journal article·2023Open access

A Lightweight ABE Security Protection Scheme in Cloud Environment Based on Attribute Weight

Lihong Guo, Jie Yang, Haitao Wu

Computers, Materials & Continua· Volume 76· Issue 2

Abstract

Attribute-based encryption (ABE) is a technique used to encrypt data, it has the flexibility of access control, high security, and resistance to collusion attacks, and especially it is used in cloud security protection. However, a large number of bilinear mappings are used in ABE, and the calculation of bilinear pairing is time-consuming. So there is the problem of low efficiency. On the other hand, the decryption key is not uniquely associated with personal identification information, if the decryption key is maliciously sold, ABE is unable to achieve accountability for the user. In practical applications, shared message requires hierarchical sharing in most cases, in this paper, we present a message security hierarchy ABE scheme for this scenario. Firstly, attributes were grouped and weighted according to the importance of attributes, and then an access structure based on a threshold tree was constructed according to attribute weight. This method saved the computing time for decryption while ensuring security and on-demand access to information for users. In addition, with the help of computing power in the cloud, two-step decryption was used to complete the access, which relieved the computing and storage burden on the client side. Finally, we simulated and tested the scheme based on CP-ABE, and selected different security levels to test its performance. The security proof and the experimental simulation result show that the proposed scheme has high efficiency and good performance, and the solution implements hierarchical access to the shared message.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Cryptography and Data Security · Privacy-Preserving Technologies in Data · Cloud Data Security Solutions

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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