SenseGen: A deep learning architecture for synthetic sensor data generation

By Moustafa Farid Alzantot, Supriyo Chakraborty, Mani B. Srivastava. 2017

143 citations recorded by the source corpus.

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. Prior work on data synthesis have often focussed on classifiers that are built for features explicitly preserved by the synthetic data. This suggests that an adversary can build classifiers that can exploit a potentially disjoint set of features for differentiating between the two datasets. 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 (MDN); 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%.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Olympus: Sensor Privacy through Utility Aware Obfuscation (2018, unknown): “SenseGen [12] uses GAN to generate synthetic sensor data to protect user’s privacy.”
Time-series Generative Adversarial Networks (2019, background): “A multitude of applied studies have since utilized these frameworks to generate synthetic sequences in such diverse domains as text [15], finance [16], biosignals [17], sensor [18] and smart grid data [19], as well as renewable scenarios [20].”
Deep learning for situational understanding (2017, methodology): “We used a variation of the hierarchical recurrent neural network architecture, proposed in [23], to perform our experiments.”
RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network (2019, methodology): “The model in Figure 2 was built using the SenseGen model as a template [2] and consisted of a network constructed from”
Recent trends in machine learning for human activity recognition—A survey (2018, background): “Alzantot, Chakraborty, and Srivastava (2017) have proposed a synthetic data generation model using deep learning for ADLs using smartphones.”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, methodology): “Deep learning methods are also used to augment the datasets to improve performance [261, 262, 263].”
Autonomous vehicle control via deep reinforcement learning (2017, background): “Due to this, the use of synthetic data for training machine learning algorithms have been an active research field [8][9][10].”