@inproceedings{rcis2021,
 abstract = {Mobile applications are required to give privacy notices to the users when they collect or share personal information. Creating consistent and concise privacy notices can be a challenging task for developers. Previous work has attempted to help developers create privacy notices through a questionnaire or predefined templates. In this paper, we propose a novel approach and a framework, called PriGen, that extends these prior work. PriGen uses static analysis to identify Android applications' code segments which process personal information (i.e. permission-requiring code segments) and then leverages a Neural Machine Translation model to translate them into privacy captions. We present the initial analysis of our translation task for \$\$\backslashsim \$\$300,000 code segments.},
 address = {Cham},
 author = {Jain, Vijayanta
and Gupta, Sanonda Datta
and Ghanavati, Sepideh
and Peddinti, Sai Teja},
 booktitle = {Research Challenges in Information Science},
 editor = {Cherfi, Samira
and Perini, Anna
and Nurcan, Selmin},
 isbn = {978-3-030-75018-3},
 pages = {142--151},
 publisher = {Springer International Publishing},
 title = {PriGen: Towards Automated Translation of Android Applications' Code to Privacy Captions},
 year = {2021}
}