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Τίτλος: Εnhancing social networking in smart cities: Privacy and security borderlines
Συγγραφείς: Moustaka, Vaia 
Theodosiou, Zenonas 
Vakali, Athena I. 
Kounoudes, Anastasis 
Anthopoulos, Leonidas G. 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Λέξεις-κλειδιά: Smart people;Smart living;Online social networks;Behavioral patterns;Privacy;Information security;Engagement
Ημερομηνία Έκδοσης: Μαΐ-2019
Πηγή: Technological Forecasting and Social Change, 2019, vol. 142, pp. 285-300
Volume: 142
Start page: 285
End page: 300
Περιοδικό: Technological Forecasting and Social Change 
Περίληψη: The proliferation of social platforms and the enhanced connectivity have led people of different age groups, ethnicity, social or economic status to reveal a great deal about themselves online. Data collected from online social networks (OSN) provides social, economic, and cultural information which can be utilized by governments, policy makers, authorities and even commercial industries to better understand market trends and behavioral patterns, that can influence the individual dynamics through open data sources. OSN constitute a breeding ground for the spread of several risks and threats to privacy and security that affect participation and quality of life in smart cities. Although the aspects of privacy and security, and individuals' behavior in social networking are important for the successful development of smart cities, they have not been adequately discussed. To this end, this study aims to address this issue by revealing the risks, threats and individuals' behavior on OSN as an attempt to enhance privacy and security, and boost community's engagement in smart cities. Furthermore, a novel model which outlines the relationships between privacy and security threats, along with some effective countermeasures for the protection of OSN users in smart cities are proposed.
URI: https://hdl.handle.net/20.500.14279/28624
ISSN: 18735509
DOI: 10.1016/j.techfore.2018.10.026
Rights: © Elsevier
Type: Article
Affiliation: Aristotle University of Thessaloniki 
SignalGeneriX Ltd 
University of Thessaly 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:Άρθρα/Articles

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