However, the sheer volume of related scientific literature and the significant gap between a lab context and real-world environments make it extremely challenging to assess the current progress in the area. The transformative potential of deep learning in enhancing computer science solutions has not gone unnoticed in the fields of security and privacy. This is ever more important in the light of new European platform regulations that intend to create boundaries for personalized advertising and introduce interoperability requirements, which in turn pose new opportunities to empower system creators and users alike to take control of users' privacy. This talk investigates approaches to both understand the roadblocks that keep system creators and users from adopting a privacy-by-design mindset and to find ways to address them. This comes to the detriment of service providers and users, who are both faced with decreased usability of websites, apps, and devices. They mainly resort to lengthy privacy policies and often deceptive cookie notices to ask users for their consent to data processing, rather than revise their own data processing practices and opt for approaches that collect less personal data. Ever since, research has continued to show that the creators of online services find it difficult to implement the legal requirements of EU legislation into practice. It has been five years since the General Data Protection Regulation (GDPR) went into effect in the EU.
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