期刊
JOURNAL OF SYSTEMS ARCHITECTURE
卷 123, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.sysarc.2021.102347
关键词
CP-ABE; Access control; IoT; Cloud computing; Fog computing
With the rapid development of IoT, the need for efficient data analysis and processing has led to the development of fog computing. However, data security risks arise due to multiple levels of storage and computation. To address these issues, a multi-authority CP-ABE scheme named MACFI is proposed, which offers efficient ciphertext and secret key size, reduces computation overhead for data users, and is secure and suitable for IoT applications.
With the rapid development of Internet of things (IoT), the number of connected devices and the data generated by them are increasing dramatically. This led to the development of paradigm fog computing, which facilitates data analysis and processing at the edge. Along with fog, cloud co-exists to provide massive storage, processing resources, etc. However, data storage and computation at multiple levels raise the risk of data security. Ciphertext-policy attribute-based encryption (CP-ABE) is a well-known cryptographic technique for providing fine-grained access control. Unfortunately, the existing multi-authority CP-ABE schemes are incompatible with resource-limited IoT devices as the ciphertext and secret key size grow linearly with the number of attributes. Therefore, we propose a multi-authority CP-ABE scheme named MACFI, where the ciphertext and secret key size are efficient. The size of the secret key held by the user is constant irrespective of the number of attributes. And, the ciphertext size increases linearly with the number of authorities rather than the number of attributes. Further, expensive decryption operations are outsourced to fog, which reduces the computation overhead of data users. Additionally, the overall encryption and decryption time are highly efficient. According to security and performance analysis, MACFI is secure and suitable for IoT applications.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据