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The increasing popularity of smartphones and location-based service (LBS) has brought us a new experience of mobile crowdsourcing marked by the characteristics of network-interconnection and information-sharing. However, these mobile crowdsourcing applications suffer from various inferential attacks based on mobile behavioral factors, such as location semantic, spatiotemporal correlation, etc. Unfortunately, most of the existing techniques protect the participant's location-privacy according to actual trajectories. Once the protection fails, data leakage will directly threaten the participant's location-related private information. It open the issue of participating in mobile crowdsourcing service without actual locations. In this paper, we propose a mobility-aware trajectory-prediction solution, TMarkov, for achieving privacy-preserving mobile crowdsourcing. Specifically, we introduce a time-partitioning concept into the Markov model to overcome its traditional limitations. A new transfer model is constructed to record the mobile user's time-varying behavioral patterns. Then, an unbiased estimation is conducted according to Gibbs Sampling method, because of the data incompleteness. Finally, we have the TMarkov model which characterizes the participant's dynamic mobile behaviors. With TMarkov in place, a mobility-aware spatiotemporal trajectory is predicted for the mobile user to participate in the crowdsourcing application. Extensive experiments with real-world dataset demonstrate that TMarkov well balances the trade-off between privacy preservation and data usability.
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http://dx.doi.org/10.3390/s21072474 | DOI Listing |
Comput Urban Sci
March 2025
Institute of Computing, Fluminense Federal University, Av. Gal. Milton Tavares de Souza, Niterói, 24210-310 Rio de Janeiro Brazil.
The recent COVID-19 pandemic has underscored the need for effective public health interventions during infectious disease outbreaks. Understanding the spatiotemporal dynamics of urban human behaviour is essential for such responses. Crowd-sourced geo-data can be a valuable data source for this understanding.
View Article and Find Full Text PDFBehav Res Methods
February 2025
Cranfield School of Management, Cranfield University, College Road, Cranfield, Bedfordshire, MK43 0AL, UK.
Online crowdsourcing platforms such as MTurk and Prolific have revolutionized how researchers recruit human participants. However, since these platforms primarily recruit computer-based respondents, they risk not reaching respondents who may have exclusive access or spend more time on mobile devices that are more widely available. Additionally, there have been concerns that respondents who heavily utilize such platforms with the incentive to earn an income provide lower-quality responses.
View Article and Find Full Text PDFSci Total Environ
March 2025
Dept. of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA 22904, USA; Link Lab, School of Engineering and Applied Science, University of Virginia, Charlottesville, VA 22904, USA. Electronic address:
Coastal urban flooding presents significant challenges due to the complex interactions between surface runoff, storm tides, and drainage systems. In highly impervious urban areas, infiltration may seem negligible, particularly during extreme events when soil saturation is rapidly exceeded. However, for nuisance flooding-becoming more frequent due to sea level rise and shifting rainfall patterns-the role of infiltration warrants closer examination.
View Article and Find Full Text PDFPLoS One
February 2025
Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh.
Background: Worldwide, millions of pregnant women use pregnancy-related apps to monitor their baby's growth and development. While most of the apps are user-friendly, not all of them are equally appealing. This study aimed to explore the user experience (UX) of pregnancy tracker mobile apps used by pregnant women.
View Article and Find Full Text PDFJ Acad Nutr Diet
January 2025
Ruby Winslow Linn Professor and Chair, Dept. of Nutrition in the College of Natural, Behavioral and Health Science, Simmons University, 300 The Fenway, Boston MA 02115-5820.
This article is part of a series of articles in the Journal of the Academy of Nutrition and Dietetics exploring the importance of research design, epidemiological methods, and statistical analysis as applied to nutrition and dietetics research. The purpose of this ongoing statistical portfolio is to assist Registered Dietitian Nutritionists (RDN) and Nutrition and Dietetic Technicians, Registered (NDTR) in interpreting nutrition research and applying scientific principles to produce high-quality data analysis. Advances in technology are promoting faster, easier, and often more diverse data collection and analysis.
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