# AntShield: On-Device Detection of Personal Information Exposure

arXiv: https://arxiv.org/abs/1803.01261 · HTML: https://mlsanity.com/paper/1803.01261/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2018.

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

- [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)

## Abstract (arXiv)

Mobile devices have access to personal, potentially sensitive data, and there is a growing number of applications that transmit this personally identifiable information (PII) over the network. In this paper, we present the AntShield system that performs on-device packet-level monitoring and detects the transmission of such sensitive information accurately and in real-time. A key insight is to distinguish PII that is predefined and is easily available on the device from PII that is unknown a priori but can be automatically detected by classifiers. Our system not only combines, for the first time, the advantages of on-device monitoring with the power of learning unknown PII, but also outperforms either of the two approaches alone. We demonstrate the real-time performance of our prototype as well as the classification performance using a dataset that we collect and analyze from scratch (including new findings in terms of leaks and patterns). AntShield is a first step towards enabling distributed learning of private information exposure.