Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/162218
Title: GR2ASP: Guided re-identification risk analysis platform
Author: Rolandus Hagedoorn, Tom
Director/Tutor: Bonchi, Francesco
Kumar, Rohit
Vitrià i Marca, Jordi
Keywords: Protecció de dades
Identitat digital
Treballs de fi de màster
Programari d'aplicació
Data protection
Online identities
Master's thesis
Application software
Issue Date: 2-Sep-2019
Abstract: [en] Data privacy has been gaining considerable momentum in the recent years. The combination of numerous data breaches with the increasing interest for data sharing is pushing policy makers to impose stronger regulations to protect user data. In the E.U, the GDPR, in place since since May 2018, is forcing countless small companies to de-identify their datasets. Numerous privacy policies developed in the last two decades along with several tools are available for doing so. However, both the policies and the tools are relatively complex and require the user to have strong foundations in data privacy. In this paper, I describe the development of GR 2 ASP, a tool aimed at guiding users through de-identifying their dataset in an intuitive manner. To do so, the user is shielded from almost all the complexity inherent to data privacy, and interacts with simplified notions. Our tool differentiates itself from state-of-the-art similar tools by providing explainable recommendations in an intuitive interface, and having a human-in-the-loop approach towards data de-identification. We therefore think that it represents a considerable improvement over currently available tools, and we expect it to be frequently used, especially in the context of the SMOOTH project for which it has been commissioned.
Note: Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2019, Tutor: Francesco Bonchi, Rohit Kumar i Jordi Vitrià
URI: http://hdl.handle.net/2445/162218
Appears in Collections:Màster Oficial - Fonaments de la Ciència de Dades

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