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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment
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作者 Zhang Lejun Peng Minghui +6 位作者 su Shen Wang Weizheng Jin Zilong su yansen Chen Huiling Guo Ran Sergey Gataullin 《China Communications》 SCIE CSCD 2024年第3期149-159,共11页
With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for clou... With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes.The storage capacity of edge nodes close to users is limited.We should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem.Our paper proposes a redundant data detection method that meets the privacy protection requirements.By scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data.It has the same effect as zero-knowledge proof,and it will not reveal the privacy of users.In addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data.We use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is deleted.The feasibility of the scheme is proved through safety analysis and efficiency analysis. 展开更多
关键词 blockchain data integrity edge computing privacy protection redundant data
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基于多层基因网络的关键基因识别算法
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作者 魏丕静 刘晶晶 +2 位作者 赵永敏 苏延森 郑春厚 《生物信息学》 2023年第4期277-285,共9页
疾病关键基因可用于疾病诊断、预测和新药或新疗法有效性的评价,故识别与疾病紧密相关的关键基因十分重要。然而现在有些疾病样本数据较少,传统基于大样本的关键基因挖掘方法不适用于该类数据。本文针对含少量样本数据的疾病,首先利用... 疾病关键基因可用于疾病诊断、预测和新药或新疗法有效性的评价,故识别与疾病紧密相关的关键基因十分重要。然而现在有些疾病样本数据较少,传统基于大样本的关键基因挖掘方法不适用于该类数据。本文针对含少量样本数据的疾病,首先利用单样本网络构建方法构建每个疾病样本的个体化基因网络,并通过建立基因间的层间联系构建多层基因网络。然后利用基于张量的多层网络中心性方法评估每层网络中基因间的相互作用以及层间影响,对基因进行重要性打分,识别疾病关键基因。最后将该方法应用到哮喘数据集上,并与经典算法进行比较,结果表明,利用该方法所识别的已获批准的药物靶标基因的排名较优;对所得到的新的潜在关键基因TP53、PUS10、MAP3K1等进行功能和通路富集分析,结果表明其与哮喘有紧密关联。 展开更多
关键词 多层基因网络 随机游走 节点中心性 关键基因
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