Enhanced MORE Algorithm for Fully Homomorphic Encryption Based on Secret Information Moduli Set Cloud Computing has offers incredible benefits in terms of data storage and data processing. However, data protection and privacy issues when it comes to data processing on the cloud has triggered several challenges for the existing cryptosystems. In this paper, we propose symmetric based Fully Homomorphic Encryption (FHE) scheme with Matrix Operation for Randomization and Encryption (MORE) that allows arbitrary computation on an encrypted data. The proposed scheme uses Secret Information Moduli Set (SIMS) to preserved data privacy and enhanced the existing MORE scheme. The result shows that the proposed scheme (MORESIMS) tradeoff 2% execution time compared to MORE scheme with high level of security and better storage overhead of n x n based on presented security analysis.

HEAX: An Architecture for Computing on Encrypted Data With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are susceptible to internal and external hacks, but also in some scenarios, data owners cannot outsource the computation due to privacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE) is a groundbreaking invention in cryptography that, unlike traditional cryptosystems, enables computation on encrypted data without ever decrypting it. However, the most critical obstacle in deploying FHE at largescale is the enormous computation overhead.  :89,this paper, we present HEAX, a novel hardware architecture for FHE that achieves unprecedented performance improvement. HEAX leverages multiple levels of parallelism, ranging from ciphertextlevel to finegrained modular arithmetic level. Our first contribution is a new highlyparallelizable architecture for numbertheoretic transform (NTT) which can be of independent interest as NTT is frequently used in many latticebased cryptography systems. Building on top of NTT engine, we design a novel architecture for computation on homomorphically encrypted data. We also introduce several techniques to enable an endtoend, fully pipelined design as well as reducing onchip memory consumption. Our implementation on reconfigurable hardware demonstrates 164268x performance improvement for a wide range of FHE parameters.