Mobile Sensor Data Anonymization

Abstract: Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions. Raw data streams from motion sensors embedded in portable and wearable devices may reveal private information about users without their awareness. For example, motion data might disclose the weight or gender of a user, or enable their re-identification. To address this problem, we propose an on-device transformation of sensor data to be shared for specific applications, such as monitoring selected daily activities, without revealing information that enables user identification. We formulate the anonymization problem using an information-theoretic approach and propose a new multi-objective loss function for training deep autoencoders. This loss function helps minimizing user-identity information as well as data distortion to preserve the application-specific utility. The training process regulates the encoder to disregard user-identifiable patterns and tunes the decoder to shape the output independently of users in the training set. The trained autoencoder can be deployed on a mobile or wearable device to anonymize sensor data even for users who are not included in the training dataset. Data from 24 users transformed by the proposed anonymizing autoencoder lead to a promising trade-off between utility and privacy, with an accuracy for activity recognition above 92% and an accuracy for user identification below 7%.

• The paper proposes a deep autoencoder framework that transforms sensor data to preserve activity recognition while significantly reducing re-identification risks.
• It introduces a novel multi-objective loss function that optimally balances privacy preservation with task-specific data utility and minimizes distortion.
• Experimental results on 24 users achieved 92% activity recognition accuracy with less than 7% user identification, validating the efficacy of the approach.

Mobile Sensor Data Anonymization: A Technical Overview

The paper "Mobile Sensor Data Anonymization" addresses the crucial challenge of safeguarding personal privacy amidst the pervasive use of motion sensors in portable and wearable devices. The authors propose a sophisticated method for anonymizing sensor data using deep learning techniques, specifically leveraging deep autoencoder architectures to mitigate the risks associated with user re-identification from raw sensor data.

Core Methodology and Results

1. Anonymization Framework: The paper outlines an anonymization framework employing deep autoencoders. The proposed framework transforms sensor data before it is shared with applications, preserving the utility for specific tasks (such as activity recognition) while minimizing exposure of information that could lead to user identification.
2. Multi-Objective Loss Function: The authors introduce a novel multi-objective loss function designed to optimize the trade-off between privacy and utility. This loss function balances three key aspects: reducing user identity leakage, preserving task-specific information, and minimizing data distortion.
3. Results: Experiments conducted on a dataset collected from 24 users demonstrated promising outcomes. The anonymizing autoencoder achieved an impressive accuracy of 92% in activity recognition, while maintaining user identification accuracy below 7%. These outcomes highlight the efficacy of the proposed method in achieving a strong privacy-utility trade-off.

Technical Contributions

• Information-Theoretic Approach: The anonymization problem is formulated within an information-theoretic framework. This involves quantifying privacy in terms of mutual information between the released data and the potential private information that can be inferred.
• Adversarial Training: The paper leverages adversarial approaches to train autoencoders, approximating mutual information by estimating posterior distributions of private variables. This is a crucial technique for effectively obscuring sensitive patterns in the data.
• Generalization Across Users: One of the notable technical achievements is the model's ability to generalize across unseen users without requiring user-specific retraining. This is achieved by shaping both the encoder and decoder outputs to ignore user-specific signals.

Implications and Speculations

Practically, the proposed anonymization framework can be directly applied to mobile and wearable devices to protect user privacy without compromising application utility. This has significant implications for fields where sensitive data is continuously generated and processed, such as health monitoring, smart home systems, and fitness tracking.

Theoretically, the approach opens avenues for further research into privacy-preserving techniques that can be deployed in real-time on edge devices. Future developments in AI might explore enhanced loss functions, better unsupervised learning mechanisms for privacy protection, and integration of federated learning to enrich training datasets while maintaining privacy.

In conclusion, while the research offers a solid step towards solving the problem of privacy in motion sensor data, the ongoing evolution of adversarial learning and information-theoretic privacy measures remains critical in adapting to diverse applications and emerging threats.