Fully homomorphic encryption scheme based on public key compression and batch processing

Chen, L., Lim, M. and Wang, M. (2018) Fully homomorphic encryption scheme based on public key compression and batch processing. In: Chen, X., Lin, D. and Yung, M. (eds.) Information Security and Cryptology. Series: Lecture notes in computer science (10726). Springer, pp. 242-259. ISBN 9783319751597 (doi: 10.1007/978-3-319-75160-3_16)

Abstract

Fully homomorphic encryption is a type of encryption technique that allows arbitrary complex operations to be performed on the ciphertext, thus generating an encrypted result that, when decrypted, matches the results of those operations performed on the plaintext. The DGHV scheme over the integers is one of the key schemes in fully homomorphic encryption research field, but the incredible size of the public key and the low computational efficiency are the main challenges. Based on the original DGHV encryption structure and parameters’ design, the idea of batch processing was introduced in this paper. With the combination of the quadratic parameter-based public key compression mechanism, a complete public key compression and batch processing fully homomorphic encryption (PKCB-FHE) scheme was presented. Like those in the original DGHV scheme, the parameter restriction of the proposed scheme was presented. Further analysis and simulation of the proposed scheme indicate that the required storage space of the public key is immensely reduced and that the overall length of the public key is compressed. Furthermore, the total processing time of the proposed scheme is reduced, which makes it much more efficient than those existing schemes.

• Item Type: Book Sections
• Additional Information: eISBN: 9783319751603.
• Status: Published
• Glasgow Author(s) Enlighten ID: Lim, Professor Ming
• Authors: Chen, L., Lim, M., and Wang, M.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Publisher: Springer
• ISBN: 9783319751597