Towards reputation-as-a-service

Reputation is used to regulate relationships of trust in online communities. When deploying a reputation system, it needs to accommodate the requirements and constrains of the specific community in order to assist the community to reach their goals. This paper identifies that there is a need for a framework to define a configurable reputation system with the ability to accommodate the requirements of a variety of online communities. Such a reputation system can be defined as a service on the Cloud, to be composed with the application environment of the online community. This paper introduces the concept of RaaS (Reputation-as-a-Service) and discusses a potential framework for creating a RaaS. In order to achieve such a framework, research is conducted into features of SaaS (Software-as-a-Service) components, user requirements for trust and reputation, and features of current reputation frameworks that can be configured in order to support a reputation service on the Cloud

Reliable data analysis through blockchain based crowdsourcing in mobile ad-hoc cloud

Mobile Ad-hoc Cloud (MAC) is the constellation of nearby mobile devices to serve the heavy computational needs of the resource constrained edge devices. One of the major challenges of MAC is to convince the mobile devices to offer their limited resources for the shared computational pool. Credit based rewarding system is considered as an effective way of incentivizing the arbitrary mobile devices for joining the MAC network and to earn the credits through computational crowdsourcing. The next challenge is to get the reliable computation as incentives attract the malicious devices to submit fake computational results for claiming their reward and we have used the blockchain based reputation system for identifying the malicious participants of MAC.This paper presents a malicious node identification algorithm integrated within the Iroha based permissioned blockchain. Iroha is a project of hyperledger which is focused on mobile devices and thus light-weight in nature. It is used for keeping the track of rewarding and reputation system driven by the malicious node detection algorithm. Experiments are conducted for evaluated the implemented test-bed and results show the effectiveness of algorithm in identifying the malicious devices and conducting the reliable data analysis through the blockchain based computational crowdsourcing in MAC

Towards distributed architecture for collaborative cloud services in community networks

Internet and communication technologies have lowered the costs for communities to collaborate, leading to new services like user-generated content and social computing, and through collaboration, collectively built infrastructures like community networks have also emerged. Community networks get formed when individuals and local organisations from a geographic area team up to create and run a community-owned IP network to satisfy the community’s demand for ICT, such as facilitating Internet access and providing services of local interest. The consolidation of today’s cloud technologies offers now the possibility of collectively built community clouds, building upon user-generated content and user-provided networks towards an ecosystem of cloud services. To address the limitation and enhance utility of community networks, we propose a collaborative distributed architecture for building a community cloud system that employs resources contributed by the members of the community network for provisioning infrastructure and software services. Such architecture needs to be tailored to the specific social, economic and technical characteristics of the community networks for community clouds to be successful and sustainable. By real deployments of clouds in community networks and evaluation of application performance, we show that community clouds are feasible. Our result may encourage collaborative innovative cloud-based services made possible with the resources of a community.Peer ReviewedPostprint (author’s final draft

Secure management of logs in internet of things

Ever since the advent of computing, managing data has been of extreme importance. With innumerable devices getting added to network infrastructure, there has been a proportionate increase in the data which needs to be stored. With the advent of Internet of Things (IOT) it is anticipated that billions of devices will be a part of the internet in another decade. Since those devices will be communicating with each other on a regular basis with little or no human intervention, plethora of real time data will be generated in quick time which will result in large number of log files. Apart from complexity pertaining to storage, it will be mandatory to maintain confidentiality and integrity of these logs in IOT enabled devices. This paper will provide a brief overview about how logs can be efficiently and securely stored in IOT devices.Comment: 6 pages, 1 tabl

Trust Management Model for Cloud Computing Environment

Software as a service or (SaaS) is a new software development and deployment paradigm over the cloud and offers Information Technology services dynamically as "on-demand" basis over the internet. Trust is one of the fundamental security concepts on storing and delivering such services. In general, trust factors are integrated into such existent security frameworks in order to add a security level to entities collaborations through the trust relationship. However, deploying trust factor in the secured cloud environment are more complex engineering task due to the existence of heterogeneous types of service providers and consumers. In this paper, a formal trust management model has been introduced to manage the trust and its properties for SaaS in cloud computing environment. The model is capable to represent the direct trust, recommended trust, reputation etc. formally. For the analysis of the trust properties in the cloud environment, the proposed approach estimates the trust value and uncertainty of each peer by computing decay function, number of positive interactions, reputation factor and satisfaction level for the collected information.Comment: 5 Pages, 2 Figures, Conferenc