Blockchain and multi-access edge com-puting(MEC)are two emerging promising tech-nologies that have received extensive attention from academia and industry.As a brand-new information storage,dissemination and managemen...Blockchain and multi-access edge com-puting(MEC)are two emerging promising tech-nologies that have received extensive attention from academia and industry.As a brand-new information storage,dissemination and management mechanism,blockchain technology achieves the reliable transmis-sion of data and value.While as a new computing paradigm,multi-access edge computing enables the high-frequency interaction and real-time transmission of data.The integration of communication and com-puting in blockchain-enabled multi-access edge com-puting networks has been studied without a systemat-ical view.In the survey,we focus on the integration of communication and computing,explores the mu-tual empowerment and mutual promotion effects be-tween the blockchain and MEC,and introduces the resource integration architecture of blockchain and multi-access edge computing.Then,the paper sum-marizes the applications of the resource integration ar-chitecture,resource management,data sharing,incen-tive mechanism,and consensus mechanism,and ana-lyzes corresponding applications in real-world scenar-ios.Finally,future challenges and potentially promis-ing research directions are discussed and present in de-tail.展开更多
The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks(SHNs).To enhance the precision of diagnosis,different participants in SHNs ...The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks(SHNs).To enhance the precision of diagnosis,different participants in SHNs share health data that contain sensitive information.Therefore,the data exchange process raises privacy concerns,especially when the integration of health data from multiple sources(linkage attack)results in further leakage.Linkage attack is a type of dominant attack in the privacy domain,which can leverage various data sources for private data mining.Furthermore,adversaries launch poisoning attacks to falsify the health data,which leads to misdiagnosing or even physical damage.To protect private health data,we propose a personalized differential privacy model based on the trust levels among users.The trust is evaluated by a defined community density,while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential privacy.To avoid linkage attacks in personalized differential privacy,we design a noise correlation decoupling mechanism using a Markov stochastic process.In addition,we build the community model on a blockchain,which can mitigate the risk of poisoning attacks during differentially private data transmission over SHNs.Extensive experiments and analysis on real-world datasets have testified the proposed model,and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.展开更多
基金the National Key Re-search and Development Program of China(No.2020YFB1807500)the National Natural Science Foundation of China(No.62102297,No.61902292)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(No.2020A1515110496)the Fundamen-tal Research Funds for the Central Universities(No.XJS210105,No.XJS201502)the Open Project of Shaanxi Key Laboratory of Information Communi-cation Network and Security(No.ICNS202005).
文摘Blockchain and multi-access edge com-puting(MEC)are two emerging promising tech-nologies that have received extensive attention from academia and industry.As a brand-new information storage,dissemination and management mechanism,blockchain technology achieves the reliable transmis-sion of data and value.While as a new computing paradigm,multi-access edge computing enables the high-frequency interaction and real-time transmission of data.The integration of communication and com-puting in blockchain-enabled multi-access edge com-puting networks has been studied without a systemat-ical view.In the survey,we focus on the integration of communication and computing,explores the mu-tual empowerment and mutual promotion effects be-tween the blockchain and MEC,and introduces the resource integration architecture of blockchain and multi-access edge computing.Then,the paper sum-marizes the applications of the resource integration ar-chitecture,resource management,data sharing,incen-tive mechanism,and consensus mechanism,and ana-lyzes corresponding applications in real-world scenar-ios.Finally,future challenges and potentially promis-ing research directions are discussed and present in de-tail.
基金supported by the National Key Research and Development Program of China(No.2021YFF0900400).
文摘The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks(SHNs).To enhance the precision of diagnosis,different participants in SHNs share health data that contain sensitive information.Therefore,the data exchange process raises privacy concerns,especially when the integration of health data from multiple sources(linkage attack)results in further leakage.Linkage attack is a type of dominant attack in the privacy domain,which can leverage various data sources for private data mining.Furthermore,adversaries launch poisoning attacks to falsify the health data,which leads to misdiagnosing or even physical damage.To protect private health data,we propose a personalized differential privacy model based on the trust levels among users.The trust is evaluated by a defined community density,while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential privacy.To avoid linkage attacks in personalized differential privacy,we design a noise correlation decoupling mechanism using a Markov stochastic process.In addition,we build the community model on a blockchain,which can mitigate the risk of poisoning attacks during differentially private data transmission over SHNs.Extensive experiments and analysis on real-world datasets have testified the proposed model,and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.