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Transparency and accountability are indispensable principles for modern dataprotection, from both, legal and technical viewpoints. Regulations such as theGDPR, therefore, require specific transparency information to be providedincluding, e.g., purpose specifications, storage periods, or legal bases forpersonal data processing. However, it has repeatedly been shown that all toooften, this information is practically hidden in legalese privacy policies,hindering data subjects from exercising their rights. This paper presents anovel approach to enable large-scale transparency information analysis acrossservice providers, leveraging machine-readable formats and graph data sciencemethods. More specifically, we propose a general approach for building atransparency analysis platform (TAP) that is used to identify data transfersempirically, provide evidence-based analyses of sharing clusters of more than70 real-world data controllers, or even to simulate network dynamics usingsynthetic transparency information for large-scale data-sharing scenarios. Weprovide the general approach for advanced transparency information analysis, anopen source architecture and implementation in the form of a queryable analysisplatform, and versatile analysis examples. These contributions pave the way formore transparent data processing for data subjects, and evidence-basedenforcement processes for data protection authorities. Future work can buildupon our contributions to gain more insights into so-far hidden data-sharingpractices.

Towards Cross-Provider Analysis of Transparency Information for Data Protection