title: Extracting association patterns in network communications.
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.
authors: Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Information Technology and Computer Science, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, Madrid 28040, Spain. jportela@estad.ucm.es.
authors: Javier
authors: J
authors: Portela
authors: Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Information Technology and Computer Science, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, Madrid 28040, Spain. javiergv@fdi.ucm.es.
authors: Luis Javier García
authors: LJ
authors: Villalba
authors: Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Information Technology and Computer Science, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, Madrid 28040, Spain. asilva@fdi.ucm.es.|Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí (UASLP), Zona Universitaria Poniente, San Luis Potosí 78290, Mexico. asilva@fdi.ucm.es.
authors: Alejandra Guadalupe Silva
authors: AG
authors: Trujillo
authors: Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Information Technology and Computer Science, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, Madrid 28040, Spain. asandoval@fdi.ucm.es.
authors: Ana Lucila Sandoval
authors: AL
authors: Orozco
authors: Department of Convergence Security, Sungshin Women's University, 249-1 Dongseon-dong 3-ga, Seoul 136-742, Korea. taihoonn@daum.net.
authors: Tai-hoon
authors: TH
authors: Kim
mesh_terms: D003195:Computer Communication Networks; D016494:Computer Security; D006801:Humans; D008962:Models, Theoretical