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 $\sim$300,000 code segments.

翻译：移动应用在收集或分享个人信息时，需要向用户提供隐私通知。对于开发者而言，创建一致且简洁的隐私通知是一项具有挑战性的任务。先前的研究尝试通过问卷或预定义模板来帮助开发者创建隐私通知。本文提出了一种名为PriGen的新颖方法与框架，对先前工作进行扩展。PriGen通过静态分析识别安卓应用中处理敏感信息的代码段（即需要权限的代码段），然后利用神经机器翻译模型将其翻译为隐私说明。我们针对约30万个代码段展示了翻译任务的初步评估结果。