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AbstractData is the key to information mining that unveils hidden knowledge. The ability to revealed knowledge relies on the extractable features of a dataset and likewise the depth of the mining model. Conversely, several of these datasets embed sensitive information that can engender privacy violation and are subsequently used to build deep neural network (DNN) models. Recent approaches to enact privacy and protect data sensitivity in DNN models does decline accuracy, thus, giving rise to significant accuracy disparity between a non-private DNN and a privacy preserving DNN model. This accuracy gap is due to the enormous uncalculated noise flooding and the inability to quantify the right level of noise required to perturb distinct neurons in the DNN model, hence, a dent in accuracy. Consequently, this has hindered the use of privacy protected DNN models in real life applications. In this paper, we present a neuron noise-injection technique based on layer-wise buffered contribution ratio forwarding and ϵ-differential privacy technique to preserve privacy in a DNN model. We adapt a layer-wise relevance propagation technique to compute contribution ratio for each neuron in our network at the pre-training phase. Based on the proportion of each neuron’s contribution ratio, we generate a noise-tuple via the Laplace mechanism, and this helps to eliminate unwanted noise flooding. The noise-tuple is subsequently injected into the training network through its neurons to preserve privacy of the training dataset in a differentially private manner. Hence, each neuron receives right proportion of noise as estimated via contribution ratio, and as a result, unquantifiable noise that drops accuracy of privacy preserving DNN models is avoided. Extensive experiments were conducted based on three real-world datasets and their results show that our approach was able to narrow down the existing accuracy gap to a close proximity, as well outperforms the state-of-the-art approaches in this context.

Recently, research on snoring sound had gained interest especially in the area of classification in Obstructive Sleep Apnea (OSA) and distinction from non-snoring sounds. Classifiers such as Support Vector Machine (SVM), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) had been used to meet this interest. These approaches relies on several underlying techniques such as Mel Frequency Cepstral Coefficients (MFCC), Short-Time Fourier transform (STFT) and several others to extract features from snore/apnea Spectrogram images before classification process. However, achieving desirable classification accuracy depends on choice of classifier, feature extraction techniques and, available dataset. In the paper, we presented a brief survey on existing methods and snore data acquisition processes to quickly expose and ease new researches in this domain to make appropriate choice from available methods.

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