(a,k)-Anonymous Scheme for Privacy-Preserving Data Collection in IoT-based Healthcare Services Systems
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The widely use of IoT technologies in healthcare services has pushed forward medical intelligence level of services. However, it also brings potential privacy threat to the data collection. In healthcare services system, health and medical data that contains privacy information are often transmitted among networks, and such privacy information should be protected. Therefore, there is a need for privacy-preserving data collection (PPDC) scheme to protect clients (patients) data. We adopt (a,k)-anonymity model as privacy pretection scheme for data collection, and propose a novel anonymity-based PPDC method for healthcare services in this paper. The threat model is analyzed in the client-server-to-user (CS2U) model. On client-side, we utilize (a,k)-anonymity notion to generate anonymous tuples which can resist possible attack, and adopt a bottom-up clustering method to create clusters that satisfy a base privacy level of (a1,k1)-anonymity. On server-side, we reduce the communication cost through generalization technology, and compress (a1,k1)-anonymous data through an UPGMA-based cluster combination method to make the data meet the deeper level of privacy (a2,k2)-anonymity (a1 ≥ a2, k2 ≥ k1). Theoretical analysis and experimental results prove that our scheme is effective in privacy-preserving and data quality.
KeywordsHealthcare services Internet of things Anonymization Privacy-preserving Data collection
- 1.Agrawal, R., and Srikant, R., Privacy-preserving data mining. SIGMOD Record (ACM Special Interest Group on Management of Data). 29(2):439–450, 2000.Google Scholar
- 3.Guan, S.P, Zhang, Y, Ji, Y., Preserving-Privacy Health Data Collection for Preschool Children. Computational and Mathematical Methods in Medical, Article ID 501607, 5 pages, 2013.Google Scholar
- 4.Rahman, F, Williams, D, Wang, Q, et al. PriDac: Privacy Preserving Data Collection in Sensor enabled REID based Healthcare Services. 2014 I.E. 15th International Symposium on High-Assurance Systems Engineering, Washington: HASE: 236–242, 2014.Google Scholar
- 5.Ni, J.B., Zhang, K., Lin, X.D., and Shen, X.M., Securing fog computing for internet of things applications: Challenges and solutions. IEEE Communications Surveys and Tutorials. https://doi.org/10.1109/COMST.2017.2762345,2017.
- 6.Kumari, S., Karuppiah, M., Das, A.K., et al., A secure authentication scheme based on elliptic curve cryptography for IoT and cloud servers[J]. J. Supercomput. 4:1–26, 2017.Google Scholar
- 7.Lakshmi, S., and Ramesh, S.P., Secure encrypted-data routing protocol for wireless sensor networks. Journal of Computer Applications. 5:167–173, 2012.Google Scholar
- 9.Kumari, S., Design flaws of “an anonymous two-factor authenticated key agreement scheme for session initiation protocol using elliptic curve cryptography”[J]. Multimedia Tools & Applications:1–3, 2016.Google Scholar
- 10.Xiong, J.B., Zhang, Y.Y., Li, X., et al., RSE-PoW: A role symmetric encryption PoW scheme with authorized deduplication for multimedia data. Mobile Networks and Applications, 2017. https://doi.org/10.1007/s11036-017-0975-x.
- 11.Kumar, S, Dohare, D, Kumar, M. An efficient key distribution scheme for wireless sensor networks using polynomial based schemes. 2012 International Conference on Information and Network Technology, Singapore: IACSIT, 21–27, 2012.Google Scholar
- 13.Zhang, N, Wang, S, Zhao, W., A new scheme on privacy-preserving data classification. International Conference on Knowledge Discovery and Data Mining, pp. 374–382, 2005.Google Scholar
- 14.Sivaraman, V., Swaminathan, N., and Vijayaraghavan, P., Privacy preserving web search by client side generalization of user profile. Asian Journal of Computer Science and Technology. 4(1):14–17, 2015.Google Scholar
- 15.Vishwakarma, B, Gupta, H, Manoria, M., A survey on privacy preserving mining implementing techniques[C]//Colossal Data Analysis and Networking (CDAN), Symposium on. IEEE: 1–5, 2016.Google Scholar
- 16.Iyengar V.S., Transforming data to satisfy privacy constraints. In: Proceeding of the 8th ACM international conference on knowledge discovery and data mining (SIGKDD), Edmonton: ACM, 279–288, 2002.Google Scholar
- 17.Samarati, P, Sweeney, L., Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. In: Proc. of the IEEE Symposium on Research in Security and Privacy, 1998.Google Scholar
- 19.Meyerson, A, Williams, R., On the complexity of optimal k-anonymity. In: Proceeding of POD’04 the 23rd ACM SIGMOD-SIGACTSIGART Symposium on the Principles of Database Systems, New York: ACM. 223–228, 2004.Google Scholar
- 20.Begum, R.S, Sugumar, R., Conditional entropy with swarm optimization approach for privacy preservation of datasets in cloud [J]. Indian Journal of Science and Technology 9(28), 2016. https://doi.org/10.17485/ijst/2016/v9i28/93817
- 21.Blake, C. L., and Merz, C. J., UCI repository of machine learning databases[OL]. http://archive.ics.uci.edu/ml/datasets.html, 1998.
- 22.Jin, X, Zhang, N, Das, G., Algorithm-safe privacy preserving data publishing. In: Proceeding of EDBT’10 the 13 International Conference on Extending Database Technology, New York: ACM. 633–644, 2010.Google Scholar
- 24.Jiang, Q., Chen, Z.R., Li, B.Y., and Ma, J.F., Security analysis and improvement of biohashing based three-factor authentication scheme for telecare medical information systems. Journal of Ambient Intelligence and Humanized Compiting, 2017. https://doi.org/10.1007/s12652-017-0516-2.
- 28.Jiang, Q., Ma, J.F., Yang, C., Ma, X., Ma, X.D., Shen, J., and Chaudhry, S.A., Efficient end-to-end authentication protocol for wearable health monitoring systems. Comput. Electr. Eng., 2017. https://doi.org/10.1016/j.compeleceng.2017.
- 29.Hung, T.H, Hsieh, S.H, Lu, C.S., Privacy-preserving data collection and recovery of compressive sensing[C]//Signal and Information Processing (ChinaSIP), 2015 I.E. China Summit and International Conference on. IEEE: 473–477, 2015.Google Scholar
- 30.Wu, D., Si, S., Wu, S., et al., Dynamic Trust Relationships Aware Data Privacy Protection in Mobile Crowd-Sensing[J]. IEEE Internet of Things Journal. PP(99):1–1, 2017.Google Scholar
- 31.Mohammed, H, Tonyali, S, Rabieh, K, et al., Efficient privacy-preserving data collection scheme for smart grid ami networks[C]//Proc. of IEEE Globecom. 2016.Google Scholar
- 33.Li, H.T., Ma, J.F., and Fu, S., A privacy-preserving data collection model for digital community. Science China Inf. Sci. 58(3):1–16, 2014.Google Scholar
- 35.Song, J, Myungae, C., SHOES: secure healthcare oriented environment service model. In Proceedings of the IEEE Biomedical Circuits and Systems Conference Healthcare Technology, London, Bio CAS: 89–93, 2006.Google Scholar