Mecanismo de preservação de privacidade do usuário em ambientes IoT

• Ano de defesa: 2018
• Autor(a) principal: Couto, Fagner Roger Pereira
• Orientador(a): Zorzo, Sérgio Donizetti
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• Palavras-chave em Português: Internet das coisas; Privacidade; Preferências de privacidade
• Palavras-chave em Inglês: Internet of things; Privacy; Privacy preferences
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/11010

The Internet of Things (IoT) is considered one of the emerging technologies in the area of information technology. The use of this technology provides an improvement to the population by providing better transportation systems, health and electrical infrastructure, among others. However, collecting information in these spaces can cause serious damage to the user’s privacy, which is one of the challenges to be overcome by IoT. In some scenarios, the user may provide personal information without being aware of the risks to their privacy. Cryptographic primitives with a high computational cost are used to maintain the privacy of users in IoT environments, but they are not applicable to all devices due to their heterogeneity. In this context, this work proposes a mechanism of preservation of privacy that aims to mediate the information exchanges that occur in IoT environments. This proposal was based on a research done with users that aimed to validate the characteristics and functionalities of a mechanism with these objectives. The privacy preservation mechanism was developed as an application to provide ease in user interaction with IoT environments. The validation of the application was performed by an experiment with the users, where they were submitted to several IoT scenarios. The results show that, with the use of the mechanism, users feel more comfortable with the availability of their data in these environments. The learning process of application privacy preferences obtained a success rate of 88.62% in the predicted scenarios and is considered satisfactory for decision making procedures for the provision of personal information.