The GDPR So Far: Implications for Information Governance, eDiscovery, and Privacy by Design

2021 ◽  
pp. 102-124
Author(s):  
Gail Gottehrer ◽  
Debbie Reynolds
2020 ◽  
Author(s):  
Marcelo Corrales Compagnucci ◽  
Mark Fenwick ◽  
Helena Haapio ◽  
Timo Minssen ◽  
Erik P.M. Vermeulen
Keyword(s):  

2015 ◽  
Vol 64 ◽  
pp. 1088-1098 ◽  
Author(s):  
Sana Bent Aboulkacem Guetat ◽  
Salem Ben Dhaou Dakhli

Author(s):  
Lamya Alkhariji ◽  
Nada Alhirabi ◽  
Mansour Naser Alraja ◽  
Mahmoud Barhamgi ◽  
Omer Rana ◽  
...  

Privacy by Design (PbD) is the most common approach followed by software developers who aim to reduce risks within their application designs, yet it remains commonplace for developers to retain little conceptual understanding of what is meant by privacy. A vision is to develop an intelligent privacy assistant to whom developers can easily ask questions to learn how to incorporate different privacy-preserving ideas into their IoT application designs. This article lays the foundations toward developing such a privacy assistant by synthesising existing PbD knowledge to elicit requirements. It is believed that such a privacy assistant should not just prescribe a list of privacy-preserving ideas that developers should incorporate into their design. Instead, it should explain how each prescribed idea helps to protect privacy in a given application design context—this approach is defined as “Explainable Privacy.” A total of 74 privacy patterns were analysed and reviewed using ten different PbD schemes to understand how each privacy pattern is built and how each helps to ensure privacy. Due to page limitations, we have presented a detailed analysis in Reference [3]. In addition, different real-world Internet of Things (IoT) use-cases, including a healthcare application, were used to demonstrate how each privacy pattern could be applied to a given application design. By doing so, several knowledge engineering requirements were identified that need to be considered when developing a privacy assistant. It was also found that, when compared to other IoT application domains, privacy patterns can significantly benefit healthcare applications. In conclusion, this article identifies the research challenges that must be addressed if one wishes to construct an intelligent privacy assistant that can truly augment software developers’ capabilities at the design phase.


2021 ◽  
pp. 1-26
Author(s):  
Henry Farrell ◽  
Abraham L. Newman

Abstract Scholars and policymakers long believed that norms of global information openness and private-sector governance helped to sustain and promote liberalism. These norms are being increasingly contested within liberal democracies. In this article, we argue that a key source of debate over the Liberal International Information Order (LIIO), a sub-order of the Liberal International Order (LIO), is generated internally by “self-undermining feedback effects,” that is, mechanisms through which institutional arrangements undermine their own political conditions of survival over time. Empirically, we demonstrate how global governance of the Internet, transnational disinformation campaigns, and domestic information governance interact to sow the seeds of this contention. In particular, illiberal states converted norms of openness into a vector of attack, unsettling political bargains in liberal states concerning the LIIO. More generally, we set out a broader research agenda to show how the international relations discipline might better understand institutional change as well as the informational aspects of the current crisis in the LIO.


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