Advertisement

Privacy Preserving Data Offloading Based on Transformation

  • Shweta SaharanEmail author
  • Vijay Laxmi
  • Manoj Singh Gaur
  • Akka Zemmari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11391)

Abstract

Mobile Cloud Computing (MCC) provides a scalable solution for both storage and computation of data over the Cloud. Though offloading benefits the execution performance, it raises new challenges regarding security. Privacy leakage risks prevent users from sharing their private data with third-party services. State-of-the-art approaches used for secure data storage are cryptography based, having an overhead of key management as well as do not support computation on encrypted data on the cloud server. However, homomorphic techniques support computation on encrypted data and generate an encrypted result, are compute intensive and not advisable due to resource constraint nature of mobile devices. This paper proposes a light-weight technique for privacy-preserving data offloading to the mobile cloud servers supporting computation. Our technique offloads the data to multiple servers instead of a single server. We have performed the security analysis for correctness, secrecy and unknown shares using various similarity measures.

Keywords

Mobile cloud Privacy Data Offloading Computation 

References

  1. 1.
    CVG-UGR image database. http://decsai.ugr.es/cvg/dbimagenes/. Accessed 17 April 2018
  2. 2.
    Bahrami, M., Li, D., Singhal, M., Kundu, A.: An efficient parallel implementation of a light-weight data privacy method for mobile cloud users. In: Proceedings of the 7th International Workshop on Data-Intensive Computing in the Cloud, pp. 51–58 (2016)Google Scholar
  3. 3.
    Qin, Z., Yan, J., Ren, K., Chen, C.W., Wang, C.: Towards efficient privacy-preserving image feature extraction in cloud computing. In: Proceedings of the ACM International Conference on Multimedia - MM 2014, pp. 497–506 (2014)Google Scholar
  4. 4.
    Sánchez, D., Batet, M.: Privacy-preserving data outsourcing in the cloud via semantic data splitting. Comput. Commun. 110, 187–201 (2017)CrossRefGoogle Scholar
  5. 5.
    Tong, Y., Sun, J., Chow, S.S.M., Li, P.: Cloud-assisted mobile-access of health data with privacy and auditability. IEEE J. Biomed. Health Inf. 18(2), 419–429 (2014)CrossRefGoogle Scholar
  6. 6.
    Xia, Z., Xiong, N.N., Vasilakos, A.V., Sun, X.: EPCBIR: an efficient and privacy-preserving content-based image retrieval scheme in cloud computing. Inf. Sci. 387, 195–204 (2017)CrossRefGoogle Scholar
  7. 7.
    Yan, Y., Han, D., Shu, T.: Privacy preserving optimization of participatory sensing in mobile cloud computing. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 1084–1093 (2017)Google Scholar
  8. 8.
    Zhang, K., Zhou, X., Chen, Y., Wang, X., Ruan, Y.: Sedic: privacy-aware data intensive computing on hybrid clouds. In: 18th ACM Conference on Computer and Communications security - CCS 2011, pp. 515–526 (2011)Google Scholar
  9. 9.
    Zhang, L., Jung, T., Liu, C., Ding, X., Li, X.Y., Liu, Y.: POP: privacy-preserving outsourced photo sharing and searching for mobile devices. In: Proceedings - International Conference on Distributed Computing Systems, July 2015, pp. 308–317 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shweta Saharan
    • 1
    Email author
  • Vijay Laxmi
    • 1
  • Manoj Singh Gaur
    • 2
  • Akka Zemmari
    • 3
  1. 1.Malaviya National Institute of Technology JaipurJaipurIndia
  2. 2.Indian Institute of Technology JammuJammuIndia
  3. 3.University of BordeauxBordeauxFrance

Personalised recommendations