Achieving data utility-privacy tradeoff in Internet of Medical Things: A machine learning approach

Yin, 2016, The internet of things in healthcare: an overview, J. Ind. Inf. Integr., 1, 3 Wei, 2011, The internet of things for resident health information service platform research, 631 Cecil, 2018, An iomt-based cyber training framework for orthopedic surgery using next generation internet technologies, Inform. Med. Unlocked, 12, 128, 10.1016/j.imu.2018.05.002 La, 2015, Extensible disease diagnosis cloud platform with medical sensors and IoT devices, 371 Orsini, 2017, My smart age with HIV: An innovative mobile and ioMT framework for patient’s empowerment, 1 Dimitrov, 2016, Medical internet of things and big data in healthcare, Healthc. Inform. Res., 22, 156, 10.4258/hir.2016.22.3.156 Plageras, 2017, Efficient large-scale medical data (ehealth big data) analytics in internet of things, 21 Ni, 2017, Cross-network clustering and cluster ranking for medical diagnosis, 163 Wu, 2017, Clinical phenotypes of nasal polyps and comorbid asthma based on cluster analysis of disease history, J. Allergy Clin. Immunol.: Pract., 6, 1297, 10.1016/j.jaip.2017.09.020 Ning, 2016, Study of the clinical phenotype of symptomatic chronic airways disease by hierarchical cluster analysis and two-step cluster analyses, Zhonghua Nei Ke Za Zhi, 55, 679 Sweeney, 2002, K-anonymity: A model for protecting privacy, Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 10, 557, 10.1142/S0218488502001648 Machanavajjhala, 2006, L-diversity: Privacy beyond k-anonymity Dwork, 2008, Differential privacy: a survey of results, 1 Islam, 2015, The internet of things for health care: a comprehensive survey, IEEE Access, 3, 678, 10.1109/ACCESS.2015.2437951 Sodhro, 2018, Convergence of iot and product lifecycle management in medical health care, Future Gener. Comput. Syst., 86, 380, 10.1016/j.future.2018.03.052 Mahmoud, 2018, Enabling technologies on cloud of things for smart healthcare, IEEE Access, 6, 31950, 10.1109/ACCESS.2018.2845399 Cruz, 2018, A reference model for internet of things middleware, IEEE Internet Things J., 5, 871, 10.1109/JIOT.2018.2796561 Liang, 2018, A payload-dependent packet rearranging covert channel for mobile voIP traffic, Inform. Sci., 465, 162, 10.1016/j.ins.2018.07.011 Zhang, 2018, Building covert timing channels by packet rearrangement over mobile networks, Inform. Sci., 445–446, 66, 10.1016/j.ins.2018.03.007 Wu, 2018, FCSS: Fog-computing-based content-aware filtering for security services in information-centric social networks, IEEE Trans. Emerg. Top. Comput., PP Moreira, 2018, Neuro-fuzzy model for hellp syndrome prediction in mobile cloud computing environments, Concurr. Comput.: Pract. Exper., PP Guan, 2019, APPA: An anonymous and privacy preserving data aggregation scheme for fog-enhanced iot, J. Netw. Comput. Appl., 125, 82, 10.1016/j.jnca.2018.09.019 Liang, 2018, Building packet length covert channel over mobile voIP traffics, J. Netw. Comput. Appl., 118, 144, 10.1016/j.jnca.2018.06.012 Miao, 2016, M2-abks: attribute-based multi-keyword search over encrypted personal health records in multi-owner setting, J. Med. Syst., 40, 1, 10.1007/s10916-016-0617-z Zhang, 2018, A sensitive network jitter measurement for covert timing channels over interactive traffic, Multimedia Tools Appl., PP Liu, 2018, A clustering k-anonymity privacy-preserving method for wearable iot devices, Secur. Commun. Netw., 2018, 1, 10.1155/2018/1635081 Ganta, 2008, Composition attacks and auxiliary information in data privacy, 265 Guan, 2017, Achieving efficient and secure data acquisition for cloud-supported internet of things in smart grid, IEEE Internet Things J., 4, 1934, 10.1109/JIOT.2017.2690522 Guan, 2019, Effect: an efficient flexible privacy-preserving data aggregation scheme with authentication in smart grid, Sci. China Inform. Sci., 62, 1, 10.1007/s11432-018-9451-y Abadi, 2016, Deep learning with differential privacy, 308 Guan, 2018, Privacy-preserving and efficient aggregation based on blockchain for power grid communications in smart communities, IEEE Commun. Mag., 56, 82, 10.1109/MCOM.2018.1700401 Yuan, 2017, Practical privacy-preserving mapreduce based K-means clustering over large-scale dataset, IEEE Trans. Cloud Comput. Gheid, 2017, Efficient and privacy-preserving k-means clustering for big data mining, 791 Su, 2016, Differentially private K-means clustering, 26 Ren, 2017, DPLK-Means: A novel differential privacy K-means mechanism, 133 Zhao, 2009, Parallel K-means clustering based on mapreduce, 674 Du, 2009, Transactions papers a routing-driven elliptic curve cryptography based key management scheme for heterogeneous sensor networks, IEEE Trans. Wirel. Commun., 8, 1223, 10.1109/TWC.2009.060598 Guimaraes, 2018, Intelligent network security monitoring based on optimum-path forest clustering, IEEE Netw., PP, 1 Du, 2007, An effective key management scheme for heterogeneous sensor networks, Ad Hoc Netw., 5, 24, 10.1016/j.adhoc.2006.05.012 Wu, 2018, Big data analysis-based secure cluster management for optimized control plane in software-defined networks, IEEE Trans. Netw. Serv. Manag., 15, 27, 10.1109/TNSM.2018.2799000 Xiao, 2007, A survey of key management schemes in wireless sensor networks, J. Comput. Commun., 30, 2314, 10.1016/j.comcom.2007.04.009 Du, 2008, Security in wireless sensor networks, IEEE Wirel. Commun. Mag., 15, 60, 10.1109/MWC.2008.4599222 Mccallum, 2000, Efficient clustering of high-dimensional data sets with application to reference matching, 169 McSherry, 2009, Privacy integrated queries: an extensible platform for privacy-preserving data analysis, 19 Dwork, 2011, A firm foundation for private data analysis, Commun. ACM, 54, 86, 10.1145/1866739.1866758