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Towards Cross-Provider Analysis of Transparency Information for Data Protection | | Elias Grünewald
; Johannes M. Halkenhäußer
; Nicola Leschke
; Frank Pallas
; | | Date: |
1 Sep 2023 | | Abstract: | Transparency and accountability are indispensable principles for modern data
protection, from both, legal and technical viewpoints. Regulations such as the
GDPR, therefore, require specific transparency information to be provided
including, e.g., purpose specifications, storage periods, or legal bases for
personal data processing. However, it has repeatedly been shown that all too
often, this information is practically hidden in legalese privacy policies,
hindering data subjects from exercising their rights. This paper presents a
novel approach to enable large-scale transparency information analysis across
service providers, leveraging machine-readable formats and graph data science
methods. More specifically, we propose a general approach for building a
transparency analysis platform (TAP) that is used to identify data transfers
empirically, provide evidence-based analyses of sharing clusters of more than
70 real-world data controllers, or even to simulate network dynamics using
synthetic transparency information for large-scale data-sharing scenarios. We
provide the general approach for advanced transparency information analysis, an
open source architecture and implementation in the form of a queryable analysis
platform, and versatile analysis examples. These contributions pave the way for
more transparent data processing for data subjects, and evidence-based
enforcement processes for data protection authorities. Future work can build
upon our contributions to gain more insights into so-far hidden data-sharing
practices. | | Source: | arXiv, 2309.00382 | | Services: | Forum | Review | PDF | Favorites | |
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