provides a better understanding of the underlying data. While plenty of practical solutions have been proposed in the literature to identify the most discriminating features describing a dataset, an understanding of feature selection over privacy-sensitive data in the absence of a trusted party is still missing. The design of such a framework is specifically important in our modern society, where each individual through accessing the Internet can play simultaneously the role of a data provider and a data-analysis beneficiary. In this study, we propose a novel feature selection framework based on Local Differential Privacy (LDP), named LDP-FS, which estimates the importance of features over securely protected data while protects the confidentiality of each individual data before leaving the user’s device. The performance of LDP-FS in terms of scoring and ordering the features is assessed by investigating the impact of datasets properties, privacy mechanism, privacy levels, and feature selection techniques on this framework. The accuracy of classifiers trained on the selected subset of features by LDP-FS is also presented. Our experimental results demonstrate the effectiveness and efficiency of the proposed framework.

Location fingerprinting is a technique employed when Global Positioning System (GPS) positioning breaks down within indoor environments. Since Location Service Providers (LSPs) would implicitly have access to such information, preserving user privacy has become a challenging issue in location estimation systems. This paper proposes a low-complexity k-anonymity approach for preserving the privacy of user location and trajectory, in which real location/trajectory data is hidden within k fake locations/trajectories held by the LSP, without degrading overall localization accuracy. To this end, three novel location privacy preserving methods and a trajectory privacy preserving algorithm are outlined. The fake trajectories are generated so as to exhibit characteristics of the user’s real trajectory. In the proposed method, no initial knowledge of the environment or location of the Access Points (APs) is required in order for the user to generate the fake location/trajectory. Moreover, the LSP is able to preserve privacy of the fingerprinting database from the users. The proposed approaches are evaluated in both simulation and experimental testing, with the proposed methods outperforming other well-known k-anonymity methods. The method further exhibits a lower implementation complexity and higher movement similarity (of up to 88%) between the real and fake trajectories.

Due to lack of GPS signals' indoor coverage, the implementation of Signals of Opportunity (SoOp) such as FM signals, Wi-Fi, Bluetooth, RFID, and Cellular Networks for indoor localization has been considered. Not all SoOp are good performers because of some constraints; for instance, lack of accuracy, higher cost in deployment, additional hardware requirements. In this research, a new method of indoor localization is developed using AM radio signalsand thereceived signal strength fingerprinting technique. The fingerprinting technique in this study uses the deterministic approach and three algorithms, Nearest Neighbour, K-Nearest Neighbour, and K-Weighted Nearest Neighbour to measure the mean distance error as the indicator of positioning accuracy. The outcome shows that the minimum mean distance error at the ground and first floor is less than 3m and the Nearest Neighbour algorithm performed best with K=1.

With increasing user demands on Location-based Services (LBS) and Social Networking Services (SNS), indoor positioning has become more crucial. Because of the general failure of GPS indoors, non-GNSS navigation technologies are essential for such areas. Wireless Local Area Networks (WLAN) have widely been employed for indoor localisation based on the Received Signal Strength (RSS)-based location fingerprinting technique. The fingerprinting technique stores the location-dependent characteristics of a signal collected at known locations ahead of the system's use for localisation in a database. When positioning, the user's device records its own vector(s) of signal strength and matches it against the pre-recorded database of vectors by applying pattern matching algorithms. Location is then calculated based on the best matches between the new and stored vectors. We examined the relationship between the measured Manhattan Distance (MD), Euclidean Distance (ED), and other vector distances over the geometric distance between Reference Points (RPs) in a fingerprint database. The correlation between geometric and vector distance was poor. However, because “nearest neighbor” algorithms are used, only short vector distances are important. Furthermore, the measured RSSs varied much more as a function of distance (due to fast fading) than it did as a function of time at a single test point. Hence, the difference between variances measured at two test points was not a good indicator of the measured difference in signal strength. This led to the current investigation of very short geometric distances. In this paper, a new algorithm is applied to examine data from locations at very short ranges from each other and to investigate the relationship between vector distance and the geometric distance in closer areas in order to observe the nature of the relationship between short-range fingerprints. The experimental test bed was carried out in a large furnished office. Two west-east and south-north lines in a cross shape with 4m length are considered. We find that even at short distances, variation due to fading dominates and using other vector distances instead of MD or ED can help decrease the effect of such variation in positioning.

Indoor positioning has become highly important because of the failure of GPS in such areas. Many Wireless Local Area Networks (WLAN) indoor localization studies use the fingerprinting technique. In this study, a new positioning system is proposed based on broadcast FM as a signal of opportunity, with significant benefits for indoor positioning. This localization system uses FM signal strength fingerprinting. The deterministic approach of fingerprinting is considered, and several algorithms are compared. The results demonstrate a minimum mean distance error of 2.96m for the K-Weighted Nearest Neighbors (KWNN) algorithm with K=6. The comparison between using fingerprinting for FM and Wi-Fi is also discussed.