面向数据密集型应用中隐私设计的DevOps:研究路线图

M. Guerriero, D. Tamburri, Y. Ridene, F. Marconi, M. Bersani, Matej Artac
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引用次数: 6

摘要

随着大数据和利用大数据的数据密集型应用程序(DIAs)的出现,为数据所有者提供隐私保障的问题变得至关重要,尤其是随着DevOps开发策略的出现,速度至关重要。本文概述了这一复杂的情况及其面临的挑战。一方面,我们概述了一个工具原型,它解决了我们在工业中发现的关键挑战,更具体地说,它有助于持续的DIA架构过程,以提供基于设计的隐私保证。另一方面,我们定义了一个研究路线图,以追求在大数据DevOps背景下更正确和完整的解决方案来确保隐私的设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards DevOps for Privacy-by-Design in Data-Intensive Applications: A Research Roadmap
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.
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