ObscureNet: Learning Attribute-invariant Latent Representation for Anonymizing Sensor Data

In this paper, we introduce ObscureNet, an encoder-decoder architecture that effectively conceals private attributes associated with time series data generated by sensors in IoT devices, while preserving the information content of the original time series. Drawing on conditional generative models and adversarial information factorization, ObscureNet learns latent representations that are invariant to the user-specified private attributes. This allows for modifying the private attributes or generating them randomly before using the decoder to synthesize a new version of data. We present three approaches to alter private attributes at anonymization time, and show that non-deterministic approaches can prevent an adversary from re-identifying private attributes. We compare ObscureNet with the autoencoder-based anonymization methods proposed in the literature and other generative models in terms of the accuracy of sensitive and desired inferences. Our experiments on two human activity recognition datasets show that compared to the original data, the sensitive inference accuracy is reduced by 80.38% on average, while the desired inference accuracy is only reduced by 6.82%. Moreover, ObscureNet reduces the sensitive inference accuracy by an additional 13.48% on average compared to the best baseline method. We report the computation overhead of running ObscureNet on a Raspberry Pi, and corroborate that it can be used for real-time anonymization of sensor data.