• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Wu Guangqiang. Multi-Authority CP-ABE with Policy Update in Cloud Storage[J]. Journal of Computer Research and Development, 2016, 53(10): 2393-2399. DOI: 10.7544/issn1000-1239.2016.20160432
Citation: Wu Guangqiang. Multi-Authority CP-ABE with Policy Update in Cloud Storage[J]. Journal of Computer Research and Development, 2016, 53(10): 2393-2399. DOI: 10.7544/issn1000-1239.2016.20160432

Multi-Authority CP-ABE with Policy Update in Cloud Storage

More Information
  • Published Date: September 30, 2016
  • Cloud storage, as a novel data storage architecture, has been widely used to provide services for data draw to store and share their data in cloud. However, the security concerns of cloud storage also draw much attention of the whole society. Since some cloud service providers are not trustworthy, the data stored in their cloud servers could be stolen or accessed by unauthorized users. Ciphertext-policy attribute based encryption (CP-ABE) can be used to solve such security problems in cloud, which can encrypt data under a specified access policy thus to maintain data confidentiality as well as access control. Unfortunately, traditional CP-ABE schemes suffer from key escrow problems and are lack of policy update. In this paper, we propose a new multi-authority CP-ABE scheme with policy update, which can efficiently cut down the computation cost and communication cost compared with other schemes in literature. We also prove the semantic security for our scheme, and also analyze its efficiency.
  • Related Articles

    [1]Zhang Zhenyu, Jiang Yuan. Label Noise Robust Learning Algorithm in Environments Evolving Features[J]. Journal of Computer Research and Development, 2023, 60(8): 1740-1753. DOI: 10.7544/issn1000-1239.202330238
    [2]Yang Wang, Gao Mingzhe, Jiang Ting. A Malicious Code Static Detection Framework Based on Multi-Feature Ensemble Learning[J]. Journal of Computer Research and Development, 2021, 58(5): 1021-1034. DOI: 10.7544/issn1000-1239.2021.20200912
    [3]Qi Qing, Cao Jian, Liu Yancen. The Evolution of Software Ecosystem in GitHub[J]. Journal of Computer Research and Development, 2020, 57(3): 513-524. DOI: 10.7544/issn1000-1239.2020.20190615
    [4]Ai Ke, Ma Guoshuai, Yang Kaikai, Qian Yuhua. A Classification Method of Scientific Collaborator Potential Prediction Based on Ensemble Learning[J]. Journal of Computer Research and Development, 2019, 56(7): 1383-1395. DOI: 10.7544/issn1000-1239.2019.20180641
    [5]Guo Yingjie, Liu Xiaoyan, Wu Chenxi, Guo Maozu, Li Ao. U-Statistics and Ensemble Learning Based Method for Gene-Gene Interaction Detection[J]. Journal of Computer Research and Development, 2018, 55(8): 1683-1693. DOI: 10.7544/issn1000-1239.2018.20180365
    [6]Zhang Hu, Tan Hongye, Qian Yuhua, Li Ru, Chen Qian. Chinese Text Deception Detection Based on Ensemble Learning[J]. Journal of Computer Research and Development, 2015, 52(5): 1005-1013. DOI: 10.7544/issn1000-1239.2015.20131552
    [7]Gong Shu, Qu Youli, and Tian Shengfeng. Supervised Learning of an Automatic Noisy Semantic Unit Filter for Multi-Document Summarization[J]. Journal of Computer Research and Development, 2013, 50(4): 873-882.
    [8]Fu Zhongliang. A Universal Ensemble Learning Algorithm[J]. Journal of Computer Research and Development, 2013, 50(4): 861-872.
    [9]Li Ming and Zhou Zhihua. Online Semi-Supervised Learning with Multi-Kernel Ensemble[J]. Journal of Computer Research and Development, 2008, 45(12): 2060-2068.
    [10]Zhan Dechuan and Zhou Zhihua. Ensemble-Based Manifold Learning for Visualization[J]. Journal of Computer Research and Development, 2005, 42(9): 1533-1537.

Catalog

    Article views (1241) PDF downloads (789) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return