An Optimized Integrated Framework of Big Data Analytics Managing Security and Privacy in Healthcare Data

1. Xu, L., Jiang, C., Wang, J., Yuan, J., & Ren, Y. (2014). Information security in big data: Privacy and data mining. Journal of Rapid Open Access Publication, 2, 1149–1176.

   Google Scholar 

2. Yu, W. D., Kollipara, M., Penmetsa, R., & Elliadka, S. (2013). A distributed storage solution for cloud based e-Healthcare Information System. In Proceedings of the IEEE 15th international conference on e-health networking, applications & services (Healthcom’13); Lisbon, Portugal (pp. 476–480).

3. Athey, B. D., Braxenthaler, M., Haas, M., & Guo, Y. (2013). Transmart: An open source and community-driven informatics and data sharing platform for clinical and translational research. AMIA Summits on Translational Science Proceedings, 2013, 6–8.

   Google Scholar 

4. Jeanquartier, F., & Holzinger, A. (2013). On visual analytics and evaluation in cell physiology: A case study. In A. Cuzzocrea, C. Kittl, D. E. Simos, E. Weippl, & L. Xu (Eds.), Availability, reliability, and security in information systems and HCI (pp. 495–502). Berlin: Springer.

   Chapter  Google Scholar 

5. Jiang, M., Zhang, S., Li, H., & Metaxas, D. N. (2015). Computer-aided diagnosis of mammographic masses using scalable image retrieval. IEEE Transactions on Biomedical Engineering, 62(2), 783–792.

   Article  Google Scholar 

6. Johnston, M. E., Langton, K. B., Brian Haynes, R., & Mathieu, A. (1994). Effects of computer-based clinical decision support systems on clinician performance and patient outcome: A critical appraisal of research. Annals of Internal Medicine, 120(2), 135–142.

   Article  Google Scholar 

7. Jung, K., LePendu, P., Iyer, S., Bauer-Mehren, A., Percha, B., & Shah, N. H. (2014). Functional evaluation of out-of-the-box text-mining tools for data-mining tasks. Journal of the American Medical Informatics Association, 22(1), 121–131.

   Article  Google Scholar 

8. Vararuk, A., Petrounias, I., & Kodogiannis, V. (2007). Data mining techniques for HIV/AIDS data management in Thailand. Journal of Enterprise Information Management. https://doi.org/10.1108/17410390810842255.

   Article  Google Scholar 

9. Asha, T., Natarajan, S., & Murthy, K. N. B. (2011). A data mining approach to the diagnosis of tuberculosis by cascading clustering and classification. Journal of Computing 3 arXiv:1108.1045 [cs.AI].

10. Uçar, T., & Karahoca, A. (2011). Predicting existence of Mycobacterium tuberculosis on patients using data mining approaches. Procedia Computer Science, 3, 1404–1411.

    Article  Google Scholar 

11. Garg, S., & Rupal, N. (2015). A review on tuberculosis using data mining approaches. International Journal of Engineering Development and Research, 3(3), 1–4.

    Google Scholar 

12. Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561–2573.

    Article  Google Scholar 

13. Kawamoto, K., Houlihan, C. A., Andrew Balas, E., & Lobach, D. F. (2005). Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success. BMJ, 330(7494), 765.

    Article  Google Scholar 

14. Keim, D. A. (2002). Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 8(1), 1–8.

    Article  MathSciNet  Google Scholar 

15. Metcalfe, J. Z., Porco, T. C., Westenhouse, J., Damesyn, M., Facer, M., Hill, J., et al. (2013). Tuberculosis and HIV co-infection, California, USA, 1993–2008. Emerging Infectious Diseases, 19(3), 400.

    Google Scholar 

16. Kim, S.-H., Kim, N.-U., & Chung, T.-M. (2013). Attribute relationship evaluation methodology for big data security. In 2013 international conference on IT convergence and security (ICITCS), IEEE (pp. 1–4).

17. Rama Lakshmi, K., & Prem Kumar, S. (2013). Utilisation of data mining techniques for prediction and diagnosis of major life threatening diseases survivability-review. International Journal for Scientific and Engineering Research, 4(6), 923–932.

    Google Scholar 

18. https://www.cdc.gov/about/organization/cio.html.

19. https://wonder.cdc.gov/tb.html.

20. Sánchez, M. A., Uremovich, S., & Acrogliano, P. (2009). Mining Tuberculosis Data. In P. Berka, J. Rauch, & D. A. Zighed (Eds.), Data mining and medical knowledge management: Cases and applications. New York: Medical Information Science Reference.

    Google Scholar 

21. Han, W., Susilo, Y., & Yan, J. (2012). Privacy preserving decentralized key-policy attribute-based encryption. IEEE Transactions on Parallel and Distributed Systems, 23, 2150–2162.

    Article  Google Scholar 

22. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105). Curran Associates.

23. Labrinidis, A., & Jagadish, H. V. (2012). Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5(12), 2032–2033.

    Article  Google Scholar 

24. Lalys, F., Riffaud, L., Bouget, D., & Jannin, P. (2012). A framework for the recognition of high-level surgical tasks from video images for cataract surgeries. IEEE Transactions on Biomedical Engineering, 59(4), 966–976.

    Article  Google Scholar 

25. Langs, G., Hanbury, A., Menze, B., & Muller, H. (2013). VISCERAL: Towards large data in medical imaging challenges and directions. In Medical content-based retrieval for clinical decision support (Vol. 7723, pp. 92–98). Springer.

26. Yazan, A., Yong, W., & Raj Kumar, N. (2015). Big data life cycle: Threats and security model. In: 21st Americas conference on information systems.

27. Greenleaf, Graham and Chung, Philip and Mowbray, Andrew, Influencing Data Privacy Practices By Global Free Access: The International Privacy Law Library (November 14, 2014). UNSW Law Research Paper No. 2014-56. 

28. OECD. (2013). Data-driven healthcare innovation, management and policy, DELSA/HEA(2013) 13. Paris: OECD.

    Google Scholar 

29. Chauhan, R., & Kaur, H. (2017). A feature based reduction technique on large scale databases. International Journal of Data Analysis Techniques and Strategies., 9(3), 207–221.

    Article  Google Scholar 

30. Chauhan, R., Kaur, H., & Chang, V. (2017). Advancement and applicability of classifiers for variant exponential model to optimize the accuracy for deep learning. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-017-0561-x.

    Article  Google Scholar 

31. Kaur, H., Chauhan, R., & Wasan, S. K. (2014). A Bayesian network model for probability estimation. In M. Khosrow-Pour (Ed.), Encyclopaedia of information science and technology (3rd ed.) (pp. 1551–1558). Retrieved December 10, 2014, from https://doi.org/10.4018/978-1-4666-5888-2.ch148.

32. Chauhan, R., & Kaur, H. (2015). Big data application in medical domain. In D. P. Acharjya, et al. (Eds.), Computational intelligence for big data analysis: Frontier advances and applications. Volume 19 of the series adaptation, learning, and optimization (pp. 165–179). Basel: Springer.

    Chapter  Google Scholar 

33. Kaur, H., Tao, X. (2014).  ICT and Millennium Development Goals: A United Nations Perspective, pp. 271, Springer, New York.

34. Chauhan, R., Kaur, H., Lechman, E., Marszk, A. (2017). Big data analytics for ICT monitoring and development. In: Kaur, H., et al. (eds.) Catalyzing Development Through ICT Adoption: The Developing World Experience, pp. 25–36. Springer, New York.

    Chapter  Google Scholar 

35. Hu, P., & Gao, H. (2017). A key-policy attribute-based encryption scheme for general circuit from bilinear maps. International Journal Network Security, 19(5), 704–710.

    Google Scholar 

36. Lai, J., Deng, R. H., Guan, C., & Weng, J. (2013). Attribute-based encryption with verifiable outsourced decryption. IEEE Transactions on Information Forensics and Security, 8(8), 1343–1354.

    Article  Google Scholar 

37. Lee, C. C., Chung, P. S., & Hwang, M. S. (2013). A survey on attribute-based encryption schemes of access control in cloud environments. International Journal Network Security, 15, 231–240.

    Google Scholar 

38. Lewis, G., Echeverria, S., Simanta, S., Bradshaw, B., & Root, J. (2014). Tactical cloudlets: Moving cloud computing to the edge. In IEEE military communications conference (pp. 1440–1446).

39. Li, J., Huang, X., Li, J., Chen, X., & Xiang, Y. (2014). Securely outsourcing attribute-based encryption with checkability. IEEE Transactions on Parallel and Distributed Systems, 25(8), 2201–2210.

    Article  Google Scholar 

40. Agarwal, S., Nguyen, D. T., Teeter, L. D., & Graviss, E. A. (2017). Spatial-temporal distribution of genotyped tuberculosis cases in a county with active transmission. BMC Infectious Diseases, 17, 378.

    Article  Google Scholar 

41. Kriegel, H.-P., Kroger, P., & Zimek, A. (2009). Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Transactions on Knowledge Discovery from Data, 3(1), 1–58. https://doi.org/10.1145/1497577.1497578.

    Article  Google Scholar 

42. Li, J., Yao, W., Zhang, Y., Qian, H., & Han, J. (2017). Flexible and fine-grained attribute-based data storage in cloud computing. IEEE Transactions on Services Computing, 10(5), 785–796.

    Article  Google Scholar 

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