| Title | A Homomorphic Cloud Framework for Big Data Analytics Based on Elliptic Curve Cryptography |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Salman, Zainab, Hammad, Mustafa, Al-Omary, Alauddin Yousif |
| Conference Name | 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) |
| Keywords | Big Data, Big data analytic, cloud computing, cloud-based framework, clustering, composability, cryptography, data privacy, Elliptic curve cryptography, homomorphic encryption, Informatics, Metrics, privacy, pubcrawl, resilience, Resiliency, Scalability, Technological innovation |
| Abstract | Homomorphic Encryption (HE) comes as a sophisticated and powerful cryptography system that can preserve the privacy of data in all cases when the data is at rest or even when data is in processing and computing. All the computations needed by the user or the provider can be done on the encrypted data without any need to decrypt it. However, HE has overheads such as big key sizes and long ciphertexts and as a result long execution time. This paper proposes a novel solution for big data analytic based on clustering and the Elliptical Curve Cryptography (ECC). The Extremely Distributed Clustering technique (EDC) has been used to divide big data into several subsets of cloud computing nodes. Different clustering techniques had been investigated, and it was found that using hybrid techniques can improve the performance and efficiency of big data analytic while at the same time data is protected and privacy is preserved using ECC. |
| DOI | 10.1109/3ICT53449.2021.9582001 |
| Citation Key | salman_homomorphic_2021 |
A Homomorphic Cloud Framework for Big Data Analytics Based on Elliptic Curve Cryptography