abstract

• Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users’ network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders’ or receivers’ identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks. © 2016 by the authors; licensee MDPI, Basel, Switzerland.

authors

• Kim T.-H.
• Orozco A.L.S.
• Portela J.
• Trujillo A.G.S.
• Villalba L.J.G.

publication date

• 2016-01-01

funding provided via

• Horizon 2020 Framework Programme, H2020: 700326  Grant

published in

• Sensors (Switzerland)  Journal

keywords

• Anonymity; Email network; Graph theory; Privacy; Small-world-ness; Social network analysis; Statistical disclosure attack Data privacy; Graph theory; Social networking (online); Anonymity; Degree distributions; E-mail networks; Incomplete information; Power-law characteristics; Relational data; Small worlds; Statistical disclosure; Electronic mail

Digital Object Identifier (DOI)

• 10.3390/s16111832

start page

end page

volume

• 16

issue

• 11