期刊：
Mathematical Problems in Engineering,2021年2021 ISSN：1024-123X
作者机构：
[Cheng, Qi] Wuhan Digital Engn Inst, Prod Engn Dept, Wuhan 430074, Peoples R China.;[Zhang, Maoyuan; Wu, Di; Yang, Qing; Zhao, Zhuo; Hsu, Chingfang] Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.
摘要：
Although nowadays lots of group key agreement schemes have been presented, most of these protocols generate a secret key for a single group. However, in the IoT HCS, more and more communications are involved in multiple groups and users can join multiple groups to communicate at the same time. Therefore, applying the conventional public-key-based one-at-a-time group key establishment protocols has heavy computational cost or suffer from security vulnerabilities. At the same time, in an IoT HCS, a trusted KGC is usually not available and so more flexible self-organized multigroup keys generation will be desired by all group members. In order to address this issue, a practical scheme for efficient and flexible KGC-free polynomial-based multigroup key establishments for IoT HCS is proposed. The proposed protocol can generate multiple group keys for all group members at once, instead of generating one key each time for a single group; more importantly, there is no need for a trusted KGC in the process of group keys establishment and each user can join multiple groups at the same time using only one reserved share. Meanwhile, the security of the proposed protocol is discussed in detail. Finally, we compare this protocol with the latest related group key distribution protocols in performance analysis. The results show that this efficient and flexible KGC-free polynomial-based multiple group keys establishment protocol is more suitable for practical group key agreement in IoT HCS.
期刊：
Wireless Communications and Mobile Computing,2021年2021 ISSN：1530-8669
作者机构：
[Ke, Lulu; Yang, Qing; Zhao, Zhuo; Hsu, Chingfang] Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.;[Harn, Lein] Univ Missouri, Dept Comp Sci Elect Engn, Kansas City, MO 64110 USA.
摘要：
Internet of Medical Things (IoMT) is a kind of Internet of Things (IoT) that includes patients and medical sensors. Patients can share real-time medical data collected in IoMT with medical professionals. This enables medical professionals to provide patients with efficient medical services. Due to the high efficiency of cloud computing, patients prefer to share gathering medical information using cloud servers. However, sharing medical data on the cloud server will cause security issues, because these data involve the privacy of patients. Although recently many researchers have designed data sharing schemes in medical domain for security purpose, most of them cannot guarantee the anonymity of patients and provide access control for shared health data, and further, they are not lightweight enough for IoMT. Due to these security and efficiency issues, a novel lightweight privacy-preserving data sharing scheme is constructed in this paper for IoMT. This scheme can achieve the anonymity of patients and access control of shared medical data. At the same time, it satisfies all described security features. In addition, this scheme can achieve lightweight computations by using elliptic curve cryptography (ECC), XOR operations, and hash function. Furthermore, performance evaluation demonstrates that the proposed scheme takes less computation cost through comparison with similar solutions. Therefore, it is fairly an attractive solution for efficient and secure data sharing in IoMT.
摘要：
This paper presents a personalized course recommended algorithm based on the hybrid recommendation. The recommendation algorithm uses the improved NewApriori algorithm to implements the association rule recommendation, and the user-based collaborative filtering algorithm is the main part of the algorithm. The hybrid algorithm adds the weight to the recommendation result of the user-based collaborative filtering and association rule recommendation, implementing a hybrid recommendation algorithm based on both of them. It has solved the problem of data sparsity and cold-start partially and provides a academic reference for the design of high performance elective system. The experiment uses the student scores data of a college as the test set and analyzes results and recommended quality of personalized elective course. According to the results of the experimental results, the quality of the improved hybrid recommendation algorithm is better.
作者机构：
[杨从平] Department of Economics and Management, Guangxi Normal University for Nationalities, Chongzuo, 532200, China;[郑世珏; 党永杰; 杨从平; 杨青] School of Computer, Central China Normal University, Wuhan, 430079, China
