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Sensors 2016, 16(11), 1832; doi:10.3390/s16111832

Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks

1
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
2
Department of Convergence Security, Sungshin Women’s University, 249-1 Dongseon-dong 3-ga, Seoul 136-742, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Muhammad Imran, Athanasios V. Vasilakos, Thaier Hayajneh and Neal N. Xiong
Received: 22 August 2016 / Revised: 24 October 2016 / Accepted: 26 October 2016 / Published: 1 November 2016
(This article belongs to the Special Issue Topology Control in Emerging Sensor Networks)
View Full-Text   |   Download PDF [4334 KB, uploaded 1 November 2016]   |  

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. View Full-Text
Keywords: anonymity; email network; graph theory; privacy; social network analysis; small-world-ness; statistical disclosure attack anonymity; email network; graph theory; privacy; social network analysis; small-world-ness; statistical disclosure attack
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Portela, J.; García Villalba, L.J.; Silva Trujillo, A.G.; Sandoval Orozco, A.L.; Kim, T.-H. Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks. Sensors 2016, 16, 1832.

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