Exploration and protection of location privacy in online social networks
AffiliationComputing and Information Systems
Document TypePhD thesis
Access StatusOpen Access
© 2018 Dr Wang Shuo
With the widespread proliferation of mobile devices and the ubiquitous accessibility and adoption of location-based social networks (LBSNs), an unprecedented amount of individual-level human spatio-temporal data is now routinely collected and available. Publication and use of such data can be of benefit to many research fields and society at large, from traffic management and urban planning through to emergency responses and crisis management, e.g. disease outbreaks and pandemics. However, such data raises many privacy concerns since it can be used to obtain details of an individual’s lifestyle in both the digital and physical world that has largely been left to LBSN providers to deal with. Many studies have explored the issues of spatio-temporal data privacy through exploring user location or their associated movement patterns (trajectories), however, such works have not considered the diversity of spatio-temporal information or the many dangers inherent to such data. With the increasing number of spatial data resources and multiple sharing applications, a portfolio of robust privacy preserving approaches are essential. In this context, one key challenge is understanding the trade-offs in loss of data utility whilst ensuring user privacy. This thesis extends the state-of-the-art in location-related through the following key contributions: it provides a systematic approach for protection of the privacy of both discrete and continuous spatio-temporal data releases under differential privacy derived from comprehensive analysis of spatio-temporal privacy issues and scenarios; it proposes utility-aware improvements for privacy-preservation to balance the trade-offs between utility and privacy; it addresses the global scale, heterogeneity and real-time nature of LBSNs, and finally it considers temporal and spatial privacy issues of individual locations and aggregated scenarios including user trajectories in time and space. This thesis is with publication whereby each contribution chapter (Chapters 3-10) is based directly on previously published work.
KeywordsLocation Privacy; Online Social Networks; Differential Privacy; Spatiotemporal; Data Publication; Social Connection
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