Cloud computing is a boon for both business and private use, but data security concerns slow its adoption. Fully homomorphic encryption (FHE) offers the means by which the cloud computing can be performed on encrypted data, obviating the data security concerns. FHE is not without its cost, as FHE operations take orders of magnitude more processing time and memory than the same operations on unencrypted data. Cloud computing can be leveraged to reduce the time taken by bringing to bear parallel processing. This paper presents an implementation of a processing dispatcher which takes an iterative set of operations on FHE encrypted data and splits them between a number of processing engines. A private cloud was implemented to support the processing engines. The processing time was measured with 1, 2, 4, and 8 processing engines. The time taken to perform the calculations with the four levels of parallelization, as well as the amount of time used in data transfers are presented. In addition, the time the computation servers spent in each of addition, subtraction, multiplication, and division are laid out. An analysis of the time gained by parallel processing is presented. The experimental results shows that the proposed parallel processing of Gentry’s encryption improves the performance better than the computations on a single node. This research provides the following contributions. A private cloud was built to support parallel processing of homomorphic encryption in the cloud. A client-server model was created to evaluate cloud computing of the Gentry’s encryption algorithm. A distributed algorithm was developed to support parallel processing of the Gentry’s algorithm for evaluation on the cloud. An experiment was setup for the evaluation of the Gentry’s algorithm, and the results of the evaluation show that the distributed algorithm can be used to speed up the processing of the Gentry’s algorithm with cloud computing. All Rights Reserved © 2015 Universidad Nacional Autonoma de Mexico, Centro de Ciencias Aplicadas y Desarrollo Tecnologico. This is an open access item distributed under the Creative Commons CC License BY-NC-ND 4.0.
[1]
Craig Gentry,et al.
Implementing Gentry's Fully-Homomorphic Encryption Scheme
,
2011,
EUROCRYPT.
[2]
Vinod Vaikuntanathan,et al.
Can homomorphic encryption be practical?
,
2011,
CCSW '11.
[3]
Aleksandar Erdeljan,et al.
Optimal Workflow Scheduling in Critical Infrastructure Systems with Neural Networks
,
2012
.
[4]
Adi Shamir,et al.
A method for obtaining digital signatures and public-key cryptosystems
,
1978,
CACM.
[5]
S. Ortega-Cisneros,et al.
Hardware and Software Co-design: An Architecture Proposal for a Network-on-Chip Switch based on Bufferless Data Flow
,
2014
.
[6]
Whitfield Diffie,et al.
New Directions in Cryptography
,
1976,
IEEE Trans. Inf. Theory.
[7]
Sanjay Ghemawat,et al.
MapReduce: Simplified Data Processing on Large Clusters
,
2004,
OSDI.
[8]
Mariana Raykova,et al.
Parallel Homomorphic Encryption
,
2013,
Financial Cryptography Workshops.
[9]
T. Elgamal.
A public key cryptosystem and a signature scheme based on discrete logarithms
,
1984,
CRYPTO 1984.
[10]
Craig Gentry,et al.
A fully homomorphic encryption scheme
,
2009
.
[11]
Chia-Chu Chiang,et al.
An Architecture for Parallelizing Fully Homomorphic Cryptography on Cloud
,
2013,
2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems.
[12]
Taher ElGamal,et al.
A public key cyryptosystem and signature scheme based on discrete logarithms
,
1985
.
[13]
Pascal Paillier,et al.
Public-Key Cryptosystems Based on Composite Degree Residuosity Classes
,
1999,
EUROCRYPT.
[14]
Urmila Meshram,et al.
Hardware and Software Co-Design for Robot Arm
,
2010,
IC3.
[15]
Ronald L. Rivest,et al.
ON DATA BANKS AND PRIVACY HOMOMORPHISMS
,
1978
.