MedCop: Verifiable Computation for Mobile Healthcare System

  • Hardik Gajera
  • Shruti Naik
  • Manik Lal DasEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 969)


Cloud-assisted mobile healthcare system collects and processes patients data and then stores them as personal health record (PHR). Verifiable monitoring program finds useful results by analysing PHR in cloud-assisted healthcare system. Service provider can delegate a monitoring program to the cloud storage server for providing cost effective and faster service. The cloud performs computation over PHR and sends result back to user. The correctness of the computation of the result must be accurate for critical diseases; otherwise, patient’s treatment can go with wrong diagnosis. At the same time, the monitoring program should be hidden from all entities involved in the computation except the service provider. This is a challenging research problem to provide efficient and secure verification of computation of result while keeping the monitoring program hidden from the cloud as well as users. In this paper, we present a secure and efficient scheme for verification of computation of result while keeping monitoring program hidden from the cloud and users. The proposed scheme, named as MedCop, uses somewhat homomorphic encryption for PHR encryption and a private polynomial function is used for computation on encrypted data. We show that the MedCop scheme is secure under discrete logarithm assumption and the proof of computation is unforgeable. The implementation result of the MedCop scheme shows that the proposed scheme is efficient in comparison to related schemes.


Verifiable computation Cloud security Data encryption 



This research was supported in part by the Indo-French Centre for the Promotion of Advanced Research (IFCPAR) and the Center Franco-Indien Pour La Promotion De La Recherche Advancée (CEFIPRA) through the project DST/CNRS 2015-03 under DST-INRIA-CNRS Targeted Programme.


  1. 1.
    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). Scholar
  2. 2.
    Nia, A.M., Mozaffari-Kermani, M., Sur-Kolay, S., Raghunathan, A., Jha, N.K.: Energy-efficient long-term continuous personal health monitoring. IEEE Trans. Multi-Scale Comput. Syst. 1(2), 85–98 (2015)CrossRefGoogle Scholar
  3. 3.
    Mohan, P., Marin, D., Sultan, S., Deen, A.: MediNet: personalizing the self-care process for patients with diabetes and cardiovascular disease using mobile telephony. In: Proceedings of IEEE Conference on Engineering in Medicine and Biology Society (EMBS 2008), pp. 755–758 (2008)Google Scholar
  4. 4.
    Chiarini, G., Ray, P., Akter, S., Masella, C., Ganz, A.: mHealth technologies for chronic diseases and elders: a systematic review. IEEE J. Sel. Areas Commun. 31(9), 6–18 (2013)CrossRefGoogle Scholar
  5. 5.
    Klasnja, P., Pratt, W.: Healthcare in the pocket: mapping the space of mobile-phone health interventions. J. Biomed. Inf. 45(1), 184–198 (2012)CrossRefGoogle Scholar
  6. 6.
    Lin, H., Shao, J., Zhang, C., Fang, Y.: CAM: cloud-assisted privacy preserving mobile health monitoring. IEEE Trans. Inf. Forensics Secur. 8(6), 985–997 (2013)CrossRefGoogle Scholar
  7. 7.
    Liu, C.H., Wen, J., Yu, Q., Yang, B., Wang, W.: HealthKiosk: a family-based connected healthcare system for long-term monitoring. In: Proceedings of IEEE Conference on Computer Communications Workshops (INFOCOM 2011), pp. 241–246 (2011)Google Scholar
  8. 8.
  9. 9.
    Google, Inc., Google Fit - Fitness Tracking.
  10. 10.
    Guo, L., Fang, Y., Li, M., Li, P.: Verifiable privacy-preserving monitoring for cloud-assisted mHealth systems. In: Proceedings of the IEEE Conference on Computer Communications (INFOCOM 2015), pp. 1026–1034 (2015)Google Scholar
  11. 11.
    Gajera, H., Naik, S., Das, M.L.: On the security of “verifiable privacy-preserving monitoring for cloud-assisted mhealth systems”. In: Ray, I., Gaur, M.S., Conti, M., Sanghi, D., Kamakoti, V. (eds.) ICISS 2016. LNCS, vol. 10063, pp. 324–335. Springer, Cham (2016). Scholar
  12. 12.
    Micciancio, D.: A first glimpse of cryptography’s holy grail. Commun. ACM 53(3), 96 (2010)CrossRefGoogle Scholar
  13. 13.
    Pisa, P.S., Abdalla, M., Duarte, O.: Somewhat homomorphic encryption scheme for arithmetic operations on large integers. In: Proceedings of Global Information Infrastructure and Networking Symposium, pp. 1–8 (2012)Google Scholar
  14. 14.
    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). Scholar
  15. 15.
    Diffie, W., Hellman, M.E.: New directions in cryptography. IEEE Trans. Inf. Theor. 22(6), 644–654 (1976)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Sage, Sagemath, the Sage Mathematics Software System (Ver 7.6).

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.DA-IICTGandhinagarIndia

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