Deema Alsekait¹, Mohammed Zakariah², Syed Umar Amin^3,*, Zafar Iqbal Khan³, Jehad Saad Alqurni⁴

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2395-2436, 2024, DOI:10.32604/cmc.2024.055469 - 18 November 2024

Abstract The widespread adoption of Internet of Things (IoT) devices has resulted in notable progress in different fields, improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks. Further, the study suggests using an advanced approach that utilizes machine learning, specifically the Wide Residual Network (WRN), to identify hidden malware in IoT systems. The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices, using the MalMemAnalysis dataset. Moreover, thorough experimentation provides evidence for the effectiveness of the WRN-based strategy, resulting in… More >

Fang Hu¹, Siyi Qiu², Xiaolian Yang¹, Chaolei Wu¹, Miguel Baptista Nunes³, Hui Chen^4,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2897-2915, 2024, DOI:10.32604/cmc.2024.052570 - 15 August 2024

Abstract As the volume of healthcare and medical data increases from diverse sources, real-world scenarios involving data sharing and collaboration have certain challenges, including the risk of privacy leakage, difficulty in data fusion, low reliability of data storage, low effectiveness of data sharing, etc. To guarantee the service quality of data collaboration, this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning, termed FL-HMChain. This system is composed of three layers: Data extraction and storage, data management, and data application. Focusing on healthcare and medical data, a healthcare and… More >

Sandeep Dasari, Rajesh Kaluri^*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 2035-2051, 2024, DOI:10.32604/cmes.2024.049152 - 20 May 2024

Abstract The increasing data pool in finance sectors forces machine learning (ML) to step into new complications. Banking data has significant financial implications and is confidential. Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages. As a result, this study employs federated learning (FL) using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model. However, diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy. To address this issue, the… More > Graphic Abstract

Nagaraju Pacharla, K. Srinivasa Reddy^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3089-3110, 2024, DOI:10.32604/cmc.2024.048133 - 15 May 2024

Abstract With the recent technological developments, massive vehicular ad hoc networks (VANETs) have been established, enabling numerous vehicles and their respective Road Side Unit (RSU) components to communicate with one another. The best way to enhance traffic flow for vehicles and traffic management departments is to share the data they receive. There needs to be more protection for the VANET systems. An effective and safe method of outsourcing is suggested, which reduces computation costs by achieving data security using a homomorphic mapping based on the conjugate operation of matrices. This research proposes a VANET-based data outsourcing… More >

Hao Jiang¹, Yuerong Liao¹, Dongdong Zhao², Wenjian Luo³, Xingyi Zhang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1045-1075, 2024, DOI:10.32604/cmes.2024.048653 - 16 April 2024

Abstract Due to the presence of a large amount of personal sensitive information in social networks, privacy preservation issues in social networks have attracted the attention of many scholars. Inspired by the self-nonself discrimination paradigm in the biological immune system, the negative representation of information indicates features such as simplicity and efficiency, which is very suitable for preserving social network privacy. Therefore, we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks, called AttNetNRI. Specifically, a negative survey-based method is developed to disturb the relationship between nodes in the… More >

Fauziyah¹, Zhaoshun Wang^1,*, Mujahid Tabassum²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4417-4452, 2024, DOI:10.32604/cmc.2024.046797 - 26 March 2024

Abstract In an era characterized by digital pervasiveness and rapidly expanding datasets, ensuring the integrity and reliability of information is paramount. As cyber threats evolve in complexity, traditional cryptographic methods face increasingly sophisticated challenges. This article initiates an exploration into these challenges, focusing on key exchanges (encompassing their variety and subtleties), scalability, and the time metrics associated with various cryptographic processes. We propose a novel cryptographic approach underpinned by theoretical frameworks and practical engineering. Central to this approach is a thorough analysis of the interplay between Confidentiality and Integrity, foundational pillars of information security. Our method… More >

Ali Altalbe^1,2,*, Abdul Rehman Javed³

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2187-2200, 2023, DOI:10.32604/csse.2023.040624 - 28 July 2023

Abstract Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence (AI) algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy. Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s. Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients. The healthcare industry faces two significant challenges: security and privacy issues and the personalization of cloud-trained AI models. This paper proposes a Deep Neural Network (DNN) based More >

Dong Chen¹, Tanping Zhou^1,2,3,*, Xu An Wang^1,2, Zichao Song¹, Yujie Ding¹, Xiaoyuan Yang^1,2

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2477-2497, 2023, DOI:10.32604/iasc.2023.039238 - 21 June 2023

Abstract Smart grid (SG) brings convenience to users while facing great challenges in protecting personal private data. Data aggregation plays a key role in protecting personal privacy by aggregating all personal data into a single value, preventing the leakage of personal data while ensuring its availability. Recently, a flexible subset data aggregation (FSDA) scheme based on the Paillier homomorphic encryption was first proposed by Zhang et al. Their scheme can dynamically adjust the size of each subset and obtain the aggregated data in the corresponding subset. In this paper, firstly, an efficient attack with both theorems… More >

T. Haritha, A. Anitha^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3923-3939, 2023, DOI:10.32604/cmc.2023.036278 - 31 March 2023

Abstract In crowded cities, searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’ time, increases air pollution, and traffic congestion. Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement. But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number, personal identity, and desired destination. This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’ security… More >

Sultan Mesfer Aldossary^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3387-3404, 2023, DOI:10.32604/iasc.2023.035249 - 15 March 2023

Abstract With the emergence of cloud technologies, the services of healthcare systems have grown. Simultaneously, machine learning systems have become important tools for developing matured and decision-making computer applications. Both cloud computing and machine learning technologies have contributed significantly to the success of healthcare services. However, in some areas, these technologies are needed to provide and decide the next course of action for patients suffering from diabetic kidney disease (DKD) while ensuring privacy preservation of the medical data. To address the cloud data privacy problem, we proposed a DKD prediction module in a framework using cloud… More >