Power normalized cepstral robust features of deep neural networks in a cloud computing data privacy protection scheme

Abstract

Deep Neural Networks (DNNs) have developed rapidly in data privacy protection applications such as medical treatment and finance. However, DNNs require high-speed and high-memory computers in terms of computation, otherwise training can be very lengthy. Furthermore, DNNs are often not available in resource-constrained mobile devices. Therefore, training and executing DNNs are increasingly using cloud computing. In the paper, the Power Normalized Cepstrum-based Robust Feature Detector (PNC-RFD), with deep learning in the cloud computing, is proposed for data privacy protection. The proposed PNC-RFD extracts a specified number of signal segments of high robustness used to embed and extract various data. For the sake of embedding and extracting the data, a method of information hiding employing Dual-Tree Complex Wavelet Packet Transform (DT CWPT) is therefore presented. The presented scheme simultaneously embeds multiple data into coefficients of the DT CWPT of signal segments. By embedding the data into the orthogonal spaces, the proposed method ensures the independent extraction of the multiple data. In line with the performance analysis, the superiority of the presented scheme is elaborated through making the comparison with the current state-of-the-art methods.