4.7 Article

Hierarchical attribute based encryption with continuous leakage-resilience

期刊

INFORMATION SCIENCES
卷 484, 期 -, 页码 113-134

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2019.01.052

关键词

Attribute based encryption; Side channel attacks; Leakage-resilience; Secret key leakage

资金

  1. National Natural Science Foundation of China [U1736112, 61772009, 61672207]
  2. Jiangsu Provincial Natural Science Foundation of China [BK20161511]
  3. Priority Academic Program Development of Jiangsu Higher Education Institutions
  4. Fundamental Research Funds for the Central Universities [2016610114]
  5. Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, NJUPT
  6. Natural Science Foundation of the Jiangsu Higher Education Institutions of China [17KJB520042]
  7. Foundation of Suclian College [2016KY04]
  8. Qing Lan Project
  9. Jiangsu Innovation Program for GraduateEducation [KYZZ-0139]
  10. Sugian college outstanding young backbone teachers program
  11. cloud computing and big data security research team of Suclian College

向作者/读者索取更多资源

Attribute based encryption (ABE) is widely applied in cloud computing settings due to its fine-grained access control. Most ABE schemes do not consider the side channel attacks which may leak the secret information of cryptosystems. Leakage-resilient cryptography aims to model security for various side channel attacks. In this paper, we first give the formal definition and security model of hierarchical attribute based encryption (HABE) with continuous leakage-resilience. Furthermore, we present a ciphertext-policy HABE scheme with continuous leakage-resilience. The proposed scheme is resilient to master key leakage and secret key leakage. We prove the security of our scheme under composite order bilinear group assumptions by using dual system encryption techniques. The performance of leakage-resilience is analyzed theoretically. If the depth of our proposed scheme is 1, the relative leakage ratio is almost up to 1/3. In addition, we give the performance comparison through experiments. (C) 2019 Elsevier Inc. All rights reserved.

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