A Solution, But Not a Panacea for Defending Privacy: The Challenges, Criticism and Limitations of Privacy by Design

Privacy by Design: 4 Key Points

PinG Privacy in Germany ◽

10.37307/j.2196-9817.2015.05.04 ◽

2015 ◽

Author(s):

Daniel J. Solove

Keyword(s):

Privacy By Design ◽

Key Points

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Privacy by Design: A Counterfactual Analysis of Google and Facebook Privacy Incidents

SSRN Electronic Journal ◽

10.2139/ssrn.2128146 ◽

2012 ◽

Cited By ~ 9

Author(s):

Ira Rubinstein ◽

Nathan Good

Keyword(s):

Counterfactual Analysis ◽

Privacy By Design

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Technology-Driven Disruption of Healthcare & 'UI Layer' Privacy-by-Design

SSRN Electronic Journal ◽

10.2139/ssrn.3611702 ◽

2020 ◽

Author(s):

Marcelo Corrales Compagnucci ◽

Mark Fenwick ◽

Helena Haapio ◽

Timo Minssen ◽

Erik P.M. Vermeulen

Keyword(s):

Privacy By Design

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Synthesising Privacy by Design Knowledge Toward Explainable Internet of Things Application Designing in Healthcare

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3434186 ◽

2021 ◽

Vol 17 (2s) ◽

pp. 1-29

Author(s):

Lamya Alkhariji ◽

Nada Alhirabi ◽

Mansour Naser Alraja ◽

Mahmoud Barhamgi ◽

Omer Rana ◽

...

Keyword(s):

Internet Of Things ◽

Conceptual Understanding ◽

Knowledge Engineering ◽

Privacy Preserving ◽

Design Knowledge ◽

Privacy By Design ◽

Healthcare Applications ◽

Software Developers ◽

Healthcare Application ◽

Application Design

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.

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