The Internet today lacks an identity protocol for identifying people and organizations. As a result, service providers needed to build and maintain their own databases of user information. This solution is costly to the service providers, inefficient as much of the information is duplicated across different providers, difficult to secure as evidenced by recent large-scale personal data breaches around the world, and cumbersome to the users who need to remember different sets of credentials for different services. Furthermore, personal information could be collected for data mining, profiling and exploitation without users' knowledge or consent. The ideal solution would be self-sovereign identity, a new form of identity management that is owned and controlled entirely by each individual user. This solution would include the individual's consolidated digital identity as well as their set of verified attributes that have been cryptographically signed by various trusted issuers. The individual provides proof of identity and membership by sharing relevant parts of their identity with the service providers. Consent for access may also be revoked hence giving the individual full control over its own data. This survey critically investigates different blockchain based identity management and authentication frameworks. A summary of the state-of-the-art blockchain based identity management and authentication solutions from year 2014 to 2018 is presented. The paper concludes with the open issues, main challenges and directions highlighted for future work in this area. In a nutshell, the discovery of this new mechanism disrupted the existing identity management and authentication solutions and by providing a more promising secure platform.
With the collection of massive amounts of data every day, big data analytics has emerged as an important trend for many organizations. These collected data can contain important information that may be key to solving wide-ranging problems, such as cyber security, marketing, healthcare, and fraud. To analyze their large volumes of data for business analyses and decisions, large companies, such as Facebook and Google, adopt analytics. Such analyses and decisions impact existing and future technology. In this paper, we explore how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce. We also discuss the data challenges introduced by big data according to the literature, including its six V's. Moreover, we investigate case studies of big data analytics on various techniques of such analytics, namely, text, voice, video, and network analytics. We conclude that big data analytics can bring positive changes in many fields, such as education, military, healthcare, politics, business, agriculture, banking, and marketing, in the future.INDEX TERMS Big data analytics, data analytics, deep learning, machine learning.
Software Defined Networking (SDN) is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs) brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN) SDN and Open Network Operating System (ONOS) controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability.
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