To privately publish sensitive multimedia data in an edge network with fog devices, one of the best privacy-preserving solutions is to use differential privacy (DP) mechanisms. However, existing DP data publication mechanisms for the infinite data stream of edge networks mainly focus on publishing data with specific types of data or a set of predetermined queries. This approach is not suitable for multimedia data with numerous features that require a more flexible data publishing mechanism. In this article, we propose EdgeSyn, a novel mechanism for accurately publishing multimedia data over infinite data streams in an edge network. It allocates privacy budgets with a sliding window, adopting data synthesis mechanisms to support dynamic publishing without loss of accuracy. In more detail, EdgeSyn addresses the limitations associated with data types in prior data stream publishing approaches and introduces a privacy budget management strategy that optimally allocates budgets for the implementation of data synthesis mechanisms over an infinite data stream. The experimental results show that EdgeSyn performs well under different privacy budgets and various lengths of active windows.

Interesting sequential patterns in human movement trajectories can provide valuable knowledge for urban management, planning, and location-based business. Existing methods for mining such patterns, however, tend not to consider the reduced likeliness of trips with increasing travel cost. Consequently, it is difficult to differentiate the patterns emerging from people’s specific travel interests from those simply due to travel convenience. To solve this problem, this article presents Geo-SigSPM for mining geographically interesting and statistically significant sequential patterns from trajectories. Here, ‘geographically interesting’ patterns are those more frequent than their expected frequencies which consider both the travel cost and non-redundancy of any place in the patterns. To achieve this, Geo-SigSPM formulates the expected frequencies of the patterns based on doubly-constrained human mobility models and the frequencies of their subsequences. A set of statistical tests is also developed to evaluate the identified interesting patterns. Experiments with synthetic and Foursquare check-in datasets demonstrate the efficacy of Geo-SigSPM in discovering geographically interesting patterns, controlling the spurious pattern rate, and discovering patterns that better reflect people’s specific travel interests than the conventional frequency-based pattern mining approach. Geo-SigSPM is a promising solution to improving relevant decision-making when people’s travel preference beyond travel cost is concerned.

In private data publishing, a promising solution is generating synthetic data that enables any query on the private dataset while satisfying differential privacy. Over the past decade, researchers mainly focused on improving the query accuracy of synthetic data. However, the limitations of existing works restrict them from achieving a better trade-off between accuracy and privacy. In this paper, we propose ABSyn, a novel scheme for differentially private data synthesis. Under the Select-Measure-Generate paradigm, ABSyn has an adaptive mechanism for precisely selecting marginals and follows the batch processes. Our adaptive-batch scheme can provide a well-selected marginal set and the optimal allocation of privacy budget, which makes its synthetic data achieve high accuracy without compromising privacy. We implement an efficient prototype of ABSyn and compare it with existing works by analyzing public datasets. Experimental results show that ABSyn achieves query accuracy on synthetic datasets by a factor of$1.26\times $and efficiency by a factor of$18.60\times $over the state-of-the-art scheme on average.

Modelling error of smartphone-reported Global Navigation Satellite System (GNSS) locations plays an important role in urban navigation under mixed LOS/NLOS environments. In the case of pedestrian navigation, the performance of GNSS error modeling significantly affects the precision of final multi-source fusion. In this work, a novel Long Short-Term Memory (LSTM) network is developed for error modeling of smartphone-reported GNSS locations combined with the detected human motion information. The LSTM network is applied to adaptively combine multi-level observations provided by GNSS and built-in sensors-based location sources under a specific time period instead of considering only adjacent timestamps. The motion features extracted from multi-level observations is then modeled as the input vector of LSTM for training and prediction purposes, and the predicted errors under two axis in the n-frame are finally modeled as the error covariance matrix and applied in the multi-sources fusion structure. The comprehensive experiments indicate the effectivity and significant improvement for integrated localization after GNSS error modeling.

High-resolution remote sensing images usually contain multiscale information, which can be used to enhance the change detection (CD) performance. How to make effective use of multiscale information needs intensive study. This letter presents a novel multiscale decision fusion (MDF) method for unsupervised CD based on Dempster–Shafer (DS) theory and modified conditional random field (CRF). The method consists of three main steps: 1) images of three different scales are created automatically by image segmentation, and then three-scale difference images (DIs) are produced by applying change vector analysis to the three-scale images; 2) the membership function of each scale DI is estimated by fuzzy clustering, and the fusion membership, as well as an initial CD map, is obtained by combining the estimated membership using DS theory; and 3) the initial CD map is refined with an improved CRF that incorporates a spatial attraction model. The proposed method can combine the multiscale information in images and the spatial contextual information. The effectiveness of the proposed method was validated by two experiments with high-resolution remote sensing images.

Various methods have been proposed to detect the base locations of individuals, with their geo-tagged social media data. However, a common challenge relating to base-location detection methods (BDMs) is that, the rare availability of ground-truth data impedes the method assessment of accuracy and robustness, thus undermining research validity and reliability. To address this challenge, we collect users’ information from unstructured online content, and evaluate both the performance and robustness of BDMs. The evaluation consists of two tasks: the detection of base locations and also the differentiation between local residents and tourists. The results show BDMs can achieve high accuracies in base-location detection but tend to overestimate the number of tourists. Evaluation conducted in this study, also shows that BDMs’ accuracy is subject to the intensity of user’s activities and number of countries visited by the user but are insensitive to user’s gender. Temporally, BDMs perform better during weekends and summertime than during other periods, but the best performances appear with datasets that cover the whole time periods (whole day, week, and year). To the best of knowledge, this study is the first work to evaluate the performance and robustness of BDMs at individual level.

Volunteered geographic information can be used to predict regional desirability. A common challenge regarding previous works is that intuitive empirical models, which are inaccurate and bring in perceptual bias, are traditionally used to predict regional desirability. This results from the fact that the hidden interactions between user online check-ins and regional desirability have not been revealed and clearly modelled yet. To solve the problem, a novel neural network model ‘RegNet’ is proposed. The user check-in history is input into a neural network encoder structure firstly for redundancy reduction and feature learning. The encoded representation is then fed into a hidden-layer structure and the regional desirability is predicted. The proposed RegNet is data-driven and can adaptively model the unknown mappings from input to output, without presumed bias and prior knowledge. We conduct experiments with real-world datasets and demonstrate RegNet outperforms state-of-the-art methods in terms of ranking quality and prediction accuracy of rating. Additionally, we also examine how the structure of encoder affects RegNet performance and suggest on choosing proper sizes of encoded representation. This work demonstrates the effectiveness of data-driven methods in modelling the hidden unknown relationships and achieving a better performance over traditional empirical methods.

An increasing number of social media users are becoming used to disseminate activities through geotagged posts. The massive available geotagged posts enable collections of users’ footprints over time and offer effective opportunities for mobility prediction. Using geotagged posts for spatio-temporal prediction of future location, however, is challenging. Previous studies either focus on next-place prediction or rely on dense data sources such as GPS data. Introduced in this article is a novel method for future location prediction of individuals based on geotagged social media data. This method employs the hierarchical density-based clustering algorithm with adaptive parameter selection to identify the regions frequently visited by a social media user. A multi-feature weighted Bayesian model is then developed to forecast users’ spatio-temporal locations by combining multiple factors affecting human mobility patterns. Further, an updating strategy is designed to efficiently adjust, over time, the proposed model to the dynamics in users’ mobility patterns. Based on two real-life datasets, the proposed approach outperforms a state-of-the-art method in prediction accuracy by up to 5.34% and 3.30%. Tests show prediction reliability is high with quality predictions, but low in the identification of erroneous locations.

Attractive regions can be detected and recommended by investigating users’ online footprints. However, social media data suffers from short noisy text and lack of a-priori knowledge, impeding the usefulness of traditional semantic modelling methods. Another challenge is the need for an effective strategy for the selection/recommendation of candidate regions. To address these challenges, we propose a comprehensive workflow which combines semantic and location information of social media data to recommend thematic urban regions to users with specific interests. This workflow is novel in: (1) developing a data-driven geographic topic modelling method which utilizes the co-occurrence patterns of self-explanatory semantic information to detect semantic communities; (2) proposing a new recommendation strategy with the consideration of region’s spatial scale. The workflow was implemented using a real-world dataset and evaluation conducted at three different levels: semantic representativeness, topic identification and recommendation desirability. The evaluation showed that the semantic communities detected were internally consistent and externally differentiable and that the recommended regions had a high degree of desirability. The work has demonstrated the effectiveness of self-explanatory semantic information for geographic topic modelling and highlighted the importance of including region spatial scale into the model for an effective region recommending strategy.