1,422 research outputs found
Differentially Private Mixture of Generative Neural Networks
Generative models are used in a wide range of applications building on large
amounts of contextually rich information. Due to possible privacy violations of
the individuals whose data is used to train these models, however, publishing
or sharing generative models is not always viable. In this paper, we present a
novel technique for privately releasing generative models and entire
high-dimensional datasets produced by these models. We model the generator
distribution of the training data with a mixture of generative neural
networks. These are trained together and collectively learn the generator
distribution of a dataset. Data is divided into clusters, using a novel
differentially private kernel -means, then each cluster is given to separate
generative neural networks, such as Restricted Boltzmann Machines or
Variational Autoencoders, which are trained only on their own cluster using
differentially private gradient descent. We evaluate our approach using the
MNIST dataset, as well as call detail records and transit datasets, showing
that it produces realistic synthetic samples, which can also be used to
accurately compute arbitrary number of counting queries.Comment: A shorter version of this paper appeared at the 17th IEEE
International Conference on Data Mining (ICDM 2017). This is the full
version, published in IEEE Transactions on Knowledge and Data Engineering
(TKDE
Detection of Freezing of Gait using Unsupervised Convolutional Denoising Autoencoder
At the advanced stage of Parkinsonâs disease, patients may suffer from âfreezing of gaitâ episodes: a debilitating condition wherein a patientâs âfeet feel as though they are glued to the floorâ. The objective, continuous monitoring of the gait of Parkinsonâs disease patients with wearable devices has led to the development of many freezing of gait detection models involving the automatic cueing of a rhythmic auditory stimulus to shorten or prevent episodes. The use of thresholding and manually extracted features or feature engineering returned promising results. However, these approaches are subjective, time-consuming, and prone to error. Furthermore, their performance varied when faced with the different walking styles of Parkinsonâs disease patients. Inspired by state-of-art deep learning techniques, this research aims to improve the detection model by proposing a feature learning deep denoising autoencoder to learn the salient characteristics of Parkinsonian gait data that is applicable to different walking styles for the elimination of manually handcrafted features. Even with the elimination of manually handcrafted features, a reduction in half of the data window sizes to 2s, and a significant dimensionality reduction of learned features, the detection model still managed to achieve 90.94% sensitivity and 67.04% specificity, which is comparable to the original Daphnet dataset research
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