Papers › Towards Fine-Grained Localization of Privacy Behaviors
Towards Fine-Grained Localization of Privacy Behaviors
Vijayanta Jain, Sepideh Ghanavati, Sai Teja Peddinti, Collin McMillan
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Mobile applications are required to give privacy notices to 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 that process sensitive 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 evaluation of our translation task for ~300,000 code segments.
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