Jin Wang¹, Wei Ou¹, Osama Alfarraj², Amr Tolba², Gwang-Jun Kim^3,*, Yongjun Ren⁴

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1805-1819, 2023, DOI:10.32604/csse.2023.029622 - 03 November 2022

Abstract Since transactions in blockchain are based on public ledger verification, this raises security concerns about privacy protection. And it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification, when the whole transaction on the chain is verified. In order to improve the efficiency and privacy protection of block data verification, this paper proposes an efficient block verification mechanism with privacy protection based on zero-knowledge proof (ZKP), which not only protects the privacy of users but also improves the speed of data block verification. There is no… More >

Waleed Halboob^1,*, Jalal Almuhtadi^1,2

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2071-2092, 2023, DOI:10.32604/csse.2023.024110 - 03 November 2022

Abstract Privacy preservation (PP) in Digital forensics (DF) is a conflicted and non-trivial issue. Existing solutions use the searchable encryption concept and, as a result, are not efficient and support only a keyword search. Moreover, the collected forensic data cannot be analyzed using existing well-known digital tools. This research paper first investigates the lawful requirements for PP in DF based on the organization for economic co-operation and development OECB) privacy guidelines. To have an efficient investigation process and meet the increased volume of data, the presented framework is designed based on the selective imaging concept and… More >

Hasanien K. Kuba¹, Mustafa A. Azzawi², Saad M. Darwish^3,*, Oday A. Hassen⁴, Ansam A. Abdulhussein⁵

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4119-4133, 2023, DOI:10.32604/cmc.2023.033182 - 31 October 2022

Abstract It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association… More >

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 - 31 October 2022

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… More >

Junhyoung Oh¹, Kyungho Lee^2,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3579-3606, 2023, DOI:10.32604/cmc.2023.031491 - 31 October 2022

Abstract As technology develops, the amount of information being used has increased a lot. Every company learns big data to provide customized services with its customers. Accordingly, collecting and analyzing data of the data subject has become one of the core competencies of the companies. However, when collecting and using it, the authority of the data subject may be violated. The data often identifies its subject by itself, and even if it is not a personal information that infringes on an individual’s authority, the moment it is connected, it becomes important and sensitive personal information that… More >

Walid El-Shafai^1,2, Hayam A. Abd El-Hameed³, Noha A. El-Hag⁴, Ashraf A. M. Khalaf³, Naglaa F. Soliman⁵, Hussah Nasser AlEisa^6,*, Fathi E. Abd El-Samie¹

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 719-732, 2023, DOI:10.32604/iasc.2023.031079 - 29 September 2022

Abstract Nowadays, the security of images or information is very important. This paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image security. First, the secret medical image is encrypted using Advanced Encryption Standard (AES) algorithm. Then, the secret report of the patient is embedded into the encrypted secret medical image with the Least Significant Bit (LSB) watermarking algorithm. After that, the encrypted secret medical image with the secret report is concealed in a cover medical image, using Kekre’s Median Codebook Generation (KMCG) algorithm. Afterwards, the stego-image obtained is split into 16 parts.… More >

Manjur Kolhar^*, Mohammed Misfer

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 529-542, 2023, DOI:10.32604/iasc.2023.030098 - 29 September 2022

Abstract Machine learning (ML) and cloud computing have now evolved to the point where they are able to be used effectively. Further improvement, however, is required when both of these technologies are combined to reap maximum benefits. A way of improving the system is by enabling healthcare workers to select appropriate machine learning algorithms for prediction and, secondly, by preserving the privacy of patient data so that it cannot be misused. The purpose of this paper is to combine these promising technologies to maintain the privacy of patient data during the disease prediction process. Treatment of… More >

Jieren Cheng¹, Zhenhao Liu^1,*, Yiming Shi¹, Ping Luo^1,2, Victor S. Sheng³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1923-1939, 2023, DOI:10.32604/cmc.2023.032758 - 22 September 2022

Abstract With the increasing number of smart devices and the development of machine learning technology, the value of users’ personal data is becoming more and more important. Based on the premise of protecting users’ personal privacy data, federated learning (FL) uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original data. However, since FL can still leak the user's original data by exchanging gradient information. The existing privacy protection strategy will increase the uplink time due to encryption measures. It is a huge challenge in terms of… More >

Mubarak Almutairi^*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1959-1983, 2023, DOI:10.32604/cmc.2023.031155 - 22 September 2022

Abstract The development and use of Internet of Things (IoT) devices have grown significantly in recent years. Advanced IoT device characteristics are mainly to blame for the wide range of applications that may now be achieved with IoT devices. Corporations have begun to embrace the IoT concept. Identifying true and suitable devices, security faults that might be used for bad reasons, and administration of such devices are only a few of the issues that IoT, a new concept in technological progress, provides. In some ways, IoT device traffic differs from regular device traffic. Devices with particular… More >

Mingsen Mo¹, Shan Ji², Xiaowan Wang^3,*, Ghulam Mohiuddin⁴, Yongjun Ren¹

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 37-53, 2023, DOI:10.32604/cmc.2023.028843 - 22 September 2022

Abstract Due to the extensive use of various intelligent terminals and the popularity of network social tools, a large amount of data in the field of medical emerged. How to manage these massive data safely and reliably has become an important challenge for the medical network community. This paper proposes a data management framework of medical network community based on Consortium Blockchain (CB) and Federated learning (FL), which realizes the data security sharing between medical institutions and research institutions. Under this framework, the data security sharing mechanism of medical network community based on smart contract and the… More >