PriGen: Towards Automated Translation of Android Applications’ Code to Privacy Captions

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 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.

Privacy in the Internet of Things: Where do We Stand? A Systematic Literature Review

“…Another idea is to leverage Neural Machine Translation (NMT) approaches to generate real-time and accurate privacy notices automatically from IoT applications' source code. In recent years, research leverages NMT approaches to translate sensitive information flows to generate simple privacy notices [169], [170] from Android applications' source code. Such automated approaches ensure accurate privacy notices which help remaining consistent throughout out applications' lifecycle.…”

Privacy in the Internet of Things: Where do We Stand? A Systematic Literature Review

“…Another idea is to leverage Neural Machine Translation (NMT) approaches to generate real-time and accurate privacy notices automatically from IoT applications' source code. In recent years, research leverages NMT approaches to translate sensitive information flows to generate simple privacy notices [169], [170] from Android applications' source code. Such automated approaches ensure accurate privacy notices which help remaining consistent throughout out applications' lifecycle.…”