Privacy on the Fly: A Predictive Adversarial Transformation Network for Mobile Sensor Data

TL;DR

This paper introduces PATN, a real-time privacy-preserving method for mobile sensor data that proactively generates adversarial perturbations using historical signals, effectively reducing privacy inference accuracy while maintaining utility.

Contribution

The paper presents PATN, a novel real-time adversarial framework that leverages historical data to protect user privacy on mobile sensors without significant utility loss.

Findings

PATN reduces privacy inference accuracy to near-random levels.

PATN outperforms baseline methods in attack success rate.

PATN maintains application utility while enhancing privacy protection.

Abstract

Mobile motion sensors such as accelerometers and gyroscopes are now ubiquitously accessible by third-party apps via standard APIs. While enabling rich functionalities like activity recognition and step counting, this openness has also enabled unregulated inference of sensitive user traits, such as gender, age, and even identity, without user consent. Existing privacy-preserving techniques, such as GAN-based obfuscation or differential privacy, typically require access to the full input sequence, introducing latency that is incompatible with real-time scenarios. Worse, they tend to distort temporal and semantic patterns, degrading the utility of the data for benign tasks like activity recognition. To address these limitations, we propose the Predictive Adversarial Transformation Network (PATN), a real-time privacy-preserving framework that leverages historical signals to generate adversarial perturbations proactively. The perturbations are applied immediately upon data acquisition, enabling continuous protection without disrupting application functionality. Experiments on two datasets demonstrate that PATN substantially degrades the performance of privacy inference models, achieving Attack Success Rate (ASR) of 40.11% and 44.65% (reducing inference accuracy to near-random) and increasing the Equal Error Rate (EER) from 8.30% and 7.56% to 41.65% and 46.22%. On ASR, PATN outperforms baseline methods by 16.16% and 31.96%, respectively.