Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/128390
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Type: Conference paper
Title: Quality assessment of online automated privacy policy generators: An empirical study
Author: Sun, R.
Xue, M.
Citation: Proceedings of the 24th Evaluation and Assessment in Software Engineering (EASE 2020), 2020 / pp.270-275
Publisher: Association for Computing Machinery
Publisher Place: New York
Issue Date: 2020
ISBN: 9781450377317
Conference Name: Evaluation and Assessment in Software Engineering (EASE) (15 Apr 2020 - 17 Apr 2020 : Trondheim, Norway)
Statement of
Responsibility: 
Ruoxi Sun, Minhui Xue
Abstract: 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.
Keywords: Automated privacy policy generator; Privacy policy completeness analysis; Quality assessment
Rights: © 2020 Association for Computing Machinery.
RMID: 1000023796
DOI: 10.1145/3383219.3383247
Published version: https://dl.acm.org/doi/proceedings/10.1145/3383219
Appears in Collections:Computer Science publications

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