Biblio

Data security has become the most important issue in every institution or company. With the existence of hackers, intruders, and third parties on the cloud, securing data has become more challenging. This paper uses a hybrid encryption method that is based on the Elliptic Curve Cryptography (ECC) and Fully Homomorphic Encryption (FHE). ECC is used as a lightweight encryption algorithm that can provide a good level of security. Besides, FHE is used to enable data computation on the encrypted data in the cloud. In this paper, the concept of unique values is combined with the hybrid encryption method. Using the concept of unique values contributes to decreasing the encryption and decryption time obviously. To evaluate the performance of the combined encryption method, the provided results are compared with the ones in the encryption method without using the concept of unique values. Experiments show that the combined encryption method can reduce the encryption time up to 43% and the decryption time up to 56%.

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.