Uma abordagem de privacidade diferencial para dados correlacionados utilizando técnicas de agrupamento

Detalhes bibliográficos
Ano de defesa: 2018
Autor(a) principal: Mendonça, André Luís da Costa
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Não Informado pela instituição
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://www.repositorio.ufc.br/handle/riufc/38796
Resumo: Differential Privacy is a mathematical model designed to hinder the process of distinguishing individuals’ records on statistical databases, while maximizing data utility. Although Differential Privacy has been widely used for protecting the privacy of individual users’ data, it was not designed to provide its guarantees for correlated data, since it considers, in essence, independence of records in the database. Existing techniques using Differential Privacy on correlated data attempt to use dependence parameters or correlation coefficients (such as Pearson or Spearman’s Rank) to measure the correlation among records in a dataset. However, they tend to introduce an amount of noise higher than the necessary in the query answer, decreasing the data utility. Different from the existing works, we propose an approach that clusters similar records, which are more likely to be correlated, based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM). Our approach also employs a correlated Laplace mechanism to compute the privatized answers, satisfying the privacy guarantees of Differential Privacy. The experimental evaluation exhibits the benefits of our clustering strategy in terms of effectiveness and efficiency, considering data utility and privacy.