Reversible Data Hiding in Encrypted Text Using Paillier Cryptosystem

Reversible Data Hiding in Encrypted Domain (RDHED) is an innovative method that can keep cover information secret and allows the data hider to insert additional information into it. This article presents a novel data hiding technique in an encrypted text called Reversible Data Hiding in Encrypted Text (RDHET). Initially, the original text is converted into their ASCII values. After that, the Paillier cryptosystem is adopted to encrypt all ASCII values of the original text and send it to the data hider for further processing. At the data hiding phase, the secret data are embedded into homomorphically encrypted text using a technique that does not lose any information, i.e., the homomorphic properties of the Paillier cryptosystem. Finally, the embedded secret data and the original text are recovered at the receiving end without any loss. Experimental results show that the proposed scheme is vital in the context of encrypted text processing at cloud-based services. Moreover, the scheme works well, especially for the embedding phase, text recovery, and performance on different security key sizes

A Vision Transformer-Based Approach to Bearing Fault Classification via Vibration Signals

Rolling bearings are the most crucial components of rotating machinery. Identifying defective bearings in a timely manner may prevent the malfunction of an entire machinery system. The mechanical condition monitoring field has entered the big data phase as a result of the fast advancement of machine parts. When working with large amounts of data, the manual feature extraction approach has the drawback of being inefficient and inaccurate. Data-driven methods like the Deep Learning method have been successfully used in recent years for mechanical intelligent fault detection. Convolutional neural networks (CNNs) were mostly used in earlier research to detect and identify bearing faults. The CNN model, however, suffers from the drawback of having trouble managing fault-time information, which results in a lack of classification results. In this study, bearing defects have been classified using a state-of-the-art Vision Transformer (ViT). Bearing defects were classified using Case Western Reserve University (CWRU) bearing failure laboratory experimental data. The research took into account 13 distinct kinds of defects under 0-load situations in addition to normal bearing conditions. Using the short-time Fourier transform (STFT), the vibration signals were converted into 2D time-frequency images. The 2D time-frequency images are used as input parameters for the ViT. The model achieved an overall accuracy of 98.8%