Advances in internet of things (IoT) and cloud computing technologies have led to the emergence of new applications such as in e-health domain bringing convenience for both physicians and patients. However, the development of these new technologies makes users' privacy vulnerable. The threats on private data may arise from service providers themselves voluntarily or by inadvertence. As a result, the data owner would like to ensure that the collected data are securely stored and accessed only by authorised users. In this paper, we propose a novel data-owner centric privacy model in IoT/cloud environment. Our model combines two promising paradigms for data privacy, which are attribute-based encryption (ABE) and blockchain, to strengthen the data-owner privacy protection. We propose a new scheme of ABE that is, in one hand, suitable to resource-constrained devices by externalising the computing capabilities, thanks to fog computing paradigm and, in the other hand, combined with a blockchain-based protocol to overcome a single point of trust and to enhance data-owner access control.

The widespread decentralized applications and Blockchain components significantly boost the security frameworks in many vertical applications and use-cases including different secured payment methods and smart contracts. The integral part of any smart contract is the validation of the stake-holder identity, in general, while ideally being achieved without the third-party involvement. Recent industrial research works introduce the sovereign-identity system, where Blockchain becomes a decentralized component to establish a self-certified identity and to avoid a centralized trust third party. Hence, the classification of distributed transactions with respect to identity validation across several users becomes more challenging, especially because of the massive and sensitive identities that are issued through many users and IoT devices and that are used to validate transactions. In this context, it is important to identify and classify the malicious and non-malicious types of transactions. Our proposed method achieves the target of identity classifications from variety of transaction data. Since different users may have different device usage patterns, the data samples and labels located on any individual device may follow a different distribution, which cannot represent the global data distribution. Therefore, the solution could be bi-focal to compensate the gap. This paper coins the approach of hybridizing the consensus where as to initiate a machine learning mechanism to collect the local data globally through a permission driven and a federated approach. We introduce here a Federated Reinforcement learning to be improvised for distributed independent data as a policy of consortium while binding the proof of consensus more centrally authenticated.

Summary The surge of the presence of personal mobile devices in multi‐environment makes a significant attention to the mobile cloud computing (MCC). Along with this concern, security issues also appear as a barrier to prevent the propagation of this trend. This paper focuses on an important feature in many security protocols and application, which is the device attestation in the MCC. The existing remote attestation mechanisms are currently used in trusted computing environment such as binary attestation and property‐based attestation. In this paper, by taking advantage of the combination of technologies and trends, such as trusted platform module, cloud computing, and bring your own device, we introduce property‐based token attestation to secure the mobile user in the enterprise cloud environment. In order to accomplish a secure MCC environment, security threats need to be studied and acted accordingly, and therefore, we first represent the common threats and then explain a novel attestation schema for addressing these threats by providing security proofs. In addition, Scyther is in use to verify the correctness of our protocol.

In this article we study the use of the Support Vector Machine technique to estimate the probability of the reception of a given transmission in a Vehicular Ad hoc NETwork (VANET). The transmission takes place between a vehicle and a RoadSide Unit (RSU) at a given distance and with a given transmission rate. The RSU computes the statistics of the receptions and is able to compute the percentage of successful transmissions versus the distance between the vehicle and the RSU and the transmission rate. Starting from this statistic, a Support Vector Machine (SVM) scheme can produce a model. Then, given a transmission rate and a distance between the vehicle and the RSU, the SVM technique can estimate the probability of a successful reception. This probability can be used to build an adaptive technique which optimizes the expected throughput between the vehicle and the RSU. Instead of using transmission values of a real experiment, we use the results of an analytical model of CSMA that is customized for 1D VANETs. The model we adopt to perform this task uses a Matern selection process to mimic the transmission in a CSMA IEEE 802.11p VANET. With this model we obtain a closed formula for the probability of successful transmissions. Thus with these results we can train an SVM model and predict other values for other couples : distance, transmission rate. The numerical results we obtain show that SVM seems very suitable to predict the reception probability in a VANET.

For every business, where the product escalation of an organization depends on proper distribution of product and services in a region, analyzing customer behavior over time in that region can produce useful results. In this article, we strive to develop a system, which when provided past customer records will predict the forthcoming customer behavior. The proposed system is specially designed to find patterns and predict changes in customer behavior for mobile service providers. The aggregation of comparison and referencing of behavioral patterns, both on off-line and on-line, represents a tremendous opportunity for understanding modeling past behaviors and predicting future behaviors. Several aspects of business and real life trends, both on and off the Web, modified over time from such observable changes, as the augmentation and evolution of contents, to more quasi yet important dynamics. The research effort in this article aimed at exploring the temporal dynamics of consumer behavior, investigating how the behavior can deploy, and predict changes of raised concern about a particular service or brand. Specifically, the informational goals behind the queries, and the search results, will finally affect the business while reducing rate of attrition of customer from particular legacy of product or services.

Service-oriented architectures are increasingly deployed in open, distributed and dynamic environments, which require an end-to-end security awareness security at each phase of the service's lifecycle. Moreover, the security should not only focus on services without considering the risks and threats that might be caused by elements from business activities or underlying hardware and software infrastructure. In this paper, we adopt a holistic approach to define a security conceptual model that covers all elements at the business, service and infrastructure levels and guides each phase in a typical design method for service-oriented architectures. Since the information security is subject to uncertain and unforeseen threats, we propose a fuzzy logic decision system that helps identify security risks based on the security conceptual model and select appropriate security measures based on security objectives.

The complexity of business and different variations of service providers inspire the sense of matching of the consumers with the most appropriate products and services. This specific attribute initiates the study of recommender systems, which analysis the patterns of user's interest in items or products and services to suggest personalized recommendations for all these verticals, and also to suit a user's taste and satisfaction. High quality recommendation demands perfect decision for classifying manifold options given to user against a particular query. Hence, research challenge remains that whether the decision taken is the optimal at the end of recommended options. In this paper coined Termite Colony Optimization (TCO) is proposed, which provides a decision making model, and it is used by termites to adjust their movement trajectories under the decision tree from web service portal. We strongly advocate that the emerging TCO could be better a choice to be used in recommender system and most importantly on a continuous data stream. The present approach is tested on a brand named as “Big Bazar” (Large Market) of India. Retail recommendation has continuous data and various constraints before achieving optimized suggestions. Empirical investigations demonstrate that Termite behavior and meta-heuristic approach is quite affin to offer optimized recommendations for specifi retail operation. The research also briefs about the potential benefi of such retail recommender model in reality.

This paper proposes a novel bio-inspired model that quantifies the quality aspect of Wiki content. Unlike the statistical measures, the proposed system automates the quality dispersion mechanism using ant colony's pheromone artifacts over Wiki content. The inclusion of artificial ant agents is relevant to the heuristic behavior of Wiki content management and reputation paradigm of Wiki. The proposed model generates substantial empirical and graphical evidences, which could be timely boosted to enhance Wiki culture and editing process of Wiki in order to be theoretically trusted and validated across the users. Observed results present evidences that despite of users' attribution, registered or anonymous, the proposed agent based model provides quantifying editing in content of Wiki and accordingly both commercial viability, in terms of quality, and vandalism can be ensured.

The oppositional and antipodal forms of forces, entities and quantities have been envisaged in the context of practical and applied field of engineering and management science in order to create a more complete picture of reality. The interplay between entities and opposite entities is apparently fundamental for maintaining universal balance. A large number of problems in engineering and science cannot be approached with conventional schemes and are generally handled with intelligent techniques such as evolutionary, neural, reinforcing and swarm-based methods. Visualization of unforeseen financial events is one of those applications, where failure of particular corporate firm can be forecasted based on the combination of several indicators. The hidden artifacts of corporate financial events could also be evaluated with the help of ant-based behavior of pheromone deposition. The learning in the pheromone deposition is subjected to oppositional forces leading towards the equilibrium of the corporate of interest. The motivation of this paper is to initiate the model to analyze and to represent the financial practices of typical clusters, which may cause failure of that firm in near future. In this paper we propose to use opposition based learning and Soft Bergman Based Clustering to implement the proposed model. Brief comparison of results is presented at the end of the proposal.