GPU Acceleration of Ciphertext-Policy Attribute-Based Encryption

With the development of cloud computing, data security became popular in recent decades. However, traditional cryptography has some major limitations. For example, public key cryptography is not scalable in cases with many clients. Since Ciphertext-Policy Attribute-based encryption (CP-ABE) was deve...

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Vydáno v:	2019 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) s. 94 - 101
Hlavní autoři:	Fan, Kai, Zhang, Chaoyu, Shan, Ruiwen, Yu, Hexuan, Jiang, Hai
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.07.2019
Témata:	Acceleration Cloud computing CP-ABE Encryption GPU Graphics processing units Parallel Security Software Software engineering Solids
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Popis
Shrnutí:	With the development of cloud computing, data security became popular in recent decades. However, traditional cryptography has some major limitations. For example, public key cryptography is not scalable in cases with many clients. Since Ciphertext-Policy Attribute-based encryption (CP-ABE) was developed in 2007, it has become as one of the major candidates to implement secure cloud storage. However, CP-ABE still cannot play a solid role due to its several limitations such as complexity of computation, lack efficiency revocation function, etc. This paper will review the CP-ABE and analyze the current CP-ABE toolkit. Major performance bottleneck will be identified and parallelized in CUDA. CP-ABE toolkit will be partially ported to GPU platform for acceleration. Some experiments have been conducted to demonstrate the effectiveness of the proposed approach.
DOI:	10.1109/SNPD.2019.8935702