Healthcare centers always aim to deliver the best quality healthcare services to patients and earn their satisfaction. Technology has played a major role in achieving these goals, such as clinical decision-support systems and mobile health social networks. These systems have improved the quality of care services by speeding-up the diagnosis process with accuracy, and allowing caregivers to monitor patients remotely through the use of WBS, respectively. However, these systems’ accuracy and efficiency are dependent on patients’ health information, which must be inevitably shared over the network, thus exposing them to cyber-attacks. Therefore, privacy-preserving services are ought to be employed to protect patients’ privacy. In this work, we proposed a privacy-preserving healthcare system, which is composed of two subsystems. The first is a privacy-preserving clinical decision-support system. The second subsystem is a privacy-preserving Mobile Health Social Network (MHSN). The former was based on decision tree classifier that is used to diagnose patients with new symptoms without disclosing patients’ records. Whereas the latter would allow physicians to monitor patients’ current condition remotely through WBS; thus sending help immediately in case of a distress situation detected. The social network, which connects patients of similar symptoms together, would also provide the service of seeking help of near-by passing people while the patient is waiting for an ambulance to arrive. Our model is expected to improve healthcare services while protecting patients’ privacy.
Berner, E.S. and La Lande, T.J. (2007) Overview of Clinical Decision Support Systems. In: Clinical Decision Support Systems, Springer, Berlin, 3-22.
Liang, X., Lu, R., Chen, L., Lin, X. and Shen, X. (2011) PEC: A Privacy-Preserving Emergency Call Scheme for Mobile Healthcare Social Networks. Journal of Communications and Networks, 13, 102-112.
Liu, X., Lu, R., Ma, J., Chen, L. and Qin, B. (2015) Privacy-Preserving Patient-Centric Clinical Decision Support System on Nave Bayesian Classification. IEEE Journal of Biomedical and Health Informatics, 1.
Lu, R., Lin, X. and Shen, X. (2013) SPOC: A Secure and Privacy-Preserving Opportunistic Computing Framework for Mobile-Healthcare Emergency. IEEE Transactions on Parallel and Distributed Systems, 24, 614-624.
Vaidya, J., Shafiq, B., Fan, W., Mehmood, D. and Lorenzi, D. (2014) A Random Decision Tree Framework for Privacy-Preserving Data Mining. IEEE Transactions on Dependable and Secure Computing, 11, 399-411. https://doi.org/10.1109/TDSC.2013.43
Kevin, L. (2010) One Patient, One Record: Report on One-Day Symposium to Promote Patient e-Health. Technical Report, Ottawa.
Ledley, R.S. and Lusted, L.B. (1959) Reasoning Foundations of Medical Diagnosis; Symbolic Logic, Probability, and Value Theory Aid Our Understanding of How Physicians Reason. Science, 130, 9-21. https://doi.org/10.1126/science.130.3366.9
Musen, M.A., Middleton, B. and Greenes, R.A. (2014) Clinical Decision-Support Systems. In: Shortliffe, E.H. and Cimino, J.J., Eds., Biomedical Informatics, Springer, London, 643-674.
Berner, E.S. (2009) Clinical Decision Support Systems: State of the Art. AHRQ Publication, 4-26.
Kaplan, B. (2001) Evaluating Informatics Applications Ome Alternative Approaches: Theory, Social Interactionism, and Call for Methodological Pluralism. International Journal of Medical Informatics, 64, 39-56.
Miller, P.L. and Sittig, D.F. (1990) The Evaluation of Clinical Decision Support Systems: What Is Necessary versus What Is Interesting. Informatics for Health and Social Care, 15, 185-190.
Zhou, J., Cao, Z., Dong, X., Xiong, N. and Vasilakos, A.V. (2015) 4S: A Secure and Privacy-Preserving Key Management Scheme for Cloud-Assisted Wireless Body Area Network in M-Healthcare Social Networks. Information Sciences, 314, 255-276. https://doi.org/10.1016/j.ins.2014.09.003
Kim, J., Beresford, A.R. and Stajano, F. (2007) Towards a Security Policy for Ubiquitous Healthcare Systems (Position Paper). In: Ubiquitous Convergence Technology, Springer, Berlin, 263-272.
Rajeswari, A. and Shanmugapriya, S. An Efficient Mobile Health Care Emergency Services.
Lu, R., Lin, X., Liang, X. and Shen, X.S. (2010) Secure Handshake with Symptoms-Matching: The Essential to the Success of M-Healthcare Social Network. In: Proceedings of the 5th International Conference on Body Area Networks, ACM, New York, 8-15. https://doi.org/10.1145/2221924.2221927
Warner, H.R. (1961) A Mathematical Approach to Medical Diagnosis: Application to Congenital Heart Disease. JAMA, 177, 177. https://doi.org/10.1001/jama.1961.03040290005002
Schurink, C., Lucas, P., Hoepelman, I. and Bonten, M. (2005) Computer-Assisted Decision Support for the Diagnosis and Treatment of Infectious Diseases in Intensive Care Units. The Lancet Infectious Diseases, 5, 305-312. https://doi.org/10.1016/S1473-3099(05)70115-8
Zhan, J. (2007) Using Homomorphic Encryption for Privacy-Preserving Collaborative Decision Tree Classification. IEEE Symposium on Computational Intelligence and Data Mining, 637-645. https://doi.org/10.1109/CIDM.2007.368936
Du, W. and Zhan, Z. (2002) Building Decision Tree Classifier on Private Data. In: Proceedings of the IEEE International Conference on Privacy, Security and Data Mining, Australian Computer Society, Inc., Vol. 14, 1-8.
Bost, R., Popa, R.A., Tu, S. and Goldwasser, S. (2015) Machine Learning Classification over Encrypted Data. NDSS.
Lindell, Y. and Pinkas, B. (2000) Privacy Preserving Data Mining. In: Advances in Cryptology, Springer, Berlin, 36-54. https://doi.org/10.1007/3-540-44598-6_3
Lindell, Y. and Pinkas, B. (2002) Privacy Preserving Data Mining. Journal of Cryptology, 15, 177-206. https://doi.org/10.1007/s00145-001-0019-2
Emekci, F., Sahin, O.D., Agrawal, D. and El Abbadi, A. (2007) Privacy Preserving Decision Tree Learning over Multiple Parties. Data & Knowledge Engineering, 63, 348-361. https://doi.org/10.1016/j.datak.2007.02.004
Bethencourt, J., Sahai, A. and Waters, B. (2007) Ciphertext-Policy Attribute-Based Encryption. IEEE Symposium on Security and Privacy, 321-334. https://doi.org/10.1109/SP.2007.11
Cheung, L. and Newport, C. (2007) Provably Secure Ciphertext Policy ABE. In: Proceedings of the 14th ACM Conference on Computer and Communications Security, ACM, New York, 456-465. https://doi.org/10.1145/1315245.1315302
Goyal, V., Jain, A., Pandey, O. and Sahai, A. (2008) Bounded Ciphertext Policy Attribute Based Encryption. In: Automata, Languages and Programming, Springer, Berlin, 579-591.
Waters, B. (2011) Ciphertext-Policy Attribute-Based Encryption: An Expressive, Efficient, and Provably Secure Realization. In: Public Key Cryptography-PKC 2011, Springer, Berlin, 53-70.
Yu, M. and Xu, Q. (2012) A Simple and Effective Scheme of Ciphertext-Policy ABE. 8th International Conference on Computational Intelligence and Security, 516-519. https://doi.org/10.1109/CIS.2012.122
Gentry, C., et al. (2009) Fully Homomorphic Encryption using Ideal Lattices. STOC, Number 2009, 169-178. https://doi.org/10.1145/1536414.1536440
Gentry, C. (2009) A Fully Homomorphic Encryption Scheme. PhD Thesis, Stanford University.
Brakerski, Z., Gentry, C. and Vaikuntanathan, V. (2014) (Leveled) Fully Homomorphic Encryption without Bootstrapping. ACM Transactions on Computation Theory, 6, 13. https://doi.org/10.1145/2633600
Brakerski, Z. and Vaikuntanathan, V. (2011) Efficient Fully Homomorphic Encryption from (Standard) LWE. 52nd Annual Symposium on Foundations of Computer Science, 97-106.
Khedr, A., Gulak, G. and Vaikuntanathan, V. (2016) SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers. IEEE Transactions on Computers, 65, 2848-2858. https://doi.org/10.1109/TC.2015.2500576
Liu, D. (2015) Practical Fully Homomorphic Encryption without Noise Reduction. IACR Cryptology ePrint Archive, 468.
Zhou, T., Yang, X., Zhang, W. and Wu, L. (2016) Efficient Fully Homomorphic Encryption with Circularly Secure Key Switching Process. International Journal of High Performance Computing and Networking, 9, 417-422. https://doi.org/10.1504/IJHPCN.2016.080414
Conti, M. and Kumar, M. (2010) Opportunities in Opportunistic Computing. Computer, 43, 42-50. https://doi.org/10.1109/MC.2010.19
Avvenuti, M., Corsini, P., Masci, P. and Vecchio, A. (2007) Opportunistic Computing for Wireless Sensor Networks. IEEE International Conference on Mobile Ad Hoc and Sensor Systems, 1-6.
Kulkarni, N.R. and Terdal, S. (2013) Pervasive Monitoring of M-Health Care using Android.
Xing, H., Chen, C., Yang, B. and Guan, X. (2013) SymMatch: Secure and Privacy-Preserving Symptom Matching for Mobile Healthcare Social Networks. International Conference on Wireless Communications & Signal Processing, 1-6.
Quinlan, J.R. (2014) C4.5: Programs for Machine Learning. Elsevier.