Document
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Extracting association patterns in network communications
related by: authorship_23872
accesstype: All Open Access, Gold, Green
related by: authorship_23873
volume: 15
type: Entity
estado: published
type: Document
issue: 2
most specific type: Document
origen: scopus
pmid: 25679311
page end: 4071
most specific type: Article
related by: authorship_23869
has publication venue: Sensors (Switzerland)
type: Thing
type: Article
e iscopus: 2-s2.0-84922746408
crisid: 4058
page start: 4052
date time value: article date 4058
type: Information Content Entity
abstract: In network communications, mixes provide protection against observers hiding the appearance of messages, patterns, length and links between senders and receivers. Statistical disclosure attacks aim to reveal the identity of senders and receivers in a communication network setting when it is protected by standard techniques based on mixes. This work aims to develop a global statistical disclosure attack to detect relationships between users. The only information used by the attacker is the number of messages sent and received by each user for each round, the batch of messages grouped by the anonymity system. A new modeling framework based on contingency tables is used. The assumptions are more flexible than those used in the literature, allowing to apply the method to multiple situations automatically, such as email data or social networks data. A classification scheme based on combinatoric solutions of the space of rounds retrieved is developed. Solutions about relationships between users are provided for all pairs of users simultaneously, since the dependence of the data retrieved needs to be addressed in a global sense. © 2015 by the authors; licensee MDPI, Basel, Switzerland.
type: Continuant
related by: authorship_23870
freetext keyword: Anonymity; Mixes; Network communications; Statistical disclosure attack Anonymity; Association patterns; Classification scheme; Contingency table; In networks; Model framework; Network communications; Statistical disclosure; Mixing; computer network; computer security; human; theoretical model; Computer Communication Networks; Computer Security; Humans; Models, Theoretical
doi: 10.3390/s150204052
related by: authorship_23871
type: Generically Dependent Continuant

Continuant
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Entity
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Generically Dependent Continuant
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RDF description of Extracting association patterns in network communications - http://orbis.uaslp.mx/vivo/individual/scopus4057
rights: termsOfUse
date: 2026-08-28T01:57:40
publisher: vivo
type: Document

Article
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Information Content Entity
type: Class

Sensors (Switzerland)
type: Periodical
type: Entity
type: Continuant
type: Document
type: Information Content Entity
type: Collection
type: Thing
type: Generically Dependent Continuant
type: Journal
publication venue for: Extracting association patterns in network communications