Geração de rótulo de privacidade por palavras-chaves e casamento de padrões

• Ano de defesa: 2016
• Autor(a) principal: Pontes, Diego Roberto Gonçalves de
• 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: Política de privacidade; Rótulo de privacidade; Tabela; Casamento de padrões; Palavras-chave; Privacidade
• Palavras-chave em Inglês: Privacy policy; Privacy label; Table; Pattern matching; Keywords; Privacy
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/8730

Users do not usually read privacy policies from online services. Among the main reasons for that is the fact that such policies are long and commonly hard to understand, which makes the user lose interest in reading them carefully. In this scenario, users are prone to agree to the policies terms without knowing what kind of data is being collected and why. This dissertation discusses how the policies' content may be presented in a more friendly way, showing information about data collection and usage in a table herein called Privacy Label. The Privacy Label is a table with lines named according to data collection terms and columns named according to expressions that reveal how the data is used by the service. The table content shows if the policy collects a particular data to a particular usage. To generate the Privacy Label, a study was made in a set of privacy policies to identify which terms repeat more often along the texts. To do so, we used techniques to find keywords, and from these keywords we were able to create privacy categories. The categories define which kind of data is being collected and why, which are represented by cells in the Privacy Label. Using word comparison techniques, a privacy policy can be analyzed and important information can be extracted by comparing its terms with the terms from the privacy categories. For each category we find, we show it in the Privacy Label. To assess the proposed approach we developed an application prototype, herein called PPMark, that analyzes a particular privacy policy, extract its keywords and generates the Privacy Label automatically. The information extracted was analyzed regarding its quality using three metrics: precision, recall and f-measure. The results show that the approach is a viable functional alternative to generate the Privacy Label and present privacy policies in a friendly manner. There are evidences of time saving by using our approach, which facilitates the process of decision making.