As an extension of probability theory, evidence theory is able to better handle unknown and imprecise information. Owing toits advantages, evidence theory has more flexibility and effectiveness for modeling and processing uncertain information.Uncertainty measure plays an essential role both in evidence theory and probability theory.In probability theory, Shannon entropy provides a novel perspective for measuring uncertainty. Various entropies exist for measuring the uncertainty of basic probability assignment (BPA) in evidence theory. However, from the standpoint of the requirements of uncertainty measurement and physics, these entropies are controversial. Therefore, the process for measuring BPA uncertainty currently remains an open issue in the literature.Firstly, this paper reviews the measures of uncertainty in evidence theory followed by an analysis of some related controversies. Secondly, we discuss the development of Deng entropy as an effective way to measure uncertainty, including introducing its definition, analyzing its properties, and comparing it to other measures. We also examine the concept of maximum Deng entropy, the pseudo-Pascal triangle of maximum Deng entropy, generalized belief entropy, and measures of divergence.In addition, we conduct an analysis of the application of Deng entropy and further examine the challenges for future studies on uncertainty measurement in evidence theory. Finally, a conclusion is provided to summarize this study.

Uncertainty measure in evidence theory

The classification in class imbalanced data has drawn significant interest in medical application. Most existing methods are prone to categorize the samples into the majority class, resulting in bias, in particular the insufficient identification of minority class. A kind of novel approach, class weights random forest is introduced to address the problem, by assigning individual weights for each class instead of a single weight. The validation test on UCI data sets demonstrates that for imbalanced medical data, the proposed method enhanced the overall performance of the classifier while producing high accuracy in identifying both majority and minority class.

Class Weights Random Forest Algorithm for Processing Class Imbalanced Medical Data

Online Social Networks are perpetually evolving and used in plenteous applications such as content sharing, chatting, making friends/followers, customer engagements, commercials, product reviews/promotions, online games, and news, etc. The substantial issues related to the colossal flood of social spam in social media are polarizing sentiments, impacting users’ online interaction time, degrading available information quality, network bandwidth, computing power, and speed. Simultaneously, groups of coordinated automated accounts/bots often use social networking sites to spread spam, rumors, bogus reviews, and fake news for targeted users or mass communication. The latest developments in the form of artificial intelligence-enabled Deepfakes have exacerbated these issues at large. Consequently, it becomes extremely relevant to review recent work concerning social spam and spammer detection to counter this issue and its effect. This paper provides a brief introduction to social spam, the spamming process, and social spam taxonomy. The comprehensive review entails several dimensionality reduction techniques used for feature selection/extraction, features used, various machine learning and deep learning techniques used for social spam and spammer detection, and their merits and demerits. Artificial intelligence and deep learning empowered Deepfake (text, image, and video) spam, and their countermeasures are also explored. Furthermore, meticulous discussions, existing challenges, and emerging issues such as robustness of detection systems, scalability, real-time datasets, evade strategies used by spammers, coordinated inauthentic behavior, and adversarial attacks on machine learning-based spam detectors, etc., have been discussed with possible directions for future research.

A review on social spam detection: Challenges, open issues, and future directions

How to manage the uncertainty of the basic probability assignment accurately and efficiently is of significance and also an open issue. Plenty of functions have been established to cover the issue, especially Deng entropy recently. Deng entropy can deal with the more complex situation of the focal elements (propositions). However, Deng entropy has some limitations when the propositions are of the intersection. In this paper, a modified function is proposed by considering the scale of the frame of discernment and the influence of the intersection between statements on uncertainty. The proposed belief entropy provides a promising way to measure the uncertain information. Some numerical examples and an application in pattern recognition are used to show the efficiency and accuracy of the proposed belief entropy.

An Improved Deng Entropy and Its Application in Pattern Recognition

Electronic health records (EHRs) replaced the old paper-based systems to make patient data more accurate, reliable, and more accessible. Yet, the EHRs system requires high transmission cost, energy, and waste of time for both doctors and patients. Furthermore, EHRs security presents a serious issue threatening the patient's privacy. Most of the third-party hosting systems have some issues related to the users' privacy and data security. Hence, it is necessary to restrict the access control policies and develop efficient mechanisms for cloud-based EHRs data. In this paper, a sensitive and energetic access control (SE-AC) mechanism is proposed for managing the cloud-hosted EHRs and providing a fine-grained access control even in critical situations. The proposed mechanism ensures the confidentiality of the patient's data, where only authorized individuals to have permission to be able to edit or review certain of the patient's data. Each EHR data is encrypted by the managing authority before submitting to the cloud storage. The requesting user can get dynamically changing permissions based on authentication and context attributes. In addition, seven major aspects have been quantified to assess the operation of any access control that could be deployed in the Internet-of-Thing (IoT). The security analysis indicates that the SE-AC mechanism is secure and will prevent any unauthorized access. The results show exceptional compatibility and performance with different setups and configuration.

/pdf/sensitive-and-energetic-iot-access-control-for-managing-4ew67hnadd.pdf

Sensitive and Energetic IoT Access Control for Managing Cloud Electronic Health Records

To solve the existing problem of the multimedia social networks platforms, for instance, the copyrighted or private multimedia content can not be safely shared among users, we proposed a novel authorization delegation scheme based on the proxy re-encryption mechanism This scheme enables one user to delegate digital rights/privileges to another or the group of users, and achieves fine-grained authorization delegation The ciphertext of content encryption key (CEK) was re-encrypted by the proxy with re-encryption key, which is generated by delegator, and then the ciphertext was sent to the delegatee only who could decrypt the ciphertext The CCA security of proxy re-encryption was formally proved that this scheme realized the authorization delegation while ensuring the confidentiality of both the sharing content and the delegator's private key Besides, the privileges can be revoked when the usage control policies have not yet been satisfied Finally, we demonstrated in detail that the proposed scheme was applied to a multimedia social network prototype called by CyVOD MSN, and it achieved the security share of multimedia content and the functionality of digital rights management, together with better resolving the issue of limited access control list

A novel authorization delegation scheme for multimedia social networks by using proxy re-encryption

Conflict management is a hot research topic in Dempster–Shafer theory which is used to avoid the counterintuition problem of combination results. In the present conflict management methods, conflicting evidence is assigned to smaller weight to reduce its influence on the combination result. However, these methods will be disabled when similarity collision occurs. In this article, a new conflict management method based on similarity and Basic Probability Assignment-Matrix is proposed; in the proposed method, similarity collision is diminished by computing the index of each element in evidence, and then, a more reasonable evidence weight can be determined in this way. In the end, two experiments are set to compare the results of several different methods, and the results illustrate that the conflicting evidence will be assigned to a smaller weight in our method than in others.

https://journals.sagepub.com/doi/pdf/10.1177/1550147717696506

A new conflict management method in Dempster–Shafer theory:

Trust is an important criterion for access control in the field of online social networks privacy preservation. In the present methods, the subjectivity and individualization of the trust is ignore...

https://journals.sagepub.com/doi/pdf/10.1177/1550147718794629

Trust evaluation based on evidence theory in online social networks

Digital content trusted usage control method based on cloud computing

This paper proposes a novel hash function construction based on cascade chaos Firstly, the L-length original message is divided into a number of blocks according to every character, which are transformed into corresponding ASCII values Then the ASCII values are normalized into the interval of (0, 1), which compose a big array with the L-length Regarding the first normalized ASCII value with the algorithm parameter H as the initial input of the cascade chaos, a series of sequences are obtained, where the CBC mode is adopted The 128-bit hash value is generated by transforming the sequences into binary sequence according to a rule Simulation results and performance analysis show that the proposed hash function has high sensitivity to the original message and system parameters, strong confusion and diffusion, excellent statistical properties, strong collision resistance and flexible to generated 160, 256, 512-bit hash value after modifying the algorithm slightly

One-Way Hash Function based on Cascade Chaos

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