International Conference on Collaborative Computing: Networking, Applications and Worksharing

Collaborative Computing: Networking, Applications, and Worksharing pp 163-172 | Cite as

Protecting Privacy for Big Data in Body Sensor Networks: A Differential Privacy Approach

  • Chi Lin
  • Zihao Song
  • Qing Liu
  • Weifeng Sun
  • Guowei Wu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 163)


As a special kind of application of wireless sensor networks, Body Sensor Networks (BSNs) have broad perspectives especially in clinical caring and medical monitoring. Big data acquired from BSNs usually contain sensitive information, which are compulsory to be appropriately protected. However, previous methods overlooked the privacy protection issue, leading to privacy violation. In this paper, a differential privacy protection scheme for big data in body sensor network is proposed. We introduce the concept of dynamic noise thresholds which makes our scheme more suitable for processing big data. It can ensure privacy during the whole life cycle of the data, which makes privacy protection for big data in BSNs promising. Extensive experiments are conducted to outline the merits of our scheme. Experimental results reveal that our scheme has higher level of privacy protection. Even in the case where the attacker has full background knowledge, it still provides sufficient ambiguity, which ensures being unable to match people based on the ECG data characteristic so as to preserve the privacy.


Body sensor networks Big data Differential privacy 


  1. 1.
    Zheng, Z., Zhu, J., Lyu, M.R.: Service-generated big data and big data-as-a-service: an overview. In: IEEE International Congress on Big Data, pp. 403–410 (2013)Google Scholar
  2. 2.
    Huang, Z., Cao, F., Li, J., Chen, X.: Developing sea cloud data system key technologies for large data analysis and mining. J. Netw. New Media 1(6), 20–26 (2012)Google Scholar
  3. 3.
    Bressan, N., Andrew, J.: Integration of drug dosing data with physiological data streams using a cloud computing paradigm. In: 35th Annual International Conference on Engineering in Medicine and Biology Society (EMBC), pp. 4175–4178. IEEE (2013)Google Scholar
  4. 4.
    Kai, E., Ashir, A.: Technical challenges in providing remote health consultancy services for the unreached community. In: 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 1016–1020. IEEE (2013)Google Scholar
  5. 5.
    Dilsizian, S.E., Siegel, E.L.: Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr. Cardiol. Rep. 16, 441 (2013)CrossRefGoogle Scholar
  6. 6.
    Kafali, O., Bromuri, S., Sindlar, M.: Commodity 12: a smart e-health environment for diabetes management. J. Ambient Intell. Smart Environ. 5(1), 479–502 (2013)Google Scholar
  7. 7.
    Wu, J., Roy, J., Stewart, W.F.: Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches. Med. Care 48(6), S106–S113 (2010)CrossRefGoogle Scholar
  8. 8.
    Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13(6), 395–405 (2012)CrossRefGoogle Scholar
  9. 9.
    Huang, Q.R., Qin, Z., Zhang, S., Chow, C.M.: Clinical patterns of obstructive sleep apnea and its co morbid conditions: a data mining approach. J. Clin. Sleep Med. 4(6), 543 (2008)Google Scholar
  10. 10.
    Zrimec, T., Wong, J.: Improving computer aided disease detection using knowledge of disease appearance. In: Med Info 2007: Proceedings of the 12th World Congressing Health (Medical) Informatics; Building Sustainable Health Systems, p. 1324. IOS Press, Amsterdam (2007)Google Scholar
  11. 11.
    Melzer, T.R., Richard, W.: Arterial spinlabelling reveals an abnormal cerebral perfusion pattern in Parkinson’s disease. Brain, awq377 (2011)Google Scholar
  12. 12.
    Xue, Y., Li, Q., Jin, L., Feng, L., Clifton, D.A., Clifford, G.D.: Detecting adolescent psychological pressures from micro-blog. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds.) HIS 2014. LNCS, vol. 8423, pp. 83–94. Springer, Heidelberg (2014)Google Scholar
  13. 13.
    Yoo, J., Yan, L., Lee, S.: A wearable ECG acquisition system with compact planar-fashionable circuit board based shirt. IEEE Trans. Inf. Technol. Biomed. 13(6), 897–902 (2009)CrossRefGoogle Scholar
  14. 14.
    Gargiulo, G., Bifulco, P., Cesarelli, M.: An ultra-high input impedance ECG amplifier for long-term monitoring of athletes. Med. Devices (Auckl) 3, 1–9 (2010)CrossRefGoogle Scholar
  15. 15.
    Yan, Y., Qin, X., Fan, J., Wang, L.: A review of big data research in medicine & healthcare. E-Sci. Technol. Appl. 5(6), 3–16 (2014)Google Scholar
  16. 16.

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Chi Lin
    • 1
    • 2
  • Zihao Song
    • 1
    • 2
  • Qing Liu
    • 1
    • 2
  • Weifeng Sun
    • 1
    • 2
  • Guowei Wu
    • 1
    • 2
  1. 1.School of SoftwareDalian University of TechnologyDalianChina
  2. 2.Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalianChina

Personalised recommendations