The General Data Protection Regulation (GDPR) has been recently introduced to harmonize the different data privacy laws across Europe. Whether inside the EU or outside, organizations have to comply with the GDPR as long as they handle personal data of EU residents. The organizations with whom personal data is shared are referred to as data controllers. When controllers subcontract certain services that involve processing personal data to service providers (also known as data processors), then a data processing agreement (DPA) has to be issued. This agreement regulates the relationship between the controllers and processors and also ensures the protection of individuals' personal data. Compliance with the GDPR is challenging for organizations since it is large and relies on complex legal concepts. In this paper, we draw on model-driven engineering to build a machine-analyzable conceptual model that characterizes DPA-related requirements in the GDPR. Further, we create a set of criteria for checking the compliance of a given DPA against the GDPR and discuss how our work in this paper can be adapted to develop an automated compliance checking solution.

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When the entity processing personal data (the processor) differs from the one collecting personal data (the controller), processing personal data is regulated in Europe by the General Data Protection Regulation (GDPR) through data processing agreements (DPAs). Checking the compliance of DPAs contributes to the compliance verification of software systems as DPAs are an important source of requirements for software development involving the processing of personal data. However, manually checking whether a given DPA complies with GDPR is challenging as it requires significant time and effort for understanding and identifying DPA-relevant compliance requirements in GDPR and then verifying these requirements in the DPA. Legal texts introduce additional complexity due to convoluted language and inherent ambiguity leading to potential misunderstandings. In this paper, we propose an automated solution to check the compliance of a given DPA against GDPR. In close interaction with legal experts, we first built two artifacts: (i) the "shall" requirements extracted from the GDPR provisions relevant to DPA compliance and (ii) a glossary table defining the legal concepts in the requirements. Then, we developed an automated solution that leverages natural language processing (NLP) technologies to check the compliance of a given DPA against these "shall" requirements. Specifically, our approach automatically generates phrasal-level representations for the textual content of the DPA and compares them against predefined representations of the "shall" requirements. By comparing these two representations, the approach not only assesses whether the DPA is GDPR compliant but it further provides recommendations about missing information in the DPA. Over a dataset of 30 actual DPAs, the approach correctly finds 618 out of 750 genuine violations while raising 76 false violations, and further correctly identifies 524 satisfied requirements. The approach has thus an average precision of 89.1%, a recall of 82.4%, and an accuracy of 84.6%. Compared to a baseline that relies on off-the-shelf NLP tools, our approach provides an average accuracy gain of ⇡20 percentage points. The accuracy of our approach can be improved to ⇡94% with limited manual verification effort.

Technological advances in information sharing have raised concerns about data protection. Privacy policies contain privacy-related requirements about how the personal data of individuals will be handled by an organization or a software system (e.g., a web service or an app). In Europe, privacy policies are subject to compliance with the General Data Protection Regulation (GDPR). A prerequisite for GDPR compliance checking is to verify whether the content of a privacy policy is complete according to the provisions of GDPR. Incomplete privacy policies might result in large fines on violating organization as well as incomplete privacy-related software specifications. Manual completeness checking is both time-consuming and error-prone. In this paper, we propose AI-based automation for the completeness checking of privacy policies. Through systematic qualitative methods, we first build two artifacts to characterize the privacy-related provisions of GDPR, namely a conceptual model and a set of completeness criteria. Then, we develop an automated solution on top of these artifacts by leveraging a combination of natural language processing and supervised machine learning. Specifically, we identify the GDPR-relevant information content in privacy policies and subsequently check them against the completeness criteria. To evaluate our approach, we collected 234 real privacy policies from the fund industry. Over a set of 48 unseen privacy policies, our approach detected 300 of the total of 334 violations of some completeness criteria correctly, while producing 23 false positives. The approach thus has a precision of 92.9% and recall of 89.8%. Compared to a baseline that applies keyword search only, our approach results in an improvement of 24.5% in precision and 38% in recall.