Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud

  • Ang LiEmail author
  • Wei DuEmail author
  • Qinghua Li
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 254)


The increasing massive data generated by various sources has given birth to big data analytics. Solving large-scale nonlinear programming problems (NLPs) is one important big data analytics task that has applications in many domains such as transport and logistics. However, NLPs are usually too computationally expensive for resource-constrained users. Fortunately, cloud computing provides an alternative and economical service for resource-constrained users to outsource their computation tasks to the cloud. However, one major concern with outsourcing NLPs is the leakage of user’s private information contained in NLP formulations and results. Although much work has been done on privacy-preserving outsourcing of computation tasks, little attention has been paid to NLPs. In this paper, we for the first time investigate secure outsourcing of general large-scale NLPs with nonlinear constraints. A secure and efficient transformation scheme at the user side is proposed to protect user’s private information; at the cloud side, generalized reduced gradient method is applied to effectively solve the transformed large-scale NLPs. The proposed protocol is implemented on a cloud computing testbed. Experimental evaluations demonstrate that significant time can be saved for users and the proposed mechanism has the potential for practical use.


  1. 1.
    Barbosa, M., Farshim, P.: Delegatable homomorphic encryption with applications to secure outsourcing of computation. In: Dunkelman, O. (ed.) CT-RSA 2012. LNCS, vol. 7178, pp. 296–312. Springer, Heidelberg (2012). Scholar
  2. 2.
    Bazaraa, M.S., Sherali, H.D., Shetty, C.M.: Nonlinear Programming: Theory and Algorithms. Wiley, Hoboken (2013)zbMATHGoogle Scholar
  3. 3.
    Bertsekas, D.P.: Nonlinear Programming. Athena scientific, Belmont (1999)zbMATHGoogle Scholar
  4. 4.
    Chen, F., Xiang, T., Lei, X., Chen, J.: Highly efficient linear regression outsourcing to a cloud. IEEE Trans. Cloud Comput. 2(4), 499–508 (2014)CrossRefGoogle Scholar
  5. 5.
    Chen, F., Xiang, T., Yang, Y.: Privacy-preserving and verifiable protocols for scientific computation outsourcing to the cloud. J. Parallel Distrib. Comput. 74(3), 2141–2151 (2014)CrossRefGoogle Scholar
  6. 6.
    Chen, X., Huang, X., Li, J., Ma, J., Lou, W., Wong, D.S.: New algorithms for secure outsourcing of large-scale systems of linear equations. IEEE Trans. Inf. Forensics Secur. 10(1), 69–78 (2015)CrossRefGoogle Scholar
  7. 7.
    Chen, X., Li, J., Ma, J., Tang, Q., Lou, W.: New algorithms for secure outsourcing of modular exponentiations. IEEE Trans. Parallel Distrib. Syst. 25(9), 2386–2396 (2014)CrossRefGoogle Scholar
  8. 8.
    Chung, K.-M., Kalai, Y., Vadhan, S.: Improved Delegation of computation using fully homomorphic encryption. In: Rabin, T. (ed.) CRYPTO 2010. LNCS, vol. 6223, pp. 483–501. Springer, Heidelberg (2010). Scholar
  9. 9.
    Du, W., Li, Q.: Secure and efficient outsourcing of large-scale nonlinear programming. In: 2017 IEEE Conference on Communications and Network Security (CNS), IEEE (2017)Google Scholar
  10. 10.
    Gentry, C.: Fully homomorphic encryption using ideal latticesGoogle Scholar
  11. 11.
    Kalai, Y., Raz, R., Rothblum, R.: How to delegate computations: the power of no-signaling proofsGoogle Scholar
  12. 12.
    Katz, J., Lindell, Y.: Introduction to Modern Cryptography. CRC Press, Boca Raton (2014)zbMATHGoogle Scholar
  13. 13.
    Lei, X., Liao, X., Huang, T., Li, H.: Cloud computing service: the case of large matrix determinant computation. IEEE Trans. Serv. Comput. 8(5), 688–700 (2015)CrossRefGoogle Scholar
  14. 14.
    Lei, X., Liao, X., Huang, T., Li, H., Hu, C.: Outsourcing large matrix inversion computation to a public cloud. IEEE Trans. Cloud Comput. 1(1), 1–1 (2013)CrossRefGoogle Scholar
  15. 15.
    Murugesan, S., Bojanova, I.: Encyclopedia of Cloud Computing. Wiley, Hoboken (2016)CrossRefGoogle Scholar
  16. 16.
    Ren, K., Wang, C., Wang, Q.: Security challenges for the public cloud. IEEE Internet Comput. 16(1), 69–73 (2012)CrossRefGoogle Scholar
  17. 17.
    Shen, W., Yin, B., Cui, X., Cheng, Y.: A distributed secure outsourcing scheme for solving linear algebraic equations in ad hoc clouds. IEEE Trans. Cloud Comput. (2017)Google Scholar
  18. 18.
    Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning, pp. 1139–1147 (2013)Google Scholar
  19. 19.
    Wang, C., Ke, R., Wang, J.: Secure and practical outsourcing of linear programming in cloud computingGoogle Scholar
  20. 20.
    Wang, C., Ren, K., Wang, J., Wang, Q.: Harnessing the cloud for securely outsourcing large-scale systems of linear equations. IEEE Trans. Parallel Distrib. Syst. 24(6), 1172–1181 (2013)CrossRefGoogle Scholar
  21. 21.
    Zhou, L., Li, C.: Outsourcing large-scale quadratic programming to a public cloud. IEEE Access 3, 2581–2589 (2015)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Computer EngineeringUniversity of ArkansasFayettevilleUSA
  2. 2.Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingUSA

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