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e General Data Protection Regulation (GDPR) was introduced in Europe to o er new rights and protections to people concerning their personal data. We investigate GDPR from a database systems perspective, translating its legal articles into a set of capabilities and characteristics that compliant systems must support. Our analysis reveals the phenomenon of metadata explosion, wherein large quantities of metadata needs to be stored along with the personal data to satisfy the GDPR requirements. Our analysis also helps us identify the new workloads that must be supported under GDPR. We design and implement an open-source benchmark called GDPRbench that consists of workloads and metrics needed to understand and assess personal-data processing database systems. To gauge how ready the modern database systems are for GDPR, we modify Redis and PostgreSQL to be GDPR compliant. Our evaluations show that this modi cation degrades their performance by up to 5×. Our results also demonstrate that the current database systems are two to four orders of magnitude worse in supporting GDPR workloads compared to traditional workloads (such as YCSB), and also do not scale as the volume of personal data increases. We discuss the real-world implications of these ndings, and identify research challenges towards making GDPR compliance e cient in production environments. We release all of our so ware artifacts and datasets at h p://www.gdprbench.org

With the arrival of the European Union's General Data Protection Regulation (GDPR), several companies are making signi cant changes to their systems to achieve compliance. The changes range from modifying privacy policies to redesigning systems which process personal data. This work analyzes the privacy policies of large-scaled cloud services which seek to be GDPR compliant. The privacy policy is the main medium of information dissemination between the data controller and the users. We show that many services that claim compliance today do not have clear and concise privacy policies. We identify several points in the privacy policies which potentially indicate non-compliance; we term these GDPR vulnerabilities. We identify GDPR vulnerabilities in ten cloud services. Based on our analysis, we propose seven best practices for crafting GDPR privacy policies.