Abstract
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.
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References
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Melzer, T.R., Richard, W.: Arterial spinlabelling reveals an abnormal cerebral perfusion pattern in Parkinson’s disease. Brain, awq377 (2011)
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)
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)
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)
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)
Shimmer. http://www.shimmersensing.com/
Acknowledgements
This research is sponsored in part by the National Natural Science Foundation of China (No. 61173179, No. 61402078) and Program for New Century Excellent Talents in University (NCET-13-0083). This research is also sponsored in part supported by the Fundamental Research Funds for the Central Universities (No. DUT14RC(3)090).
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Lin, C., Song, Z., Liu, Q., Sun, W., Wu, G. (2016). Protecting Privacy for Big Data in Body Sensor Networks: A Differential Privacy Approach. In: Guo, S., Liao, X., Liu, F., Zhu, Y. (eds) Collaborative Computing: Networking, Applications, and Worksharing. CollaborateCom 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 163. Springer, Cham. https://doi.org/10.1007/978-3-319-28910-6_15
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DOI: https://doi.org/10.1007/978-3-319-28910-6_15
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