3篇
您的检索式：作者名="Longxia Huang"
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| 1 | Certificateless Public Verification for Data Storage and Sharing in the Cloud显示文摘By advances in cloud computing,users are allowed to remotely store their data in the cloud,manage the stored data without limitation of time and place,and give rights to visitors that want to access to their data.As may no longer possess data physically,the data owner has to ensure the integrity of the data with the public key given by Public key infrastructure(PKI).However,there are many security risks of the traditional PKI and the certificate management is complex.We utilize elliptic curve group to propose a certificateless signature to solve the above problem.To check the integrity of files stored in the cloud,we design a certificateless public verification mechanism based on the signature and further extend it to support batch auditing tasks.Meanwhile,an efficient key updating method is proposed to provide visitors a friendly data success environment.The security analysis proves that the proposed scheme is secure under the discrete logarithm assumption.Extensive theoretical analyses and experimental results show the effectiveness of the proposed scheme. | HUANG Longxia ZHOU Junlong ZHANG Gongxuan ZHANG Mingyue | 2020 | Chinese Journal of Electronics2020，29，4： | 3 |
| 2 | CExp: secure and verifiable outsourcing of composite modular exponentiation with single untrusted server显示文摘 | Shuai Li Longxia Huang Anmin Fu John Yearwood | 2017 | Digital Communications and Networks2017，3，4： | 2 |
| 3 | K-Means Clustering with Local Distance Privacy显示文摘With the development of information technology,a mass of data are generated every day.Collecting and analysing these data help service providers improve their services and gain an advantage in the fierce market competition.K-means clustering has been widely used for cluster analysis in real life.However,these analyses are based on users’data,which disclose users’privacy.Local differential privacy has attracted lots of attention recently due to its strong privacy guarantee and has been applied for clustering analysis.However,existing K-means clustering methods with local differential privacy protection cannot get an ideal clustering result due to the large amount of noise introduced to the whole dataset to ensure the privacy guarantee.To solve this problem,we propose a novel method that provides local distance privacy for users who participate in the clustering analysis.Instead of making the users’records in-distinguish from each other in high-dimensional space,we map the user’s record into a one-dimensional distance space and make the records in such a distance space not be distinguished from each other.To be specific,we generate a noisy distance first and then synthesize the high-dimensional data record.We propose a Bounded Laplace Method(BLM)and a Cluster Indistinguishable Method(CIM)to sample such a noisy distance,which satisfies the local differential privacy guarantee and local dE-privacy guarantee,respectively.Furthermore,we introduce a way to generate synthetic data records in high-dimensional space.Our experimental evaluation results show that our methods outperform the traditional methods significantly. | Mengmeng Yang Longxia Huang Chenghua Tang | 2023 | Big Data Mining and Analytics2023，6，4： | 0 |
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