Towards Fine-Grained Localization of Privacy Behaviors

• URL: http://arxiv.org/abs/2305.15314v1
• Date: Wed, 24 May 2023 16:32:14 GMT
• Title: Towards Fine-Grained Localization of Privacy Behaviors
• Authors: Vijayanta Jain, Sepideh Ghanavati, Sai Teja Peddinti, Collin McMillan
• Abstract summary: PriGen uses static analysis to identify Android applications' code segments that process sensitive information. We present the initial evaluation of our translation task for 300,000 code segments.
• Score: 5.74186288696419
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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