English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Processing personal data is regulated in Europe by the General DataProtection Regulation (GDPR) through data processing agreements (DPAs).Checking the compliance of DPAs contributes to the compliance verification ofsoftware systems as DPAs are an important source of requirements for softwaredevelopment involving the processing of personal data. However, manuallychecking whether a given DPA complies with GDPR is challenging as it requiressignificant time and effort for understanding and identifying DPA-relevantcompliance requirements in GDPR and then verifying these requirements in theDPA. In this paper, we propose an automated solution to check the compliance ofa given DPA against GDPR. In close interaction with legal experts, we firstbuilt two artifacts: (i) the "shall" requirements extracted from the GDPRprovisions relevant to DPA compliance and (ii) a glossary table defining thelegal concepts in the requirements. Then, we developed an automated solutionthat leverages natural language processing (NLP) technologies to check thecompliance of a given DPA against these "shall" requirements. Specifically, ourapproach automatically generates phrasal-level representations for the textualcontent of the DPA and compares it against predefined representations of the"shall" requirements. Over a dataset of 30 actual DPAs, the approach correctlyfinds 618 out of 750 genuine violations while raising 76 false violations, andfurther correctly identifies 524 satisfied requirements. The approach has thusan 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 approachprovides an average accuracy gain of ~20 percentage points. The accuracy of ourapproach can be improved to ~94% with limited manual verification effort.

NLP-based Automated Compliance Checking of Data Processing Agreements against GDPR