Privacy Preserving Release of Mobile Sensor Data

TL;DR

This paper introduces a privacy-preserving mechanism for mobile sensor data that reduces user tracking and re-identification risks by 60% while maintaining data utility, using time-series modeling and correlated noise.

Contribution

It presents a novel, autonomous obfuscation method that handles sensor data fluctuations and counters noise filtering attacks without user or service interaction.

Findings

Reduces user trackability by 60% across datasets.

Maintains utility loss below 0.5 MAE.

More effective on larger datasets like Swipes.

Abstract

Sensors embedded in mobile smart devices can monitor users' activity with high accuracy to provide a variety of services to end-users ranging from precise geolocation, health monitoring, and handwritten word recognition. However, this involves the risk of accessing and potentially disclosing sensitive information of individuals to the apps that may lead to privacy breaches. In this paper, we aim to minimize privacy leakages that may lead to user identification on mobile devices through user tracking and distinguishability while preserving the functionality of apps and services. We propose a privacy-preserving mechanism that effectively handles the sensor data fluctuations (e.g., inconsistent sensor readings while walking, sitting, and running at different times) by formulating the data as time-series modeling and forecasting. The proposed mechanism also uses the notion of correlated noise-series against noise filtering attacks from an adversary, which aims to filter out the noise from the perturbed data to re-identify the original data. Unlike existing solutions, our mechanism keeps running in isolation without the interaction of a user or a service provider. We perform rigorous experiments on benchmark datasets and show that our proposed mechanism limits user tracking and distinguishability threats to a significant extent compared to the original data while maintaining a reasonable level of utility of functionalities. In general, we show that our obfuscation mechanism reduces the user trackability threat by 60\% across all the datasets while maintaining the utility loss below 0.5 Mean Absolute Error (MAE). We also observe that our mechanism is more effective in large datasets. For example, with the Swipes dataset, the distinguishability risk is reduced by 60\% on average while the utility loss is below 0.5 MAE.