Chunming Rong

Bio: Chunming Rong is an academic researcher from University of Stavanger. The author has contributed to research in topics: Cloud computing & Cloud computing security. The author has an hindex of 28, co-authored 241 publications receiving 3530 citations. Previous affiliations of Chunming Rong include University of Tokyo & Chinese Ministry of Education.

A Comprehensive Survey of Blockchain: From Theory to IoT Applications and Beyond

Abstract: As an innovated and revolutionized technology, blockchain has been applied in many fields, such as cryptocurrency, food traceability, identity management, or even market prediction. To discover its great potential, both industry and academia have paid great attention to it and numerous researches have been conducted. Based on the literature and industry whitepapers, in this survey, we unroll and structure the blockchain related discoveries and scientific results in many aspects. Particularly, we classify blockchain technologies into four layers and carry out a comprehensive study on the consensus strategies, the network, and the applications of blockchain. Different blockchain applications are put into the corresponding categories based on the fields, especially in Internet of Things (IoT). When introducing each layer, we not only organize and summarize the related works, but also discuss the fundamental issues and future research directions. We hope this survey could shed some light on the research of blockchain and serve as a guide for further studies.

Strengthen Cloud Computing Security with Federal Identity Management Using Hierarchical Identity-Based Cryptography

Abstract: More and more companies begin to provide different kinds of cloud computing services for Internet users at the same time these services also bring some security problems. Currently the majority of cloud computing systems provide digital identity for users to access their services, this will bring some inconvenience for a hybrid cloud that includes multiple private clouds and/or public clouds. Today most cloud computing system use asymmetric and traditional public key cryptography to provide data security and mutual authentication. Identity-based cryptography has some attraction characteristics that seem to fit well the requirements of cloud computing. In this paper, by adopting federated identity management together with hierarchical identity-based cryptography (HIBC), not only the key distribution but also the mutual authentication can be simplified in the cloud.

A survey of key management in ad hoc networks

Abstract: The wireless and dynamic nature of mobile ad hoc networks (MANETs) leaves them more vulnerable to security attacks than their wired counterparts. The nodes act both as routers and as communication end points. This makes the network layer more prone to security attacks. A main challenge is to judge whether or not a routing message originates from a trustworthy node. The solution thus far is cryptographically signed messages. The general assumption is that nodes in possession of a valid secret key can be trusted. Consequently, a secure and efficient key-management scheme is crucial. Keys are also required for protection of application data. However, the focus here is on network-layer management information. Whereas keymanagement schemes for the upper layers can assume an already running network service, schemes for the protection of the network layer cannot. Keys are a prerequisite to bootstrap a protected network service. This article surveys the state of the art within key management for ad hoc networks, and analyzes their applicability for network-layer security. The analysis puts some emphasis on their applicability in scenarios such as emergency and rescue operations, as this work was initiated by a study of security in MANETs for emergency and rescue operations.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).