With the onset of Big Data and Data-Intensive Applications (DIAs) exploiting such big data, the problem of offering privacy guarantees to data owners becomes crucial, even more so with the emergence of DevOps development strategies where speed is paramount. This paper outlines this complex scenario and the challenges therein. On one hand, we outline a tool prototype that addresses the key challenge we found in industry, more specifically, assisting the process of continuous DIA architecting for the purpose of offering privacy-by-design guarantees. On the other hand we define a research roadmap in pursuit of a more correct and complete solution for ensured privacy-by-design in the context of Big Data DevOps.
Towards DevOps for privacy-by-design in data-intensive applications: A research roadmap
Guerriero, Michele;Tamburri, Damian A.;Marconi, Francesco;Bersani, Marcello M.;
2017-01-01
Abstract
With the onset of Big Data and Data-Intensive Applications (DIAs) exploiting such big data, the problem of offering privacy guarantees to data owners becomes crucial, even more so with the emergence of DevOps development strategies where speed is paramount. This paper outlines this complex scenario and the challenges therein. On one hand, we outline a tool prototype that addresses the key challenge we found in industry, more specifically, assisting the process of continuous DIA architecting for the purpose of offering privacy-by-design guarantees. On the other hand we define a research roadmap in pursuit of a more correct and complete solution for ensured privacy-by-design in the context of Big Data DevOps.File | Dimensione | Formato | |
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