A Lightweight ABE Security Protection Scheme in Cloud Environment Based on Attribute Weight

Attribute-based encryption(ABE)is a technique used to encrypt data,it has the flexibility of access control,high security,and resistance to collusion attacks,and especially it is used in cloud security protection.However,a large number of bilinear mappings are used in ABE,and the calculation of bilinear pairing is time-consuming.So there is the problem of low efficiency.On the other hand,the decryption key is not uniquely associated with personal identification information,if the decryption key is maliciously sold,ABE is unable to achieve accountability for the user.In practical applications,shared message requires hierarchical sharing in most cases,in this paper,we present a message security hierarchy ABE scheme for this scenario.Firstly,attributes were grouped and weighted according to the importance of attributes,and then an access structure based on a threshold tree was constructed according to attribute weight.This method saved the computing time for decryption while ensuring security and on-demand access to information for users.In addition,with the help of computing power in the cloud,two-step decryption was used to complete the access,which relieved the computing and storage burden on the client side.Finally,we simulated and tested the scheme based on CP-ABE,and selected different security levels to test its performance.The security proof and the experimental simulation result showthat the proposed scheme has high efficiency and good performance,and the solution implements hierarchical access to the shared message.

Attribute Weighted Naïve Bayes Classifier

The naïve Bayes classifier is one of the commonly used data mining methods for classification.Despite its simplicity,naïve Bayes is effective and computationally efficient.Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning,this assumption may not hold in real-world applications.Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption.While these methods improve the classification performance,they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time.One approach to reduce the naïvetéof the classifier is to incorporate attribute weights in the conditional probability.In this paper,we proposed a method to incorporate attribute weights to naïve Bayes.To evaluate the performance of our method,we used the public benchmark datasets.We compared our method with the standard naïve Bayes and baseline attribute weighting methods.Experimental results show that our method to incorporate attribute weights improves the classification performance compared to both standard naïve Bayes and baseline attribute weighting methods in terms of classification accuracy and F1,especially when the independence assumption is strongly violated,which was validated using the Chi-square test of independence.

An Integrated FCEM-AHP Approach for Borrower’s Satisfaction and Perception Analysis of Microfinance Institution

Themain objective of this paper is to present an integrated approach to evaluate the level of satisfaction of borrowers with the products and services of microfinance institutions(MFI)at different criterion levels.For this,the study adopts the concept of FCEM(Fuzzy Comprehensive EvaluationMethod)in concurrence with the AHP(Analytical Hierarchy Process).In our day-to-day situation,the researchers have made many efforts to assess the impact of Microfinance on poverty reduction,but borrowers’satisfaction is always overlooked.Since the multiple factors impact the borrower’s satisfaction,each factor is made of different items.Thus,as the factors items increase,many uncertainties are created,and hence this will make the decisionmaking unsmooth or imprecise.To describe this,the FCEM method deals with the vagueness in the collection information phase.However,the AHP has been utilized to determine the objective weights of each factor.The presented integrated framework has been illustrated with a case study and presented their results.The study’s managerial benefit is also reported to address the situation.

Pattern discovery of long non-coding RNAs associated with the herbal treatments in breast and prostate cancers

Background:Accumulating evidence shows that long non-coding RNAs(lncRNAs)play critical roles in cancer progression.The possible association between lncRNAs and herbal medicine is yet to be known.This study aims to identify medicinal herbs associated with lncRNAs by RNA-seq data for breast and prostate cancer.Methods:To develop the optimal approach for identifying cancer-related lncRNAs,we implemented two steps:(1)applying protein–protein interaction(PPI),Gene Ontology(GO),and pathway analyses,and(2)applying attribute weighting and finding the efficient classification model of the machine learning approach.Results:In the first step,GO terms and pathway analyses on differential co-expressed mRNAs revealed that lncRNAs were widely co-expressed with metabolic process genes.We identified two hub lncRNA-mRNA networks that implicate lncRNAs associated with breast and prostate cancer.In the second step,we implemented various machine learning-based prediction systems(Decision Tree,Random Forest,Deep Learning,and Gradient-Boosted Tree)on the non-transformed and Z-standardized differential co-expressed lncRNAs.Based on five-fold cross-validation,we obtained high accuracy(91.11%),high sensitivity(88.33%),and high specificity(93.33%)in Deep Learning which reinforces the biomarker power of identified lncRNAs in this study.As data originally came from different cell lines at different durations of herbal treatment intervention,we applied seven attribute weighting algorithms to check the effects of variables on identifying lncRNAs.Attribute weighting results showed that the cell line and time had little or no effect on the selected lncRNAs list.Besides,we identified one known lncRNAs,downregulated RNA in cancer(DRAIC),as an essential feature.Conclusions:This study will provide further insights to investigate the potential therapeutic and prognostic targets for prostate cancer(PC)and breast cancer(BC)in common.

Analysis of Classical Encryption Techniques in Cloud Computing

Cloud computing has become a significant computing model in the IT industry. In this emerging model,computing resources such as software, hardware, networking, and storage can be accessed anywhere in the world on a pay-per-use basis. However, storing sensitive data on un-trusted servers is a challenging issue for this model. To guarantee confidentiality and proper access control of outsourced sensitive data, classical encryption techniques are used. However, such access control schemes are not feasible in cloud computing because of their lack of flexibility, scalability, and fine-grained access control. Instead, Attribute-Based Encryption（ABE） techniques are used in the cloud. This paper extensively surveys all ABE schemes and creates a comparison table for the key criteria for these schemes in cloud applications.

Bridging Real World Semantics to Model World Semantics for Taxonomy Based Knowledge Representation System

As a mean to map ontology concepts, a similarity technique is employed.Especially a context dependent concept mapping is tackled, which needs contextual information fromknowledge taxonomy. Context-based semantic similarity differs from the real world similarity in thatit requires contextual information to calculate similarity. The notion of semantic coupling isintroduced to derive similarity for a taxonomy-based system. The semantic coupling shows the degreeof semantic cohesiveness for a group of concepts toward a given context. In order to calculate thesemantic coupling effectively, the edge counting method is revisited for measuring basic semanticsimilarity by considering the weighting attributes from where they affect an edge''s strength. Theattributes of scaling depth effect, semantic relation type, and virtual connection for the edgecounting are considered. Furthermore, how the proposed edge counting method could be well adaptedfor calculating context-based similarity is showed. Thorough experimental results are provided forboth edge counting and context-based similarity. The results of proposed edge counting wereencouraging compared with other combined approaches, and the context-based similarity also showedunderstandable results. The novel contributions of this paper come from two aspects. First, thesimilarity is increased to the viable level for edge counting. Second, a mechanism is provided toderive a context-based similarity in taxonomy-based system, which has emerged as a hot issue in theliterature such as Semantic Web, MDR, and other ontology-mapping environments.