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Metrics for privacy assessment when sharing information in online social networks

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Metrics for privacy assessment when sharing information in online social networks

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dc.contributor.author Alemany-Bordera, José es_ES
dc.contributor.author Del Val Noguera, Elena es_ES
dc.contributor.author Alberola Oltra, Juan Miguel es_ES
dc.contributor.author García-Fornes, A es_ES
dc.date.accessioned 2020-03-30T07:22:16Z
dc.date.available 2020-03-30T07:22:16Z
dc.date.issued 2019-09-30 es_ES
dc.identifier.uri http://hdl.handle.net/10251/139772
dc.description (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. es_ES
dc.description.abstract [EN] Privacy risk in Online Social Networks has become an important social concern. Users, with different perceptions of risk, share information without considering the audience that has access to the information disclosed or how far a publication will go. According to this, we propose two metrics (Audience and Reachability) based on information flows and friendship layers that indicate the privacy risk of sharing information, addressing the posts¿ scope and invisible audience. We assess these metrics through agent simulations in well-known models of networks. The findings show a strong relationship between metrics and structural centrality network properties. We also studied scenarios where there is no previous information about users activity or the information about the traces of the messages cannot be obtained. To deal with privacy assessment in these scenarios, we analyze the relationship between the proposed privacy metrics and local centrality properties as an estimation of privacy risk. The results showed that effectiveness centrality can be used as a suitable approximation of the proposed privacy measures. es_ES
dc.description.sponsorship This work was supported in part by the Spanish Government project under Grant TIN2017-89156-R, and in part by the FPI under Grant BES-2015-074498. es_ES
dc.language Inglés es_ES
dc.publisher Institute of Electrical and Electronics Engineers es_ES
dc.relation MINECO/BES-2015-074498 es_ES
dc.relation AEI/TIN2017-89156-R es_ES
dc.relation.ispartof IEEE Access es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Privacy es_ES
dc.subject Information sharing es_ES
dc.subject Social networks es_ES
dc.subject Network topology es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Metrics for privacy assessment when sharing information in online social networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1109/ACCESS.2019.2944723 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2019). Metrics for privacy assessment when sharing information in online social networks. IEEE Access. 7:143631-143645. https://doi.org/10.1109/ACCESS.2019.2944723 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1109/ACCESS.2019.2944723 es_ES
dc.description.upvformatpinicio 143631 es_ES
dc.description.upvformatpfin 143645 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 7 es_ES
dc.identifier.eissn 2169-3536 es_ES
dc.relation.pasarela S\395094 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Ministerio de Economía y Empresa es_ES


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