A Visualization Interface to Improve the Transparency of Collected Personal Data on the Internet
Marija Schufrin, Steven Lamarr Reynolds, Arjan Kuijper, Jörn Kohlhammer
Abstract
Fig. 1: The TimeView of the web interface TransparencyVis with MultiView mode on. The data elements from the GDPR data exports of two different users, each from Google and Facebook, are visualized in interactive scatterplots as circles over time. Different colors represent the categories of the data elements. Patterns can be detected and compared as described in use case 2 (see Sect. 5.2).
Abstract-Online services are used for all kinds of activities, like news, entertainment, publishing content or connecting with others. But information technology enables new threats to privacy by means of global mass surveillance, vast databases and fast distribution networks. Current news are full of misuses and data leakages. In most cases, users are powerless in such situations and develop an attitude of neglect for their online behaviour. On the other hand, the GDPR (General Data Protection Regulation) gives users the right to request a copy of all their personal data stored by a particular service, but the received data is hard to understand or analyze by the common internet user. This paper presents TransparencyVis -a web-based interface to support the visual and interactive exploration of data exports from different online services. With this approach, we aim at increasing the awareness of personal data stored by such online services and the effects of online behaviour. This design study provides an online accessible prototype and a best practice to unify data exports from different sources.
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Install the CLIlune papers fulltext a6c8c72a-6373-47d9-bb55-712438f147b3Cited by top-tier papers3
- Data Subjects' Reactions to Exercising Their Right of AccessArthur Borem, Elleen Pan, Olufunmilola Obielodan, Aurelie Roubinowitz et al.USENIX Security 2024 · 7 citations
- A Scoping Review and Guidelines on Privacy Policy's Visualization from an HCI PerspectiveShuning Zhang, Eve He, Sixing Tao, Yuting Yang et al.CHI 2026 · 2 citations
- Hidden in Plain Bytes: Investigating Interpersonal Account Compromise with Data ExportsJulia Nonnenkamp, Naman Gupta, Abhimanyu Dev Gupta, Rahul ChatterjeeCCS 2025
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