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Lightweight distributed trust propagation

Quercia, D; Hailes, S; Capra, L; (2007) Lightweight distributed trust propagation. In: Ramakrishnan, N and Zaiane, OR and Shi, Y and Clifton, CW and Wu, XD, (eds.) ICDM 2007: PROCEEDINGS OF THE SEVENTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING. (pp. 282 - 291). IEEE COMPUTER SOC: Los Alamitos, US. Green open access

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Abstract

Using mobile devices, such as smart phones, people may create and distribute different types of digital content (e.g., photos, videos). One of the problems is that digital content, being easy to create and replicate, may likely swamp users rather than informing them. To avoid that, users may organize content producers that they know and trust in a web of trust. Users may then reason about this web of trust to form opinions about content producers with whom they have never interacted before. These opinions will then determine whether content is accepted. The process of forming opinions is called trust propagation. We design a mechanism for mobile devices that effectively propagates trust and that is lightweight and distributed (as opposed to previous work that focuses on centralized propagation). This mechanism uses a graph-based learning technique. We evaluate the effectiveness (predictive accuracy) of this mechanism against a large real-world data set. We also evaluate the computational cost of a J2ME implementation on a mobile phone.

Type:Proceedings paper
Title:Lightweight distributed trust propagation
Event:7th IEEE International Conference on Data Mining
Location:Omaha, NE
Dates:2007-10-28 - 2007-10-31
ISBN-13:978-0-7695-3018-5
Open access status:An open access version is available from UCL Discovery
DOI:10.1109/ICDM.2007.64
Publisher version:http://dx.doi.org/10.1109/ICDM.2007.64
Language:English
Additional information:©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

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