Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks
Javier Pórtela García-Miguel, Luis Javier García Villalba, Alejandra Guadalupe Silva Trujillo, Ana Lucila Sandoval Orozco, Tai-hoon Kim · Sensors · 2016
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.