  Our ability to synthesize sensory data that preserves specific statistical
properties of the real data has had tremendous implications on data privacy and
big data analytics. The synthetic data can be used as a substitute for
selective real data segments,that are sensitive to the user, thus protecting
privacy and resulting in improved analytics.However, increasingly adversarial
roles taken by data recipients such as mobile apps, or other cloud-based
analytics services, mandate that the synthetic data, in addition to preserving
statistical properties, should also be difficult to distinguish from the real
data. Typically, visual inspection has been used as a test to distinguish
between datasets. But more recently, sophisticated classifier models
(discriminators), corresponding to a set of events, have also been employed to
distinguish between synthesized and real data. The model operates on both
datasets and the respective event outputs are compared for consistency. In this
paper, we take a step towards generating sensory data that can pass a deep
learning based discriminator model test, and make two specific contributions:
first, we present a deep learning based architecture for synthesizing sensory
data. This architecture comprises of a generator model, which is a stack of
multiple Long-Short-Term-Memory (LSTM) networks and a Mixture Density Network.
second, we use another LSTM network based discriminator model for
distinguishing between the true and the synthesized data. Using a dataset of
accelerometer traces, collected using smartphones of users doing their daily
activities, we show that the deep learning based discriminator model can only
distinguish between the real and synthesized traces with an accuracy in the
neighborhood of 50%.
