RT Journal Article
T1 Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks
A1 Portela García-Miguel, Javier
A1 García Villalba, Luis Javier
A1 Silva Trujillo, Alejandra Guadalupe
A1 Sandoval Orozco, Ana Lucila
A1 Kim, Tai-Hoon
AB 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.
PB MDPI
SN 1424-8220
YR 2016
FD 2016-11-01
LK https://hdl.handle.net/20.500.14352/19229
UL https://hdl.handle.net/20.500.14352/19229
LA eng
DS Docta Complutense
RD 8 jun 2025