k-Degree anonymity on directed networks
In this paper, we consider the problem of anonymization on directed networks. Although there are several anonymization methods for networks, most of them have explicitly been designed to work with undirected networks and they can not be straightforwardly applied when they are directed. Moreover, ign...
| Autores: | , , , |
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| Formato: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2018 |
| País: | España |
| Recursos: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/150579 |
| Acesso em linha: | http://hdl.handle.net/10609/150579 https://doi.org/10.1007/s10115-018-1251-5 |
| Access Level: | acceso abierto |
| Palavra-chave: | data utility privacy anonymity social networks directed networks |
| Resumo: | In this paper, we consider the problem of anonymization on directed networks. Although there are several anonymization methods for networks, most of them have explicitly been designed to work with undirected networks and they can not be straightforwardly applied when they are directed. Moreover, ignoring the direction of the edges causes important information loss on the anonymized networks in the best case. In the worst case, the direction of the edges may be used for reidentification, if it is not considered in the anonymization process. Here, we propose two different models for k-degree anonymity on directed networks, and we also present algorithms to fulfill these k-degree anonymity models. Given a network G, we construct a k-degree anonymous network by the minimum number of edge additions. Our algorithms use multivariate micro-aggregation to anonymize the degree sequence, and then they modify the graph structure to meet the k-degree anonymous sequence. We apply our algorithms to several real datasets anddemonstrate their efficiency and practical utility. |
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