# ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic

arXiv: https://arxiv.org/abs/1507.00255 · HTML: https://mlsanity.com/paper/1507.00255/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2015.

## Built on by (most cited first, 9 of 9)

- [Security and Privacy Challenges of Large Language Models: A Survey](https://mlsanity.com/paper/2402.00888/) (2024)
- [Training Data Extraction From Pre-trained Language Models: A Survey](https://mlsanity.com/paper/2305.16157/) (2023)
- [GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding](https://mlsanity.com/paper/2407.13519/) (2024)
- [Quantifying Memorization and Detecting Training Data of Pre-trained Language Models using Japanese Newspaper](https://mlsanity.com/paper/2404.17143/) (2024)
- [Brief View and Analysis to Latest Android Security Issues and Approaches](https://mlsanity.com/paper/2109.00805/) (2021)
- [Statistical Feature-based Personal Information Detection in Mobile Network Traffic](https://mlsanity.com/paper/2112.12346/) (2021)
- [Guarding Digital Privacy: Exploring User Profiling and Security Enhancements](https://mlsanity.com/paper/2504.07107/) (2025)
- [Addressing Labelled Data Scarcity: Taxonomy-Agnostic Annotation of PII Values in HTTP Traffic using LLMs](https://mlsanity.com/paper/2605.06305/) (2026)
- [Enabling Multilingual Privacy Policy Audits: Large-Scale Analysis of Spanish Mobile Apps](https://mlsanity.com/paper/2607.18424/) (2026)

## Abstract (arXiv)

It is well known that apps running on mobile devices extensively track and leak users' personally identifiable information (PII); however, these users have little visibility into PII leaked through the network traffic generated by their devices, and have poor control over how, when and where that traffic is sent and handled by third parties. In this paper, we present the design, implementation, and evaluation of ReCon: a cross-platform system that reveals PII leaks and gives users control over them without requiring any special privileges or custom OSes. ReCon leverages machine learning to reveal potential PII leaks by inspecting network traffic, and provides a visualization tool to empower users with the ability to control these leaks via blocking or substitution of PII. We evaluate ReCon's effectiveness with measurements from controlled experiments using leaks from the 100 most popular iOS, Android, and Windows Phone apps, and via an IRB-approved user study with 92 participants. We show that ReCon is accurate, efficient, and identifies a wider range of PII than previous approaches.