PEDRO: Privacy-Enhancing Decision suppoRt tOol

Citizens face online privacy threats from social media, online service providers and governments. Privacy-enhancing tools (PETs) can prevent privacy invasion, but the uptake of these is limited. We developed a novel conceptual framework for privacy self-protection, consisting of a classification framework of four distinct privacy threats and our own novel staged model of PET adoption requisites. Through an expert survey (N = 12) and a lay user survey (N = 500), we identified suitable PETs for non-expert users and identified potential barriers to PET adoption. Based on the studies and our theoretical framework, we then developed and implemented a PET decision support tool called PEDRO, and conducted expert evaluations (N = 10) to confirm the validity of its recommendations.

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Publication Details

Journal
Applied Sciences
Published
2024-10-11
DOI
https://doi.org/10.3390/app14209275
Citations
2
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.69
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article

PEDRO: Privacy-Enhancing Decision suppoRt tOol

Paul van Schaik, Karen Renaud
2 citations
Applied Sciences
Privacy-Preserving Technologies in Data
0.69
article

PEDRO: Privacy-Enhancing Decision suppoRt tOol

Paul van Schaik, Karen Renaud
article en
2 citations

Abstract

Citizens face online privacy threats from social media, online service providers and governments. Privacy-enhancing tools (PETs) can prevent privacy invasion, but the uptake of these is limited. We developed a novel conceptual framework for privacy self-protection, consisting of a classification framework of four distinct privacy threats and our own novel staged model of PET adoption requisites. Through an expert survey (N = 12) and a lay user survey (N = 500), we identified suitable PETs for non-expert users and identified potential barriers to PET adoption. Based on the studies and our theoretical framework, we then developed and implemented a PET decision support tool called PEDRO, and conducted expert evaluations (N = 10) to confirm the validity of its recommendations.

Applied SciencesVol. 14(20)
University of Strathclyde (GB), Teesside University (GB)
Openalex Percentile: Top 24%
Privacy-Preserving Technologies in Data
0.69
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PEDRO: Privacy-Enhancing Decision suppoRt tOol — Paul van Schaik, Karen Renaud · Applied Sciences (2024) | TGRS Research Map | TGRS