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Estimation of anonymous email network characteristics through statistical disclosure attacks
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.
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
Horizon 2020 Framework Programme, H2020: 700326
Generically Dependent Continuant
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