Jinqiu Hou¹, Changgen Peng^1,*, Weijie Tan^1,2, Hongfa Ding³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 917-938, 2024, DOI:10.32604/cmes.2023.027276 - 22 September 2023

Abstract Cloud-based services have powerful storage functions and can provide accurate computation. However, the question of how to guarantee cloud-based services access control and achieve data sharing security has always been a research highlight. Although the attribute-based proxy re-encryption (ABPRE) schemes based on number theory can solve this problem, it is still difficult to resist quantum attacks and have limited expression capabilities. To address these issues, we present a novel linear secret sharing schemes (LSSS) matrix-based ABPRE scheme with the fine-grained policy on the lattice in the research. Additionally, to detect the activities of illegal proxies, More >

Wangke Yu, Shuhua Wang^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 67-80, 2023, DOI:10.32604/cmc.2023.035337 - 06 February 2023

Abstract Wireless network is the basis of the Internet of things and the intelligent vehicle Internet. Due to the complexity of the Internet of things and intelligent vehicle Internet environment, the nodes of the Internet of things and the intelligent vehicle Internet are more vulnerable to malicious destruction and attacks. Most of the proposed authentication and key agreement protocols for wireless networks are based on traditional cryptosystems such as large integer decomposition and elliptic curves. With the rapid development of quantum computing, these authentication protocols based on traditional cryptography will be more and more threatened, so… More >

Joo Woo¹, Jonghyun Kim¹, Jong Hwan Park^2,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1985-2003, 2023, DOI:10.32604/cmc.2023.033102 - 22 September 2022

Abstract A fuzzy extractor can extract an almost uniform random string from a noisy source with enough entropy such as biometric data. To reproduce an identical key from repeated readings of biometric data, the fuzzy extractor generates a helper data and a random string from biometric data and uses the helper data to reproduce the random string from the second reading. In 2013, Fuller et al. proposed a computational fuzzy extractor based on the learning with errors problem. Their construction, however, can tolerate a sub-linear fraction of errors and has an inefficient decoding algorithm, which causes the reproducing time… More >