Efficient compliance with GDPR through automating privacy policy captions in web and mobile application
1 School of Engineering Prairie View, A and M University Prairie View, Texas USA.
2 Department of Information Systems and Business Analysis, Aston Business School, Aston University, Birmingham, UK.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 12(02), 446–467.
Article DOI: 10.30574/wjaets.2024.12.2.0317
Publication history:
Received on 15 June 2024; revised on 26 July 2024; accepted on 29 July 2024
Abstract:
Ensuring compliance with the General Data Protection Regulation (GDPR) presents significant challenges for organizations, especially those developing web and mobile applications. This study investigates the use of automation to enhance GDPR compliance by generating privacy policy captions through static code analysis and deep learning models. Privacy policy captions offer concise, user-friendly summaries of data processing practices, improving transparency and user trust. The research combines qualitative and quantitative methodologies, including static code analysis of application source codes and the application of neural machine translation models to generate privacy policy captions. Findings indicate that automation can effectively produce accurate, consistent, and comprehensible privacy policy captions that align with GDPR requirements. However, limitations such as tool capabilities, dataset diversity, and user testing scale highlight areas for future research. This study provides practical guidelines for implementing automated privacy policy captions, emphasizing the importance of continuous monitoring and updates to maintain compliance. By leveraging automation, organizations can enhance their data protection practices, build user trust, and achieve efficient GDPR compliance.
Keywords:
GDPR compliance; Privacy policy automation;Static code analysis; Neural machine translation; Data protection
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0