WLAN-based localization is widely adopted for mobile positioning, which is the prerequisite for location based services and more. However, the existing positioning systems are vulnerable to location spoofing attacks, which may bring major privacy concerns to mobile social network service (MSNS). In this paper, we first show a privacy attack model base on spoofing attack in MSNS, and then propose a novel defense mechanism based on WiFi-hotspot tags (i.e. base-station tags, BS tags). Specially, we utilize the unpredictability and reproducibility of BS tags to authenticate the spatial-temporal property of a geolocation. Furthermore, we introduce the bloom filter to compress parts of real-time hotspots frames, while guaranteeing its high entropy. Also, we design a tag verification algorithm based on the fuzzy extractors, which can well adapt to the high-bit error rates of wireless transmission. Finally, the safety and feasibility of the mechanism are proved by theoretical and experimental analysis.

With the fast development of the Internet of Things (IoT), the growing amounts of data transmitted through edge devices tempt hackers to attack vulnerabilities. Because of data fragmentation and heterogeneous data distribution of IoT, attack detection models on the edge devices are proficient at detecting only a limited set of specific attacks, causing a high false alarm rate when detecting new traffic data. personalized federated learning (PFL) widely expands the range of detectable attacks and adapts local models to new traffic data by one step of gradient descent. However, it demands a part of the new traffic data (test-support set) with correct labels to realize adaptation, which is labor-consuming when detecting large amounts of traffic data. To solve this issue, our main idea is to find helpful models to prelabel the test-support set, we propose a novel self-labeled PFL called SOH-FL, including an autoencoder based on cosine similarity (CT-AE) to extract features and an aggregation method (BS-Agg) to tailor models for prelabeling test-support sets depending on features extracted from edge devices. SOH-FL is evaluated in three heterogeneous scenarios using the CICIDS2017 dataset, and consistently outperforms the baselines across all metrics, achieving performance comparable to PFL without manual labeling. In the real-world feature heterogeneous scenarios of the IoT-23 and TON-IoT datasets, SOH-FL achieves accuracy improvements of 11.5% and 9.1% over the baseline, respectively. The experimental code is publicly available at https://github.com/deer-echo/SOH-FL.git.

The increasing abundance of data in the trajectories of personal movement opens up new opportunities for analyzing and mining human mobility. But the inherent personal information in the data raises the privacy concern. In this paper, the trajectories are not simply considered as a sequence of the coordinates in Euclidean space, they combine the semantics-aware information with the background knowledge of underlying map for the location points. A novel approach is then proposed to conceal the actually visited sensitive places. To this end, a cloaking region for each personalized sensitive place is built before a reasonable dummy trajectory in the cloaking region is constructed. The performance of our approach is evaluated based on a real-world trajectories dataset. And the results show that the proposed approach outperforms the existing privacy-preserving approaches.

Received signal strength (RSS) in wireless networks is widely adopted for indoor positioning purpose because of its low cost and open access properties. However due to the sophisticated propagation of radio signals, the RSS shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, the authors present a novel method to improve the indoor pedestrian positioning accuracy by modelling fingerprinting and information on the movement into a hidden Markov models (HMMs). They divide the whole continuous positioning process into specified‐size sub‐processes, which could efficiently reduce the accumulative and resonance error caused by iterative estimation. They use an accelerometer sensor to provide the information on the movement distance to calculate the transition probability of the HMMs. In their experiments, they demonstrate that, compared with the deterministic pattern matching algorithm, the proposed method greatly improves the positioning accuracy and shows robust environmental adaptability.

A growing amount of data containing the sensitive information of users is being collected by emerging smart connected devices to the center server in Internet of things (IoT) era, which raises serious privacy concerns for millions of users. However, existing perturbation methods are not effective because of increased disclosure risk and reduced data utility, especially for small data sets. To overcome this issue, we propose a new edge perturbation mechanism based on the concept of global sensitivity to protect the sensitive information in IoT data collection. The edge server is used to mask users' sensitive data, which can not only avoid the data leakage caused by centralized perturbation, but also achieve better data utility than local perturbation. In addition, we present a global noise generation algorithm based on edge perturbation. Each edge server utilizes the global noise generated by the center server to perturb users' sensitive data. It can minimize the disclosure risk while ensuring that the results of commonly performed statistical analyses are identical and equal for both the raw and the perturbed data. Finally, theoretical and experimental evaluations indicate that the proposed mechanism is private and accurate for small data sets.