Can I read SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation on EtoBox?

SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation by Alzantot, Moustafa; Chakraborty, Supriyo; Srivastava, Mani B. is a scholarly article available to read on EtoBox.

What is SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation about?

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 syn

Author

Alzantot, Moustafa; Chakraborty, Supriyo; Srivastava, Mani B.

Published

2017

Language

EN