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

2012 ◽  
Author(s):  
Ira Rubinstein ◽  
Nathan Good
Keyword(s):  
Counterfactual Analysis ◽  
Privacy By Design

Senate Electoral Responsiveness under Indirect and Direct Election

2017 ◽  
Author(s):  
Wendy J. Schiller ◽  
Charles Stewart
Keyword(s):  
The Political ◽  
Counterfactual Analysis ◽  
Senate Elections ◽  
Seventeenth Amendment ◽  
Direct Election ◽  
Political Outcomes ◽  
Widespread Belief ◽  
Direct Elections ◽  
The Impact

This chapter integrates findings on indirect elections with current scholarship on the impact of the adoption of the Seventeenth Amendment and onset of direct elections. It constructs a comprehensive counterfactual analysis that helps demonstrate what the political outcomes would have been with direct elections in place since the founding, and in contrast, what Senate elections would look like after 1913 if indirect elections were still in place. It also addresses the question of whether U.S. senators represented states as units and responded to state governmental concerns more under the indirect system than they do under direct elections. It argues that indirect election had little impact on the Senate's overall partisan composition prior to 1913. Contrary to widespread belief, had direct election been in effect during the years immediately preceding the Seventeenth Amendment's passage, Republicans, not Democrats, would have benefited.

A Counterfactual Analysis of the Bank-Industry Relationship in Italy, 1913-1936

2013 ◽  
Author(s):  
Carlo Drago ◽  
Roberto Ricciuti ◽  
Alberto Rinaldi ◽  
Michelangelo Vasta
Keyword(s):  
Counterfactual Analysis ◽  
Industry Relationship

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

2020 ◽  
Author(s):  
Marcelo Corrales Compagnucci ◽  
Mark Fenwick ◽  
Helena Haapio ◽  
Timo Minssen ◽  
Erik P.M. Vermeulen
Keyword(s):  
Privacy By Design

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

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.

Defining IoT Orchestrations with Security and Privacy by Design: A Gap Analysis

2021 ◽  
pp. 1-8
Author(s):  
Papoutsakis Manos ◽  
Fysarakis Konstantinos ◽  
Spanoudakis George ◽  
Ioannidis Sotiris
Keyword(s):  
Gap Analysis ◽  
Security And Privacy ◽  
Privacy By Design
Sign in / Sign up

Export Citation Format

Share Document