Wi-Fi received signal strength (RSS) fingerprint-based indoor positioning has been widely used because of its low cost and universality advantages. However, the Wi-Fi RSS is greatly affected by multipath interference in indoor environments, which can cause significant errors in RSS observations. Many methods have been proposed to overcome this issue, including the average method and the error handling method, but these existing methods do not consider the ever-changing dynamics of RSS in indoor environments. In addition, traditional RSS-based clustering algorithms have been proposed in the literature, but they make clusters without considering the nonlinear similarity between reference points (RPs) and the signal distribution in ever-changing indoor environments. Therefore, to improve the positioning accuracy, this paper presents an improved RSS measurement technique (IRSSMT) to minimize the error of RSS observation by using the number of selected RSS and its median values, and the strongest access point (SAP) information-based clustering technique, which groups the RPs using their SAP similarity. The performance of this proposed method is tested by experiments conducted in two different experimental environments. The results reveal that our proposed method can greatly outperform the existing algorithms and improve the positioning accuracy by 89.06% and 67.48%, respectively.

K-anonymization generated a cloaked region (CR) that was K-anonymous; that is, the query issuer was indistinguishable from K-1 other users (nearest neighbors) within the CR. This reduced the probability of the query issuer’s location being exposed to untrusted parties (1/K). However, location cloaking is vulnerable to query tracking attacks, wherein the adversary can infer the query issuer by comparing the two regions in continuous LBS queries. This paper proposes a novel location cloaking method to resist this attack. The target systems of the proposed method are road networks where the mobile clients’ trajectories are fixed (the road network is preknown and fixed, instead of the trajectories), such as subways, railways, and highways. The proposed method, called adaptive-fixed K-anonymization (A-KF), takes this issue into account and generates smaller CRs without compromising the privacy of the query issuer’s location. Our results show that the proposed A-KF method outperforms previous location cloaking methods.