With the rapid advancement of cloud computing,cloud storage services have developed rapidly.One issue that has attracted particular attention in such remote storage services is that cloud storage servers are not enoug...With the rapid advancement of cloud computing,cloud storage services have developed rapidly.One issue that has attracted particular attention in such remote storage services is that cloud storage servers are not enough to reliably save and maintain data,which greatly affects users’confidence in purchasing and consuming cloud storage services.Traditional data integrity auditing techniques for cloud data storage are centralized,which faces huge security risks due to single-point-of-failure and vulnerabilities of central auditing servers.Blockchain technology offers a new approach to this problem.Many researchers have endeavored to employ the blockchain for data integrity auditing.Based on the search of relevant papers,we found that existing literature lacks a thorough survey of blockchain-based integrity auditing for cloud data.In this paper,we make an in-depth survey on cloud data integrity auditing based on blockchain.Firstly,we cover essential basic knowledge of integrity auditing for cloud data and blockchain techniques.Then,we propose a series of requirements for evaluating existing Blockchain-based Data Integrity Auditing(BDIA)schemes.Furthermore,we provide a comprehensive review of existing BDIA schemes and evaluate them based on our proposed criteria.Finally,according to our completed review and analysis,we explore some open issues and suggest research directions worthy of further efforts in the future.展开更多
It is challenging to cluster multi-view data in which the clusters have overlapping areas.Existing multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them int...It is challenging to cluster multi-view data in which the clusters have overlapping areas.Existing multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering errors.Our solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping areas.MvDKE offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective function.Experiments on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grant 62072351in part by the Academy of Finland under Grant 308087,Grant 335262,Grant 345072,and Grant 350464+1 种基金in part by the Open Project of Zhejiang Lab under Grant 2021PD0AB01and in part by the 111 Project under Grant B16037.
文摘With the rapid advancement of cloud computing,cloud storage services have developed rapidly.One issue that has attracted particular attention in such remote storage services is that cloud storage servers are not enough to reliably save and maintain data,which greatly affects users’confidence in purchasing and consuming cloud storage services.Traditional data integrity auditing techniques for cloud data storage are centralized,which faces huge security risks due to single-point-of-failure and vulnerabilities of central auditing servers.Blockchain technology offers a new approach to this problem.Many researchers have endeavored to employ the blockchain for data integrity auditing.Based on the search of relevant papers,we found that existing literature lacks a thorough survey of blockchain-based integrity auditing for cloud data.In this paper,we make an in-depth survey on cloud data integrity auditing based on blockchain.Firstly,we cover essential basic knowledge of integrity auditing for cloud data and blockchain techniques.Then,we propose a series of requirements for evaluating existing Blockchain-based Data Integrity Auditing(BDIA)schemes.Furthermore,we provide a comprehensive review of existing BDIA schemes and evaluate them based on our proposed criteria.Finally,according to our completed review and analysis,we explore some open issues and suggest research directions worthy of further efforts in the future.
基金supported in part by the Youth Foundation of Shanxi Province(5113240053)the Fundamental Research Funds for the Central Universities(G2023KY05102)+2 种基金the Natural Science Foundation of China(61976120)the Natural Science Foundation of Jiangsu Province(BK20231337)the Natural Science Key Foundation of Jiangsu Education Department(21KJA510004)。
文摘It is challenging to cluster multi-view data in which the clusters have overlapping areas.Existing multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering errors.Our solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping areas.MvDKE offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective function.Experiments on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.
