Abstract
With development of cloud computing, how to keep privacy and compute outsourcing data effectively at the same time is highly significant in practice. Homomorphic encryption is a common method to support ciphertext calculation, but most schemes do not provide fully homomorphic properties. Some fully homomorphic encryption schemes feature complicated design, high computational complexity and no practicability. Some cloud service providers are not trustable and return incorrect computational results due to resource saving or other malicious behaviors. Therefore, this paper proposes a verifiable fully homomorphic encryption scheme VFHES. VFHES implements fully homomorphic encryption based on the principle of the matrix computing principle and matrix blinding technology and supports to verify correctness of the computational results. Security analysis proves that VFHES is privacy-safe and verifiable. The performance analysis and experimental results show that VFHES is practicable and effective.
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References
Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of the Annual ACM Symposium on Theory of Computing, Bethesda, pp. 169–178. ACM (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
Brakerski, Z., Vaikuntanathan, V.: Efficient fully homomorphic encryption from (standard) LWE. In: Foundations of Computer Science, CA, pp. 97–106. IEEE (2011)
Brakerski, Z., Gentry, C., Vaikuntanathan, V.: (Leveled)fully homomorphic encryption without bootstrapping. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 309–325. ACM Press, New York (2012)
Gentry, C., Sahai, A., Waters, B.: Homomorphic encryption from learning with errors: conceptually-simpler, asymptotically-faster, attribute-based. In: Canetti, R., Garay, Juan A. (eds.) CRYPTO 2013, Part I. LNCS, vol. 8042, pp. 75–92. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40041-4_5
Gennaro, R., Gentry, C., Parno, B.: Non-interactive verifiable computing: outsourcing computation to untrusted workers. In: Rabin, T. (ed.) CRYPTO 2010. LNCS, vol. 6223, pp. 465–482. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14623-7_25
Jin, F., Zhu, Y., Luo, X.: Verifiable fully homomorphic encryption scheme. In: International Conference on Consumer Electronics, Communications and Networks, Yichang, pp. 743–746. IEEE (2012)
Zhou, J., Cao, Z., Dong, X., et al.: EVOC: more efficient verifiable outsourced computation from any one-way trapdoor function. In: IEEE International Conference on Communications, pp. 7444–7449. IEEE (2015)
Ahmed, E.Y., El Kettani, M.D.E.C.: A verifiable fully homomorphic encryption scheme to secure big data in cloud computing. In: International Conference on Wireless Networks and Mobile Communications, Rabat, pp. 1–5. IEEE (2017)
Zhao, Q., Zeng, Q., Liu, X., Xu, H.: Verifiable computation using re-randomizable garbled circuits. J. Softw. 30(2), 399–415 (2019)
Xu, J., Wei, L.W., Zhang, Y.: Dynamic fully homomorphic encryption-based Merkle tree for lightweight streaming authenticated data structures. J. Netw. Comput. Appl. 107, 113–124 (2018)
Acknowledgments
This work was supported in part by the Guangxi Natural Fund Project under Grant No. 2016GXNSFAA380115, Guangxi Innovation-Driven Development Project under Grant No. AA17204058-17.
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Huang, R., Li, Z., Zhao, J. (2019). A Verifiable Fully Homomorphic Encryption Scheme. In: Wang, G., Feng, J., Bhuiyan, M., Lu, R. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2019. Lecture Notes in Computer Science(), vol 11611. Springer, Cham. https://doi.org/10.1007/978-3-030-24907-6_31
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DOI: https://doi.org/10.1007/978-3-030-24907-6_31
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