2026-08-16T05:40:38Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/192292023-08-26T05:47:22Zcom_20.500.14352_14col_20.500.14352_15

Portela García-Miguel, Javier García Villalba, Luis Javier Silva Trujillo, Alejandra Guadalupe Sandoval Orozco, Ana Lucila Kim, Tai-Hoon 2023-06-18T00:05:06Z 2023-06-18T00:05:06Z 2016-11-01 1424-8220 10.3390/s16111832 https://hdl.handle.net/20.500.14352/19229 https://doi.org/10.3390/s16111832 https://www.mdpi.com/1424-8220/16/11/1832 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. eng https://creativecommons.org/licenses/by/3.0/es/ open access Atribución 3.0 España Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks journal article