Abstract: In an example embodiment, an additional classifier is introduced to an autoencoder neural network. The additional classifier performs an additional classification task during the training and testing phases of the autoencoder neural network. More precisely, the autoencoder neural network learns to classify the domain (or origin) of each specific input sample. This leads to additional contextual awareness in the autoencoder neural network, which improves the reconstruction quality during both the training and testing phases. Thus, the technical problem of decreased autoencoder neural network reconstruction quality caused by high data variance is addressed.

Abstract: Techniques for implementing a differentially private variational autoencoder for data obfuscation are disclosed. In some embodiments, a computer system performs operations comprising: encoding input data into a latent space representation of the input data, the encoding of the input data comprising: inferring latent space parameters of a latent space distribution based on the input data, the latent space parameters comprising a mean and a standard deviation, the inferring of the latent space parameters comprising bounding the mean within a finite space and using a global value for the standard deviation, the global value being independent of the input data; and sampling data from the latent space distribution; and decoding the sampled data of the latent space representation into output data.

Abstract: Various examples are directed to systems and methods for obscuring personal information in a sensor data stream. A system may apply an encoder model to the sensor data stream to generate a latent space representation of the sensor data stream. The system may also apply a noise-scaling parameter to the latent space representation of the sensor data stream and apply a decoder model to the latent space representation of the sensor data stream to generate an obscured data stream.