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Interactive GDPR-Compliant Privacy Policy Generation for Software Applications

Pattaraporn Sangaroonsilp, Hoa Khanh Dam, Omar Haggag, John Grundy

TL;DR

This work proposes an approach that generates a comprehensive and compliant privacy policy with respect to the General Data Protection Regulation (GDPR) for diverse software applications and develops an interactive rule-based system that prompts software developers with a series of questions and uses their answers to generate a customised privacy policy.

Abstract

Software applications are designed to assist users in conducting a wide range of tasks or interactions. They have become prevalent and play an integral part in people's lives in this digital era. To use those software applications, users are sometimes requested to provide their personal information. As privacy has become a significant concern and many data protection regulations exist worldwide, software applications must provide users with a privacy policy detailing how their personal information is collected and processed. We propose an approach that generates a comprehensive and compliant privacy policy with respect to the General Data Protection Regulation (GDPR) for diverse software applications. To support this, we first built a library of privacy clauses based on existing privacy policy analysis. We then developed an interactive rule-based system that prompts software developers with a series of questions and uses their answers to generate a customised privacy policy for a given software application. We evaluated privacy policies generated by our approach in terms of readability, completeness and coverage and compared them to privacy policies generated by three existing privacy policy generators and a Generative AI-based tool. Our evaluation results show that the privacy policy generated by our approach is the most complete and comprehensive.

Interactive GDPR-Compliant Privacy Policy Generation for Software Applications

TL;DR

This work proposes an approach that generates a comprehensive and compliant privacy policy with respect to the General Data Protection Regulation (GDPR) for diverse software applications and develops an interactive rule-based system that prompts software developers with a series of questions and uses their answers to generate a customised privacy policy.

Abstract

Software applications are designed to assist users in conducting a wide range of tasks or interactions. They have become prevalent and play an integral part in people's lives in this digital era. To use those software applications, users are sometimes requested to provide their personal information. As privacy has become a significant concern and many data protection regulations exist worldwide, software applications must provide users with a privacy policy detailing how their personal information is collected and processed. We propose an approach that generates a comprehensive and compliant privacy policy with respect to the General Data Protection Regulation (GDPR) for diverse software applications. To support this, we first built a library of privacy clauses based on existing privacy policy analysis. We then developed an interactive rule-based system that prompts software developers with a series of questions and uses their answers to generate a customised privacy policy for a given software application. We evaluated privacy policies generated by our approach in terms of readability, completeness and coverage and compared them to privacy policies generated by three existing privacy policy generators and a Generative AI-based tool. Our evaluation results show that the privacy policy generated by our approach is the most complete and comprehensive.
Paper Structure (28 sections, 1 equation, 3 figures, 7 tables)

This paper contains 28 sections, 1 equation, 3 figures, 7 tables.

Figures (3)

  • Figure 1: PPGen framework
  • Figure 2: An example of the conceptual model of privacy policy metadata types proposed in Amaral2021Amaral2021Amaral. The full conceptual model can be found in Amaral. The red boxes and edges added to the original model are the samples of additional metadata types and relationships identified in our study.
  • Figure 3: Screenshots of our PPGen tool in use