Nitish Pathak¹, Shams Tabrez Siddiqui², Anjani Kumar Singha³, Heba G Mohamed⁴, Shabana Urooj^4,*, Abhinandan R Patil⁵

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3021-3036, 2023, DOI:10.32604/iasc.2023.029053 - 17 August 2022

Abstract The coronavirus, formerly known as COVID-19, has caused massive global disasters. As a precaution, most governments imposed quarantine periods ranging from months to years and postponed significant financial obligations. Furthermore, governments around the world have used cutting-edge technologies to track citizens’ activity. Thousands of sensors were connected to IoT (Internet of Things) devices to monitor the catastrophic eruption with billions of connected devices that use these novel tools and apps, privacy and security issues regarding data transmission and memory space abound. In this study, we suggest a blockchain-based methodology for safeguarding data in the billions More >

Yining Qi^1,2,*, Yubo Luo³, Yongfeng Huang^1,2, Xing Li^1,2

Computer Systems Science and Engineering, Vol.45, No.1, pp. 183-199, 2023, DOI:10.32604/csse.2023.031030 - 16 August 2022

Abstract Progress in cloud computing makes group data sharing in outsourced storage a reality. People join in group and share data with each other, making team work more convenient. This new application scenario also faces data security threats, even more complex. When a user quit its group, remaining data block signatures must be re-signed to ensure security. Some researchers noticed this problem and proposed a few works to relieve computing overhead on user side. However, considering the privacy and security need of group auditing, there still lacks a comprehensive solution to implement secure group user revocation,… More >

S. Stewart Kirubakaran^1,*, V. P. Arunachalam¹, S. Karthik¹, S. Kannan²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 1881-1895, 2023, DOI:10.32604/csse.2023.026690 - 01 August 2022

Abstract In the present scenario of rapid growth in cloud computing models, several companies and users started to share their data on cloud servers. However, when the model is not completely trusted, the data owners face several security-related problems, such as user privacy breaches, data disclosure, data corruption, and so on, during the process of data outsourcing. For addressing and handling the security-related issues on Cloud, several models were proposed. With that concern, this paper develops a Privacy-Preserved Data Security Approach (PP-DSA) to provide the data security and data integrity for the outsourcing data in Cloud… More >

K. Saranya^*, K. Premalatha

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2029-2042, 2023, DOI:10.32604/iasc.2023.027949 - 19 July 2022

Abstract Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy. In this background, several authentication and accessibility issues emerge with an intention to protect the sensitive details of the patients over getting published in open domain. To solve this problem, Multi Attribute Case based Privacy Preservation (MACPP) technique is proposed in this study to enhance the security of privacy-preserving data. Private information can be any attribute information which is categorized as sensitive logs in a patient’s records. The semantic relation between transactional 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 - 29 March 2022

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… More >

D. Dhinakaran^1,*, P. M. Joe Prathap²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1877-1892, 2022, DOI:10.32604/iasc.2022.024509 - 24 March 2022

Abstract These days, investigations of information are becoming essential for various associations all over the globe. By and large, different associations need to perform information examinations on their joined data sets. Privacy and security have become a relentless concern wherein business experts do not desire to contribute their classified transaction data. Therefore, there is a requirement to build a proficient methodology that can process the broad mixture of data and convert those data into meaningful knowledge to the user without forfeiting the security and privacy of individuals’ crude information. We devised two unique protocols for frequent… More >

Zhe Sun¹, Jiyuan Feng¹, Lihua Yin^1,*, Zixu Zhang², Ran Li¹, Yu Hu¹, Chongning Na³

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1867-1886, 2022, DOI:10.32604/cmc.2022.022290 - 03 November 2021

Abstract Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data. However, the training mechanism for passing model parameters is still threatened by gradient inversion, inference attacks, etc. With a lightweight encryption overhead, function encryption is a viable secure aggregation technique in federation learning, which is often used in combination with differential privacy. The function encryption in federal learning still has the following problems: a) Traditional function encryption usually requires a trust third party (TTP) to assign the keys. If a TTP colludes with a server, the… More >

Yichuan Liu¹, Chungen Xu^1,*, Lei Xu¹, Lin Mei¹, Xing Zhang², Cong Zuo³

Journal of Information Hiding and Privacy Protection, Vol.3, No.4, pp. 151-164, 2021, DOI:10.32604/jihpp.2021.026944 - 22 March 2022

Abstract The widespread acceptance of machine learning, particularly of neural networks leads to great success in many areas, such as recommender systems, medical predictions, and recognition. It is becoming possible for any individual with a personal electronic device and Internet access to complete complex machine learning tasks using cloud servers. However, it must be taken into consideration that the data from clients may be exposed to cloud servers. Recent work to preserve data confidentiality has allowed for the outsourcing of services using homomorphic encryption schemes. But these architectures are based on honest but curious cloud servers,… More >

Jia-Shun Zhang¹, Gang Xu^2,*, Xiu-Bo Chen¹, Haseeb Ahmad³, Xin Liu⁴, Wen Liu^5,6,7

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2903-2916, 2021, DOI:10.32604/cmc.2021.017227 - 24 August 2021

Abstract With the rapid development of cloud computing technology, cloud services have now become a new business model for information services. The cloud server provides the IT resources required by customers in a self-service manner through the network, realizing business expansion and rapid innovation. However, due to the insufficient protection of data privacy, the problem of data privacy leakage in cloud storage is threatening cloud computing. To address the problem, we propose BC-PECK, a data protection scheme based on blockchain and public key searchable encryption. Firstly, all the data is protected by the encryption algorithm. The… More >

Shiqi Wang¹, Mingfang Jiang^2,*, Jiaohua Qin¹, Hengfu Yang², Zhichen Gao³

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2979-2993, 2021, DOI:10.32604/cmc.2021.017094 - 06 May 2021

Abstract In the era of big data, outsourcing massive data to a remote cloud server is a promising approach. Outsourcing storage and computation services can reduce storage costs and computational burdens. However, public cloud storage brings about new privacy and security concerns since the cloud servers can be shared by multiple users. Privacy-preserving feature extraction techniques are an effective solution to this issue. Because the Rotation Invariant Local Binary Pattern (RILBP) has been widely used in various image processing fields, we propose a new privacy-preserving outsourcing computation of RILBP over encrypted images in this paper (called More >