Quality Assessment of Online Automated Privacy Policy Generators: An Empirical Study
This study addresses the problem of legal compliance for app developers using automated tools, though it is incremental as it focuses on assessment rather than proposing new solutions.
The paper conducted an empirical study to assess the quality of online Automated Privacy Policy Generators (APPGs), finding that generated policies are often incomplete or contain unnecessary commitments, which could lead to legal risks for developers.
Online Automated Privacy Policy Generators (APPGs) are tools used by app developers to quickly create app privacy policies which are required by privacy regulations to be incorporated to each mobile app. The creation of these tools brings convenience to app developers; however, the quality of these tools puts developers and stakeholders at legal risk. In this paper, we conduct an empirical study to assess the quality of online APPGs. We analyze the completeness of privacy policies, determine what categories and items should be covered in a complete privacy policy, and conduct APPG assessment with boilerplate apps. The results of assessment show that due to the lack of static or dynamic analysis of app's behavior, developers may encounter two types of issues caused by APPGs. First, the generated policies could be incomplete because they do not cover all the essential items required by a privacy policy. Second, some generated privacy policies contain unnecessary personal information collection or arbitrary commitments inconsistent with user input. Ultimately, the defects of APPGs may potentially lead to serious legal issues. We hope that the results and insights developed in this paper can motivate the healthy and ethical development of APPGs towards generating a more complete, accurate, and robust privacy policy.