Abstract
Security is an ever-present consideration for applications and data in the cloud computing environment. As an important method of performing computations directly on encrypted data without any need of decryption and compromising privacy, homomorphic encryption is an increasingly popular topic of protecting the privacy of data in cloud security research. However, as high computational complexity of the homomorphic encryption, it will be a heavy workload for computing resources in the cloud computing paradigm. Motivated by this observation, this paper proposes a fast parallel scheme with DGHV algorithm based on CPU-4GPUs hybrid system. Our main contribution of this paper is to present a parallel processing stream scheme for large-scale data encryption based on CPU-4GPUs hybrid system as fast as possible. Particularly, the proposed method applies CPU-4GPUs parallel implementation to accelerating encryption operation with DGHV algorithm to reduce the time duration and provide a comparative performance study. We also make further efforts to design a pipeline architecture of processing stream in CPU-4GPUs hybrid system to accelerate encryption for DGHV algorithm. The experiment results show that our method gains more than 91% improvement (run time) and 70% improvement compared to the serial addition and multiplication operation with DGHV algorithm respectively.
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References
Rivest, R.L., Adleman, L., Dertouzos, M.L.: On data banks and privacy homeomorphisms. In: Foundations of Secure Computation, pp. 169–177 (1978)
Gentry, C.: A fully homomorphic encryption scheme. Ph.D. thesis, Stanford University (2009)
Gentry, C.: Fully homomrphic encryption using ideal lattices. In: ACM STOC 2009, pp. 169–178 (2009)
van Dijk, M., Gentry, C., Halevi, S., Vaikuntanathan, V.: Fully homomorphic encryption over the integers. In: Gilbert, H. (ed.) EUROCRYPT 2010. LNCS, vol. 6110, pp. 24–43. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13190-5_2
Fugkeaw, S., Sato, H.: Privacy-preserving access control model for big data cloud. In: Computer Science and Engineering Conference, pp. 1–6. IEEE Press, Changchun (2016)
Swathi, R., Subha, T.: Enhancing data storage security in cloud using certificateless public auditing. In: 2017 2nd International Conference on Computing and Communications Technologies (ICCCT), Bangkok, Thailand, pp. 348–352 (2017)
Jiang, W., Zhao, Z., Laat, C.D.: An autonomous security storage solution for data-intensive cooperative cloud computing. In: 2013 IEEE 9th International Conference on e-Science, pp. 369–372. IEEE Press, Beijing (2013)
Shu, J., Jia, X., Yang, K., et al.: Privacy-preserving task recommendation services for crowdsourcing. In: IEEE Transactions on Services Computing (2018)
Zhang, J., Li, H., Liu, X., et al.: On efficient and robust anonymization for privacy protection on massive streaming categorical information. In: IEEE Transactions on Dependable and Secure Computing, vol. 14, pp. 507–520. IEEE (2017)
Jayapandian, N., Rahman, A.M.J.M.Z.: Secure and efficient online data storage and sharing over cloud environment using probabilistic with homomorphic encryption. Clust. Comput. 20, 1561–1573 (2017)
Sethi, K., Majumdar, A., Bera, P.: A novel implementation of parallel homomorphic encryption for secure data storage in cloud. In: 2017 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), pp. 1–7. IEEE Press, San Francisco (2017)
Min, Z., Yang, G., Shi, J.: A privacy-preserving parallel and homomorphic encryption scheme. Open Phys. 15, 135–142 (2017). De Gruyter Open
Khedr, A., Gulak, G.: SecureMed: secure medical computation using GPU-accelerated homomorphic encryption scheme. IEEE J. Biomed. Health Inform. 22, 597–606 (2017)
Tian, Y., Al-Rodhaan, M., Song, B., et al.: Somewhat homomorphic cryptography for matrix multiplication using GPU acceleration. In: 2014 International Symposium on Biometrics and Security Technologies (ISBAST), pp. 166–170. IEEE Press, Kuala Lumpur (2015)
Moayedfard, M., Molahosseini, A.S.: Parallel implementations of somewhat homomorphic encryption based on open-MP and CUDA. In: 2015 International Congress on Technology, Communication and Knowledge (ICTCK), pp. 186–190. IEEE Press, Mashhad (2015)
Wang, W., Hu, Y., Chen, L., et al.: Accelerating fully homomorphic encryption using GPU. In: 2012 IEEE Conference on High Performance Extreme Computing, pp. 1–5. IEEE Press, Waltham (2012)
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Xia, J., Ma, Z., Dai, X., Xu, J. (2018). Fast Homomorphic Encryption Based on CPU-4GPUs Hybrid System in Cloud. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_8
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DOI: https://doi.org/10.1007/978-3-030-02934-0_8
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