A Noise-Free Homomorphic Evaluation of the AES Circuits to Optimize Secure Big Data Storage in Cloud Computing

  • Ahmed EL-Yahyaoui
  • Mohamed Dafir Ech-Chrif El Kettani
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 37)


In this paper, we describe a homomorphic evaluation of the different AES circuits (AES-128, AES-192 and AES-256) using a noise-free fully homomorphic encryption scheme. This technique is supposed to be an efficient solution to optimize data storage in a context of outsourcing computations to a remote cloud computing as it is considered a powerful tool to minimize runtime in the client side. In this implementation, we will use a noise free quaternionique based fully homomorphic encryption scheme with different key sizes. Among the tools we are using in this work, a small laptop with characteristics: bi-cores Intel core i5 CPU running at 2.40 GHz, with 512 KB L2 cache and 4 GB of Random Access Memory. Our implementation takes about 18 min to evaluate an entire AES circuit using a key of 1024 bits for the fully homomorphic encryption scheme.


AES Evaluation Fully homomorphic encryption Optimization 


  1. 1.
    Rivest, R., Adleman, L., Dertouzos, M.: On data banks and privacy homorphisms. In: Foundataions of Secure Computataion, pp. 169–179. Academic Press (1978)Google Scholar
  2. 2.
    Gentry, C.: A fully homomorphic encryption scheme, September 2009.
  3. 3.
    Brakerski, Z., Gentry, C., Vaikantanathan, V.: Fully Homomorphique Encryption without Bootstrapping.
  4. 4.
    van Dijk, M., Gentry, C., Halevi, S., Vaikuntana, V.: Fully homomorphic encryption over the integers, Cryptology ePrint Archive, Report 2009/616 (2009)Google Scholar
  5. 5.
    Fan , J., Vercauteren, F.: Somewhat Practical Fully Homomorphic Encryption.
  6. 6.
    Gentry, C., Halevi, S., Smart, N.: Homomorphic Evaluation of the AES Circuit.
  7. 7.
    Brakerski, Z., Vaikantanathan, V.: Efficient Fully Homomorphic Encryption from (Standard) LWE.
  8. 8.
    Gentry, C., Sahai, A., Waters, B.: Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based.
  9. 9.
    Cheon, J.H., Coron, J.-S., Kim, J., Lee, M.S., Lepoint, T., Tibouchi, M., Yun, A.: Batch Fully Homomorphic Encryption over the Integers.
  10. 10.
    Nuida, K., Kurosawa, K.: (Batch) Fully Homomorphic Encryption over Integers for Non-Binary Message Spaces.
  11. 11.
    Gentry, C., Halevi, S., Smart, N.: Fully Homomorphic Encryption with Polylog Overhead.
  12. 12.
    Lauter, K., Naehrig, M., Vaikuntanathan, V.: Can Homomorphic Encryption be Practical?

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Information Security Research Team, CEDOC ST2I ENSIASMohammed V UniversityRabatMorocco

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