Document
type: Class

Estimation of anonymous email network characteristics through statistical disclosure attacks
origen: scopus
type: Article
most specific type: Article
type: Continuant
related by: authorship_27435
e iscopus: 2-s2.0-84994761233
type: Document
crisid: 4673
estado: published
related by: authorship_27436
has publication venue: Sensors (Switzerland)
date time value: article date 4673
type: Entity
doi: 10.3390/s16111832
related by: authorship_27433
related by: authorship_27434
has funding vehicle: Horizon 2020 Framework Programme, H2020: 700326
volume: 16
artnum: 1832
type: Thing
most specific type: Document
type: Information Content Entity
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.
freetext keyword: 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
issue: 11
accesstype: All Open Access, Gold, Green
related by: authorship_27432
type: Generically Dependent Continuant

Continuant
type: Class

Horizon 2020 Framework Programme, H2020: 700326
type: Relationship
type: BFO_0000020
type: Entity
type: Thing
type: Grant
type: Continuant
funding vehicle for: Estimation of anonymous email network characteristics through statistical disclosure attacks

Entity
type: Class

Generically Dependent Continuant
type: Class

RDF description of Estimation of anonymous email network characteristics through statistical disclosure attacks - http://orbis.uaslp.mx/vivo/individual/scopus4672
rights: termsOfUse
date: 2026-08-28T01:50:47
publisher: vivo
type: Document

Article
type: Class

Information Content Entity
type: Class

Sensors (Switzerland)
type: Periodical
type: Entity
publication venue for: Estimation of anonymous email network characteristics through statistical disclosure attacks
type: Continuant
type: Document
type: Information Content Entity
type: Collection
type: Thing
type: Generically Dependent Continuant
type: Journal