# Towards Fine-Grained Localization of Privacy Behaviors

arXiv: https://arxiv.org/abs/2305.15314 · HTML: https://mlsanity.com/paper/2305.15314/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2023.

## Built on by (most cited first, 2 of 2)

- [Generating Privacy Stories From Software Documentation](https://mlsanity.com/paper/2506.23014/) (2025)
- [SoK: From Generation to Consumption of Privacy Documents in Software Systems](https://mlsanity.com/paper/2608.12511/) (2026)

## Abstract (arXiv)

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.