A taxonomy on privacy and confidentiality - Nunes, José Bernardi S.; Brito, Andrey (Anais do Latin-American Symposium on Dependable Computing (LADC); 2022: Anais do XI Latin-American Symposium on Dependable Computing, 2022)
Ensuring privacy and confidentiality often implies in tailoring a solution to a specific application. We currently lack a common framework to compare and assess different solutions in terms of privacy and confidentiality. All of this makes it harder to establish whether we can reapply strategies to new applications and problems. From the point of view of an application’s stakeholders, the lack of this common framework makes it harder to navigate and search for the correct alternative, significantly if one cannot easily place the application in a context of privacy and confidentiality vulnerabilities. We believe that a taxonomy centered on applications’ privacy and confidentiality vulnerabilities would provide this framework. We then provide a taxonomy on privacy and confidentiality we employed to successfully classify nineteen applications, showcasing the generality of our taxonomy. We have further validated our taxonomy through an orthogonality demonstration and a utility demonstration and its utility by applying it to an intelligent infection analysis system part of a smart campus initiative.