Ping Zhang¹, Haoran Zhu¹, Wenjun Li², Osama Alfarraj³, Amr Tolba³, Gwang-jun Kim^4,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3589-3605, 2023, DOI:10.32604/cmc.2023.032097

Abstract Many Internet of things application scenarios have the characteristics of limited hardware resources and limited energy supply, which are not suitable for traditional security technology. The security technology based on the physical mechanism has attracted extensive attention. How to improve the key generation rate has always been one of the urgent problems to be solved in the security technology based on the physical mechanism. In this paper, superlattice technology is introduced to the security field of Internet of things, and a high-speed symmetric key generation scheme based on superlattice for Internet of things is proposed. In order to ensure the… More >

Mao Yuxin, Wang Honglin^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 863-878, 2023, DOI:10.32604/cmc.2023.034879

Abstract In the financial sector, data are highly confidential and sensitive, and ensuring data privacy is critical. Sample fusion is the basis of horizontal federation learning, but it is suitable only for scenarios where customers have the same format but different targets, namely for scenarios with strong feature overlapping and weak user overlapping. To solve this limitation, this paper proposes a federated learning-based model with local data sharing and differential privacy. The indexing mechanism of differential privacy is used to obtain different degrees of privacy budgets, which are applied to the gradient according to the contribution degree to ensure privacy without… More >

Ze Xu, Sanxing Cao^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 861-881, 2023, DOI:10.32604/cmes.2023.025159

Abstract Multi-Source data plays an important role in the evolution of media convergence. Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data. However, it also faces serious problems in terms of protecting user and data privacy. Many privacy protection methods have been proposed to solve the problem of privacy leakage during the process of data sharing, but they suffer from two flaws: 1) the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain; 2) the inability to solve… More > Graphic Abstract

Yinggang Sun¹, Hongguo Zhang¹, Chao Ma^1,*, Hai Huang¹, Dongyang Zhan^2,3, Jiaxing Qu⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4043-4058, 2023, DOI:10.32604/cmc.2023.031684

Abstract The problem of data island hinders the application of big data in artificial intelligence model training, so researchers propose a federated learning framework. It enables model training without having to centralize all data in a central storage point. In the current horizontal federated learning scheme, each participant gets the final jointly trained model. No solution is proposed for scenarios where participants only provide training data in exchange for benefits, but do not care about the final jointly trained model. Therefore, this paper proposes a new boosted tree algorithm, called RPBT (the originator Rights Protected federated Boosted Tree algorithm). Compared with… More >

B. Murugeshwari^1,*, S. Rajalakshmi¹, K. Sudharson²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2293-2307, 2023, DOI:10.32604/csse.2023.029074

Abstract Imagine numerous clients, each with personal data; individual inputs are severely corrupt, and a server only concerns the collective, statistically essential facets of this data. In several data mining methods, privacy has become highly critical. As a result, various privacy-preserving data analysis technologies have emerged. Hence, we use the randomization process to reconstruct composite data attributes accurately. Also, we use privacy measures to estimate how much deception is required to guarantee privacy. There are several viable privacy protections; however, determining which one is the best is still a work in progress. This paper discusses the difficulty of measuring privacy while… More >

Vivekraj Mannayee^1,*, Thirumalai Ramanathan²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1545-1563, 2023, DOI:10.32604/iasc.2023.027675

Abstract Blockchain has recently sparked interest in both the technological and business firms. The Internet of Things's (IoT) core principle emerged due to the connectivity of several new technologies, including wireless technology, the Internet, embedded automation systems, and micro-electromechanical devices. Manufacturing environments and operations have been successfully converted by implementing recent advanced technology like Cloud Computing (CC), Cyber-Physical System (CSP), Information and Communication Technologies (ICT) and Enterprise Model, and other technological innovations into the fourth industrial revolution referred to as Industry 4.0. Data management is defined as the process of accumulation in order to make better business decisions, and process, secure… More >

J. Priya^*, C. Palanisamy

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 243-259, 2023, DOI:10.32604/iasc.2023.025719

Abstract The Internet of Things (IoT) and Cloud computing are gaining popularity due to their numerous advantages, including the efficient utilization of internet and computing resources. In recent years, many more IoT applications have been extensively used. For instance, Healthcare applications execute computations utilizing the user’s private data stored on cloud servers. However, the main obstacles faced by the extensive acceptance and usage of these emerging technologies are security and privacy. Moreover, many healthcare data management system applications have emerged, offering solutions for distinct circumstances. But still, the existing system has issues with specific security issues, privacy-preserving rate, information loss, etc.… More >

Hyun Sook Rhee^*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 69-85, 2022, DOI:10.32604/cmc.2022.026751

Abstract As the use of cloud storage for various services increases, the amount of private personal information along with data stored in the cloud storage is also increasing. To remotely use the data stored on the cloud storage, the data to be stored needs to be encrypted for this reason. Since “searchable encryption” is enable to search on the encrypted data without any decryption, it is one of convenient solutions for secure data management. A public key encryption with keyword search (for short, PEKS) is one of searchable encryptions. Abdalla et al. firstly defined IND-CCA security for PEKS to enhance it’s… More >

Meng-Wei Zhang¹, Xiu-Bo Chen¹, Haseeb Ahmad², Gang Xu^3,4,*, Yi-Xian Yang¹

Journal of Cyber Security, Vol.4, No.1, pp. 29-39, 2022, DOI:10.32604/jcs.2022.027693

Abstract Blockchain has a profound impact on all areas of society by virtue of its immutability, decentralization and other characteristics. However, blockchain faces the problem of data privacy leakage during the application process, and the rapid development of quantum computing also brings the threat of quantum attack to blockchain. In this paper, we propose a lattice-based certificateless fully homomorphic encryption (LCFHE) algorithm based on approximate eigenvector firstly. And we use the lattice-based delegate algorithm and preimage sampling algorithm to extract part of the private key based on certificateless scheme, which is composed of the private key together with the secret value… More >

Mohammed BinJubier¹, Mohd Arfian Ismail¹, Abdulghani Ali Ahmed^2,*, Ali Safaa Sadiq³

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3665-3686, 2022, DOI:10.32604/cmc.2022.024663

Abstract Publishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan, conduct, and assess scientific research. However, publishing and processing big data poses a privacy concern related to protecting individuals’ sensitive information while maintaining the usability of the published data. Several anonymization methods, such as slicing and merging, have been designed as solutions to the privacy concerns for publishing big data. However, the major drawback of merging and slicing is the random permutation procedure, which does not always guarantee complete protection against attribute or membership disclosure. Moreover,… More >