DFP: A Data Fragment Protection Scheme for mHealth in Wireless Network

  • Lin Zhang
  • Zhigang Chen
  • Deyu Zhang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 768)


The mHealth system gradually become widely promoted, the user data privacy issues by the community a strong concern in the complex wireless network environment. In this paper, we propose a data fragment protection scheme, named DFP. The proposed DFP scheme according to the characteristics of the medical environment to system preprocessing, let the wearable equipment or implantation equipment to collect the patient information classified as patient personal privacy data and general medical data, the two types of data on the degree of privacy of different treatment. And according to the data connectivity design reliable transmission scheme. Our framework can not only more reasonable protection of medical data privacy and security, but also to reduce communication consumption and reduce the average time delay. Extensive performance analysis and experimental results proves its effectiveness and reliability.


Data fragment Mobile health Privacy protection Security 



This work was supported in part by Major Program of National Natural Science Foundation of China (71633006); The National Natural Science Foundation of China (61672540, 61379057).


  1. 1.
    Huang, C.F., Lin, W.C.: Data collection for multiple mobile users in wireless sensor networks. J. Supercomput. 72(7), 2651–2669 (2016)CrossRefGoogle Scholar
  2. 2.
    Kotz, D., Gunter, C.A., Kumar, S., et al.: Privacy and security in mobile health: a research agenda. Computer 49(6), 22 (2016)CrossRefGoogle Scholar
  3. 3.
    Reuben, D.B., Hackbarth, A.S., Wenger, N.S., et al.: An automated approach to identifying patients with dementia using electronic medical records. J. Am. Geriatr. Soc. 65(3), 658–659 (2017)CrossRefGoogle Scholar
  4. 4.
    Zhang, D., Chen, Z., Zhou, H., et al.: Energy-balanced cooperative transmission based on relay selection and power control in energy harvesting wireless sensor network. Comput. Netw. 104, 189–197 (2016)CrossRefGoogle Scholar
  5. 5.
    Zhou, J., Cao, Z., Dong, X., et al.: PPDM: a privacy-preserving protocol for cloud-assisted e-healthcare systems. IEEE J. Sel. Top. Sig. Process. 9(7), 1332–1344 (2015)CrossRefGoogle Scholar
  6. 6.
    Ng, J.W.P., Lo, B.P.L., Wells, O., et al.: Ubiquitous monitoring environment for wearable and implantable sensors (UbiMon). In: International Workshop on Ubiquitous Computing (2004)Google Scholar
  7. 7.
    Schlamp, J., Holz, R., Jacquemart, Q., et al.: HEAP: reliable assessment of BGP hijacking attacks. IEEE J. Sel. Areas Commun. 34(6), 1849–1861 (2016)CrossRefGoogle Scholar
  8. 8.
    Wu, J., Chen, Z.: Data decision and transmission based on mobile data health records on sensor devices in wireless networks. Wirel. Pers. Commun. 90(4), 2073–2087 (2016)CrossRefGoogle Scholar
  9. 9.
    Konstantas, D., Van, H.A., Bults, R., et al.: Mobile patient monitoring: the MobiHealth system. Conf. Proc. IEEE Eng. Med. Biol. Soc. 103(5), 1238–1241 (2009)Google Scholar
  10. 10.
    Guo, L., Zhang, C., Sun, J., et al.: A privacy-preserving attribute-based authentication system for mobile health networks. IEEE Trans. Mob. Comput. 13(9), 1927–1941 (2014)CrossRefGoogle Scholar
  11. 11.
    Poulymenopoulou, M., Malamateniou, F., Vassilacopoulos, G.: A virtual PHR authorization system. In: IEEE-EMBS International Conference on Biomedical and Health Informatics, pp. 73–76. IEEE (2014)Google Scholar
  12. 12.
    Harvey, M.J., Harvey, M.G.: Privacy, security issues for mobile health platforms. J. Assoc. Inf. Sci. Technol. 65, 1305–1318 (2014)CrossRefGoogle Scholar
  13. 13.
    Ankarali, Z.E., Abbasi, Q.H., Demir, A.F., et al.: A comparative review on the wireless implantable medical devices privacy and security. In: EAI, International Conference on Wireless Mobile Communication and Healthcare, pp. 246–249. IEEE (2015)Google Scholar
  14. 14.
    Spachos, P., Toumpakaris, D., Hatzinakos, D.: Angle-based dynamic routing scheme for source location privacy in wireless sensor networks. In: Vehicular Technology Conference, pp. 1–5. IEEE (2014)Google Scholar
  15. 15.
    Lu, R., Liang, X., Li, X., et al.: EPPA: an efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Trans. Parallel Distrib. Syst. 23(9), 1621–1631 (2012)CrossRefGoogle Scholar
  16. 16.
    Sharma, A., Baweja, P.: Medical identity theft: a case report. Ann. Internal Med. 166(5), 380 (2017)CrossRefGoogle Scholar
  17. 17.
    Hambidge, S.J., Ross, C., Shoup, J.A., et al.: Integration of data from a safety net health care system into the vaccine safety datalink. Vaccine 35(9), 1329–1334 (2017)CrossRefGoogle Scholar
  18. 18.
    Mare, S., Sorber, J., Shin, M., et al.: Hide-n-Sense: preserving privacy efficiently in wireless mHealth. Mob. Netw. Appl. 19(3), 331–344 (2014)CrossRefGoogle Scholar
  19. 19.
    Lin, X., Lu, R., Shen, X., et al.: Sage: a strong privacy-preserving scheme against global eavesdropping for ehealth systems. IEEE J. Sel. Areas Commun. 27(4), 365–378 (2009)CrossRefGoogle Scholar
  20. 20.
    Zhang, Y., Chen, Q., Zhong, S.: Privacy-preserving data aggregation in mobile phone sensing. IEEE Trans. Inf. Forensics Secur. 11(5), 980–992 (2016)CrossRefGoogle Scholar
  21. 21.
    Zhang, Z., Wang, H., Lin, X., et al.: Effective epidemic control and source tracing through mobile social sensing over WBANs. In: 2013 Proceedings of IEEE INFOCOM, pp. 300–304. IEEE (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.School of SoftwareCentral South UniversityChangshaChina
  2. 2.Mobile Health Ministry of Education China Mobile Joint LaboratoryChangshaChina

Personalised recommendations