Abstract
Privacy visualizations help users understand the privacy implications of using an online service. Privacy by Design guidelines provide generally accepted privacy standards for developers of online services. To obtain a comprehensive understanding of online privacy, we review established approaches, distill a unified list of 15 privacy attributes and rank them based on perceived importance by users and privacy experts. We then discuss similarities, explain notable differences, and examine trends in terms of the attributes covered. Finally, we show how our results provide a foundation for user-centric privacy visualizations, inspire best practices for developers, and give structure to privacy policies.
Highlights
Online services currently handle unprecedented amounts of user-related data [129]
To make sure we did not miss anything, we performed several Google searches using all of the keywords we identified and found five more proposals for privacy visualizations coming from Non-Governmental Organisations (NGOs) and industry
While all Privacy by Design (PbD) guidelines make statements about security and transparency requirements, only half of the privacy visualizations we reviewed communicate these aspects to users
Summary
Machine learning algorithms extract value from large amounts of data by recognizing hidden patterns, links, behaviors, trends, identities, and practical knowledge, which has given birth to a “big data economy” [9, 152] This has opened a “Pandora’s Box” of privacy concerns [113, 141, 151]. Nissenbaum posits that privacy is shaped by social boundaries and norms [99] and, because individuals cannot provide truly informed consent, she suggests articulating context-specific norms that govern the collection and sharing of data online [100].
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