  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.
