Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach

TL;DR

This paper introduces a novel approach using variational autoencoders with deterministic and probabilistic transformations to anonymize IoT sensor data, balancing privacy and utility while enabling real-time processing on edge devices.

Contribution

It proposes a new method combining representation learning and data transformation for sensor data anonymization, including real-time edge device implementation.

Findings

Effective privacy preservation with maintained data utility.

Outperforms existing autoencoder-based anonymization methods.

Enables real-time anonymization on resource-limited devices.

Abstract

The abundance of data collected by sensors in Internet of Things (IoT) devices, and the success of deep neural networks in uncovering hidden patterns in time series data have led to mounting privacy concerns. This is because private and sensitive information can be potentially learned from sensor data by applications that have access to this data. In this paper, we aim to examine the tradeoff between utility and privacy loss by learning low-dimensional representations that are useful for data obfuscation. We propose deterministic and probabilistic transformations in the latent space of a variational autoencoder to synthesize time series data such that intrusive inferences are prevented while desired inferences can still be made with sufficient accuracy. In the deterministic case, we use a linear transformation to move the representation of input data in the latent space such that the reconstructed data is likely to have the same public attribute but a different private attribute than the original input data. In the probabilistic case, we apply the linear transformation to the latent representation of input data with some probability. We compare our technique with autoencoder-based anonymization techniques and additionally show that it can anonymize data in real time on resource-constrained edge devices.