The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been pre...
详细信息
With rapid development of computing technologies, large amount of data are gathered from edge terminals or Internet of Things (IoT) devices, however data trust and security in edge computing environment are very impor...
详细信息
With rapid development of computing technologies, large amount of data are gathered from edge terminals or Internet of Things (IoT) devices, however data trust and security in edge computing environment are very important issues to be considered, especially when the gathered data are fraud or dishonest, or the data are misused or spread without any authorization, which may lead to serious problems. In this article, a blockchain-based trusted data management scheme (called BlockTDM) in edge computing is proposed to solve the above problems, in which we proposed a flexible and configurable blockchain architecture that includes mutual authentication protocol, flexible consensus, smart contract, block and transaction datamanagement, blockchain nodes management, and deployment. The BlockTDM scheme can support matrix-based multichannel data segment and isolation for sensitive or privacy data protection, and moreover, we have designed user-defined sensitive data encryption before the transaction payload stores in blockchain system, and have implemented conditional access and decryption query of the protected blockchain data and transactions through smart contract. Finally, we have evaluated the proposed BlockTDM scheme security, availability, and efficiency with large amount of experiments. Analysis and evaluations manifest that the proposed BlockTDM scheme provides a general, flexible, and configurable blockchain-based paradigm for trusted data management with tamper-resistance, which is suitable for edge computing with high-level security and creditability.
Online Social Network (OSN) services have rapidly grown into a wide network and offer users a variety of benefits. However, they also bring fiew threats and privacy issues to the community. Unfortunately, there are at...
详细信息
Online Social Network (OSN) services have rapidly grown into a wide network and offer users a variety of benefits. However, they also bring fiew threats and privacy issues to the community. Unfortunately, there are attackers that attempt to expose OSN users' private information or conceal the information that the user desire to share with other users. Therefore, in this research we develop a framework that can provide trusted data management in OSN services. We first define the data types in OSN services and the states of shared data with respect to Optimal, Under-shared, Over-shared, and Hybrid states. We also identify the facilitating, detracting, and preventive parameters that are responsible for the state transition of the data. In a reliable OSN service, we address that a user should be able to set up his or her desired level of information sharing with a certain group of other users. However, it is not always clear to the ordinary users how to determine how much information they should reveal to others. In order to support such a decision, we propose an approach for helping OSN users to determine their optimum levels of information sharing, taking into consideration the payoffs (potential Reward or Cost) based on the Markov decision process (MDP). As an extension of the MDP-based approach, we also introduce a game theoretic approach, considering the interactions of OSN users and attackers with conflicting interests whose decisions affect each other's. Finally, after developing the framework for the optimal data sharing on OSNs, we conduct several experiments with attack simulation based on the proposed ideas and discuss the results. Our proposed approach has the capability to allow a large amount of variables to be altered to suit particular setups that an organization might have. (C) 2014 Elsevier Ltd. All rights reserved.
暂无评论