| Title | Multi-Authority Attribute-Based Encryption for Resource-Constrained Users in Edge Computing |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Huang, Kaiqing |
| Conference Name | 2019 International Conference on Information Technology and Computer Application (ITCA) |
| Keywords | Access Control, attribute-based encryption, authorisation, ciphertext pool, Computational modeling, cryptography, data privacy, edge computing, Encryption, encryption algorithm, fine-grained access control, Human Behavior, Internet of Things, IoT, large-universe MA-CP-ABE scheme, MA-ABE schemes, Multi-Authority Attribute-Based Encryption, multiauthority attribute-based encryption, Online/Offline Encryption, outsourced decryption, Performance analysis, policy-based governance, pubcrawl, Scalability |
| Abstract | Multi-authority attribute-based encryption (MA-ABE) is a promising technique to protect data privacy and achieve fine-grained access control in edge computing for Internet of Things (IoT). However, most of the existing MA-ABE schemes suffer from expensive computational cost in the encryption and decryption phases, which are not practical for resource constrained users in IoT. We propose a large-universe MA-CP-ABE scheme with online/offline encryption and outsourced decryption. In our scheme, most expensive encryption operations have been executed in the user's initialization phase by adding reusable ciphertext pool besides splitting the encryption algorithm to online encryption and offline encryption. Moreover, massive decryption operation are outsourced to the near edge server for reducing the computation overhead of decryption. The proposed scheme is proven statically secure under the q-DPBDHE2 assumption. The performance analysis results indicate that the proposed scheme is efficient and suitable for resource-constrained users in edge computing for IoT. |
| DOI | 10.1109/ITCA49981.2019.00078 |
| Citation Key | huang_multi-authority_2019 |
Multi-Authority Attribute-Based Encryption for Resource-Constrained Users in Edge Computing