Visible to the public An Optimal Fully Homomorphic Encryption Scheme

TitleAn Optimal Fully Homomorphic Encryption Scheme
Publication TypeConference Paper
Year of Publication2017
AuthorsGai, K., Qiu, M.
Conference Name2017 ieee 3rd international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids)
Date Publishedmay
KeywordsAlgorithm design and analysis, cloud computing, Computing Theory, cryptography, cybersecurity, Data processing, distributed computing, Encryption, FHE scheme, fully homomorphic encryption, Human Behavior, human factor, KP, Kronecker product, Mathematical model, O-FHE scheme, optimal fully homomorphic encryption scheme, privacy, privacy concern, pubcrawl, resilience, Resiliency, Scalability, security concern, System analysis and design, tensor theory, user data protection
Abstract

The expeditious expansion of the networking technologies have remarkably driven the usage of the distributedcomputing as well as services, such as task offloading to the cloud. However, security and privacy concerns are restricting the implementations of cloud computing because of the threats from both outsiders and insiders. The primary alternative of protecting users' data is developing a Fully Homomorphic Encryption (FHE) scheme, which can cover both data protections and data processing in the cloud. Despite many previous attempts addressing this approach, none of the proposed work can simultaneously satisfy two requirements that include the non-noise accuracy and an efficiency execution. This paper focuses on the issue of FHE design and proposes a novel FHE scheme, which is called Optimal Fully Homomorphic Encryption (O-FHE). Our approach utilizes the properties of the Kronecker Product (KP) and designs a mechanism of achieving FHE, which consider both accuracy and efficiency. We have assessed our scheme in both theoretical proofing and experimental evaluations with the confirmed and exceptional results.

URLhttps://ieeexplore.ieee.org/document/7980325/
DOI10.1109/BigDataSecurity.2017.43
Citation Keygai_optimal_2017