Biblio

This paper has firstly introduced big data services and cloud computing model based on different process forms, and analyzed the authentication technology and security services of the existing big data to understand their processing characteristics. Operation principles and complexity of the big data services and cloud computing have also been studied, and summary about their suitable environment and pros and cons have been made. Based on the Cloud Computing, the author has put forward the Model of Big Data Cloud Computing based on Extended Subjective Logic (MBDCC-ESL), which has introduced Jφsang's subjective logic to test the data credibility and expanded it to solve the problem of the trustworthiness of big data in the cloud computing environment. Simulation results show that the model works pretty well.

Computational trust is the digital counterpart of the human notion of trust as applied in social systems. Its main purpose is to improve the reliability of interactions in online communities and of knowledge transfer in information management systems. Trust models are formal frameworks in which the notion of computational trust is described rigorously and where its dynamics are explained precisely. In this paper we will consider and extend a computational trust model, i.e., JØsang's Subjective Logic: we will show how this model is well-suited to describe the dynamics of computational trust, but lacks effective tools to compute initial trust values to feed in the model. To overcome some of the issues with subjective logic, we will introduce a logical language which can be employed to describe and reason about trust. The core ideas behind the logical language will turn out to be useful in computing initial trust values to feed into subjective logic. The aim of the paper is, therefore, that of providing an improvement on subjective logic.

In recent years, the number of smart mobile devices has rapidly increased worldwide. This explosion of continuously connected mobile devices has resulted in an exponential growth in the number of publically available mobile Apps. To facilitate the selection of mobile Apps, from various available choices, the App distribution platforms typically rank/recommend Apps based on average star ratings, the number of downloads, and associated reviews - the external aspect of an App. However, these ranking schemes typically tend to ignore critical internal aspects (e.g., security vulnerabilities) of the Apps. Such an omission of internal aspects is certainly not desirable, especially when many of the users do not possess the necessary skills to evaluate the internal aspects and choose an App based on the default ranking scheme which uses the external aspect. In this paper, we build upon our earlier efforts by focusing specifically on the security-related internal aspect of an App and its combination with the external aspect computed from the user reviews by identifying security-related comments.We use this combination to rank-order similar Apps. We evaluate our approach on publicly available Apps from the Google PlayStore and compare our ranking with prevalent ranking techniques such as the average star ratings. The experimental results indicate the effectiveness of our proposed approach.

We propose an approach to enforce security in disruption- and delay-tolerant networks (DTNs) where long delays, high packet drop rates, unavailability of central trusted entity etc. make traditional approaches unfeasible. We use trust model based on subjective logic to continuously evaluate trustworthiness of security credentials issued in distributed manner by network participants to deal with absence of centralised trusted authorities.

Data clustering is an important topic in data science in general, but also in user modeling and recommendation systems. Some clustering algorithms like K-means require the adjustment of many parameters, and force the clustering without considering the clusterability of the dataset. Others, like DBSCAN, are adjusted to a fixed density threshold, so can't detect clusters with different densities. In this paper we propose a new clustering algorithm based on the mutual vote, which adjusts itself automatically to the dataset, demands a minimum of parameterizing, and is able to detect clusters with different densities in the same dataset. We test our algorithm and compare it to other clustering algorithms for clustering users, and predict their purchases in the context of recommendation systems.