Detalhes bibliográficos
| Ano de defesa: |
2017 |
| Autor(a) principal: |
TELES, Ariel Soares
 |
| Orientador(a): |
SILVA, Francisco José da Silva e
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| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Tese
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| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal do Maranhão
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| Programa de Pós-Graduação: |
PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
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| Departamento: |
DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
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| País: |
Brasil
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| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://tedebc.ufma.br:8080/jspui/handle/tede/1250
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Resumo: |
This research firstly investigates the privacy requirements of users in Mobile Social Networks (MSNs) through a study with 164 Brazilians, which indicated that their requirements are usually dynamic and contextual. Next, the research applies the Situational Computing paradigm to develop a solution to serve them. This solution is called SelPri, developed as proof of concept in the form of a mobile social application to autonomously adapt the privacy settings of posts in MSNs according to the user situation. SelPri uses a conceptual model with fuzzy logic as the basis for constructing an inference engine to identify mobile user situations from the following context information: location, time of the day, day of week, and co-location. SelPri is integrated with Facebook. Additionally, to show the flexibility of the conceptual model, it is also used to construct an inference engine to be used in a different application domain, the mental health. This second inference engine identifies user situations from different context information: it does not use co-location and uses the user activity. The solution originated in the mental health domain is called SituMan. Two experiments were carried out with both solutions, in order to verify the accuracy of the fuzzy inference engine to identify situations, and to evaluate the user satisfaction. The use experience evaluation with SelPri emphasized that the approach to meet the dynamic and contextdependent privacy requirements was well accepted by the participants and proved to be of practical use. The experiments also showed that both solutions were well evaluated with respect to usability. The accuracy evaluations showed a high hit rate of the inference engines to identify situations: ≈94.6% and ≈ 92.04%, for SelPri and SituMan, respectively. |
