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. 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 it against predefined representations of the"shall"requirements. 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.

https://export.arxiv.org/pdf/2209.09722v2.pdf

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

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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 20 percentage points. The accuracy of our approach can be improved to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 94% with limited manual verification effort.

/pdf/nlp-based-automated-compliance-checking-of-data-processing-lbsw9jvf.pdf

Since the first lockdown in 2020, video conferencing tools have become increasingly important for employment, education, and social interaction, making them essential tools in everyday life. This study investigates the accessibility and usability of the desktop and mobile versions of three popular video conferencing tools, Zoom, Google Meet, and MS Teams, for visually impaired people interacting via screen readers and keyboard or gestures. This involved two inspection evaluations to test the most important features of the desktop and mobile device versions and two surveys of visually impaired users to obtain information about the accessibility of the selected video conferencing tools. Sixty-five people answered the survey for desktop and 94 for mobile platforms. The results showed that Zoom was preferred to Google Meet and MS Teams but that none of the tools was fully accessible via screen reader and keyboard or gestures. Finally, the results of this empirical study were used to develop a set of guidelines for designers of video conferencing tools and assistive technology.

Video Conferencing Tools: Comparative Study of the Experiences of Screen Reader Users and the Development of More Inclusive Design Guidelines

The objective of this scoping review is to characterize the current scenario of mobile applications considering accessibility issues for people with cognitive, motor, and sensory disabilities. Nowadays, mobile devices have grown exponentially, giving way to new ways of relating, managing, and working. In this context, mobile devices seek to democratize access to knowledge on different topics; however, the application of accessibility guidelines is neglected. The reviewers extracted the most relevant articles published between 2000 and 2020 from the ACM Digital Library, IEEE Xplore, ScienceDirect, Scopus, and Web of Science databases. In this scoping review, the PRISMA-ScR checklist was used to extract scientific articles; Cohen’s kappa coefficient = 0.4117 was applied, which implies moderate concordance of reviewers; 22 primary studies were extracted from a total of 211. The results obtained in this research suggest applying WCAG 2.1 in mobile applications to achieve an adequate level of accessibility. Future work suggests designing review tools that include machine learning based on artificial intelligence algorithms.

https://www.mdpi.com/2076-3417/11/12/5707/pdf

Accessibility in Native Mobile Applications for Users with Disabilities: A Scoping Review

Automated Evaluation Tools for Web and Mobile Accessibility: proposal of a new adaptive interface tool

As the number of mobile services grows, mobile users who gain access to the Internet via mobile devices are increasing. Accessibility refers to providing functionality to smart phone apps (and web) so that all human can access contents/service/products on the Internet regardless their age and disability. Accessibility support is essential for mobile users owing to the unique nature and capabilities of mobile devices, such as smaller screens, touch and gesture interface, and built-in screen readers specific to operating systems. The latest mobile device usage statistics showed that between the two different mobile channels, mobile apps and mobile webs, consumers spend most of their time in mobile apps. Accessibility of apps in a smartphone is more important than ever as more people use a smartphone apps to consume content/ service/ product on the Internet. Automated tools for mobile apps that evaluate compliance with accessibility standards are still scarce. This makes it difficult for developers who are unfamiliar with accessibility needs to ensure their apps are accessible by everyone. While the prototype does not include all the functions, it demonstrates that it is feasible to develop a full version of an automatic assessment tool for smartphone apps, which is an under-researched area.This paper designs an accessibility evaluation tool that checks mobile apps for conformance with accessibility standards, and develops a prototype of the evaluation tool. The proposed tool verifies the existence (or nonexistence) of alternative text for non-text content, which is an important accessibility factor for visually impaired users. It discovers missing alternative text by checking the contentDescription attribute of the ImageView element in Android XML files.

Development of Automatic Evaluation Tool for Mobile Accessibility for Android Application

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Development of Automatic Evaluation Tool for Mobile Accessibility for Android Application