Abstract In the era of digital proliferation, individuals opt for cloud servers to store their data due to the diverse advantages they offer. However, entrusting data to cloud servers relinquishes users' control, potentially compromising data confidentiality and integrity. Traditional auditing methods designed to ensure data integrity in cloud servers typically depend on Trusted Third Party Auditors. Yet, many of these existing auditing approaches grapple with intricate certificate management and key escrow issues. Furthermore, the imminent threat of powerful quantum computers poses a risk of swiftly compromising these methods in polynomial time. To overcome these challenges, this paper introduces a Quantum‐based Secure Key Communication and Key Generation Scheme QSKCG for Outsourced Data in the Cloud. Leveraging Elliptic Curve Cryptography, the BB84 secure communication protocol, certificateless signature, and blockchain network, the proposed scheme is demonstrated through security analysis, affirming its robustness and high efficiency. Additionally, performance analysis underscores the practicality of the proposed scheme in achieving post‐quantum security in cloud storage.

Summary Cloud‐server is an effective and flexible way to manage the massive amounts of data produced by cyber‐physical systems. It is a better option to outsource these data to the cloud. When data is outsourced to the cloud, users lose control over it, compromising the data's integrity. Many public auditing schemes were proposed to address this issue, where trusted third‐party auditors (TPAs) verify the integrity of data on behalf of the users. However, trusted TPAs are vulnerable and may not provide correct auditing results on time. Moreover, there is no trust nowadays. Blockchain‐based auditing schemes were introduced to prevent trusted third parties from overcoming this issue. However, most blockchain‐based public auditing schemes suffer from the key‐escrow issue. To overcome the key‐escrow problem and malicious auditors, in this article, a blockchain‐based certificateless public auditing with privacy‐preserving for cloud‐based cyber‐physical systems is constructed. The proposed scheme is secure against type I, II, III, and IV adversaries in the random oracle model. Further, the original proof of the data is masked using a random function to achieve data privacy when data is transferred between the cloud‐server and verifier. The performance analysis shows that the proposed scheme has higher efficiency and is suitable for cyber‐physical systems.

The convenience of connecting through the internet and eagerness to spread any news through online social media is very intriguing as it can be done rapidly and with very little effort. This permits the very quick spread of fake news globally and misleads the people against democracy and freedom. The content of fake news very closely resembles true news. So, technically, it is tough for a deep neural network to 'detect and attend to' the 'fake only' aspects of a news article. Fake news detection is a significantly complex and challenging task from the aspect of deep learning-based attention mechanisms. The deep learning-based fake news detection systems suffer from indistinguishability of fake and real news/data, the curse of high dimensionality, the high training time of deep neural networks, the over-fitting of the network training, and the over-thinking problem. In this paper, a shallow-based convolution neural networks (SCNN) model has been proposed for the fake news detection system to overcome the mentioned issues. The proposed SCNN model is experimentally tested for a complex benchmark LIAR dataset. The performance of the proposed SCNN is better than other existing models in terms of accuracy, precision, recall and F1-score.

Abstract Fire accidents in vehicles lead to the loss of human lives. The fire detection and alarm systems are often error‐prone and respond to nonactual indications of fire presence, known as false alarms. The proposed model detects the fire at the smoldering stage and buzzes an alarm if an actual fire or smoke is detected. This system can achieve this real alarm using multiple internet of things‐based sensors, namely smoke/gas, flame, temperature, and a visualization camera. The visualization camera continuously captures images of the vehicle to check the existence of fire. Machine learning algorithms are executed on the sensor and image dataset to reduce false alarms and achieve high accuracy of results by using various performance metrics.