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Intelligent privacy preservation electronic health record framework using soft computing

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References

  • Fovino, I. N. (2008). Privacy Preserving Data Mining, Concepts, Techniques, and Evaluation Methodologies. In Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications (pp. 2379-2401 ). IGI Global.
  • Zhang, N., & Zhao, W. (2007). Privacy-preserving data mining systems. Computer, 40(4), 52-58.
  • Jensen, P. B., Jensen, L. J., & Brunak, S. (2012). Mining electronic health records: towards better research applications and clinicalcare. Nature Reviews Genetics, 13(6), 395-405.
  • Schraagen, M., & Kosters, W. (2012, September). Data-driven name reduction for record linkage. In Second International Conference on the Innovative Computing Technology (INTECH 2012) (pp. 311-316). IEEE.
  • Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 557-570.
  • Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkita subramaniam, M. (2007). l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 3-es.
  • Li, N., Li, T., & Venkata subramanian, S. (2007, April). t-closeness: Privacy beyond k-anonymity and l-diversity. In 2007 IEEE 23rd International Conference on Data Engineering (pp. 106-115). IEEE.
  • Aggarwal, C. C., & Yu, P. S. (2001, May). Outlier detection for high dimensional data. In Proceedings of the 2001 ACM SIGMOD international conference on Management of data (pp. 37-46).
  • Agrawal, R., Srikant, R., & Vu, Q. (2000). U.S. Patent No. 6,061,682. Washington, DC: U.S. Patent and Trademark Office.
  • Liu, K., Giannella, C., & Kargupta, H. (2006, September). An attacker’s view of distance preserving maps for privacy preserving data mining. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 297-308). Springer, Berlin, Heidelberg.
  • Nayak, G., & Devi, S. (2011). A survey on privacy preserving data mining: approaches and techniques. International Journal of Engineering Science and Technology, 3(3), 2127-2133.
  • Keyvanpour, M., & Moradi, S. S. (2011). Classification and evaluation the privacy preserving data mining techniques by using a data modification-based framework. arXiv preprint arXiv:1105.1945.
  • Liu, H. (2007). Social network profiles as taste performances. Journal of Computer-Mediated Communication, 13(1), 252-275.
  • Li, X. B., & Sarkar, S. (2006). Privacy protection in data mining: A perturbation approach for categorical data. Information Systems Research, 17(3), 254-270.
  • Dalenius, T., & Reiss, S. P. (1982). Data-swapping: A technique for disclosure control. Journal of statistical planning and inference, 6(1), 73-85.
  • Levin, R., & Reiss, P. C. (1984). Tests of a Schumpeterian model of R&D and market structure. In R&D, patents, and productivity (pp. 175-208). University of Chicago Press.
  • Schlörer, J. (1981). Security of statistical databases: multidimensional transformation. ACM Transactions on Database Systems (TODS), 6(1), 95-112.
  • Fuhrer, J., & Moore, G. (1995). Inflation persistence. The Quarterly Journal of Economics, 110(1), 127-159.
  • Domingo-Ferrer, J., & Mateo-Sanz, J. M. (2001). An empirical comparison of SDC methods for continuous microdata in terms of information loss and disclosure risk. In Proc. Joint ECE/Eurostat Work Session Stat. Data Confidentiality, Conf. Eur. Satisticians.
  • Defays, D., & Anwar, M. N. (1998). Masking microdata using microaggregation. Journal of Official Statistics, 14(4), 449.
  • Domingo-Ferrer, J., & Mateo-Sanz, J. M. (2002). Practical data-oriented micro aggregation for statistical disclosure control. IEEE Transactions on Knowledge and data Engineering, 14(1), 189-201.
  • Domingo-Ferrer, J., Mateo-Sanz, J. M., & Torra, V. (2001, May). Comparing SDC methods for microdata on the basis of information loss and disclosure risk. In Pre-proceedings of ETK-NTTS (Vol. 2, pp. 807-826 ).
  • Kim, J. J., & Winkler, W. E. (1995). Masking microdata files. In Proceedings of the Survey Research Methods Section, American Statistical Association.
  • Rabinowitz, J. (1993). Diagnostic reasoning and reliability: A review of the literature and a model of decision-making. The Journal of mind and behavior, 297-315.
  • Cameron, K. S. (1986). Effectiveness as paradox: Consensus and conflict in conceptions of organizational effectiveness. Management science, 32(5), 539-553.
  • Edgar, D. M., Dement, W. C., & Fuller, C. A. (1993). Effect of SCN lesions on sleep in squirrel monkeys: evidence for opponent processes in sleep-wake regulation. Journal of Neuroscience, 13(3), 1065-1079.
  • Aggarwal, C. C., & Philip, S. Y. (Eds.). (2008). Privacy-preserving data mining: models and algorithms. Springer Science & Business Media.
  • Mohammed, N., Fung, B. C., Wang, K., & Hung, P. C. (2009, March). Privacy-preserving data mashup. In Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology (pp. 228-239).
  • Susmel, L., Meneghetti, G., & Atzori, B. (2008). On the use of the Modified Manson-Coffin Curve Method to estimate low-cycle fatigue lifetime of notched components subjected to multiaxial cyclic loading. In Sixth International Conference on Low Cycle Fatigue (pp. 119-124 ). DVM.
  • Vaidya, J., Atluri, V., & Guo, Q. (2010). The role mining problem: A formal perspective. ACM Transactions on Information and System Security (TISSEC), 13(3), 1-31.
  • Verykios, V. S., Bertino, E., Fovino, I. N., Provenza, L. P., Saygin, Y., & Theodoridis, Y. (2004). State-of-the-art in privacy preserving data mining. ACM Sigmod Record, 33(1), 50-57.
  • A. Kumar, R. Tadayoni and L. T. Sorensen, “Metric based efficiency analysis of educational ERP system usability-using fuzzy model,” 2015 Third International Conference on Image Information Processing (ICIIP), Waknaghat, 2015, pp. 382-386.
  • S. Aggrwal, A. Kumar and R. Kumar, “The Privacy Preservation of Patients’ Health Records using Soft Computing in Python,” 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 2019, pp. 156-160.
  • A. Kumar and R. Kumar, “Privacy-Preservation of Vertically Partitioned Electronic Health Record using Perturbation Methods,” 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 2019, pp. 161-166.
  • Kumar, A., & Kumar, R. (2020). Privacy Preservation of Electronic Health Record: Current Status and Future Direction. In Handbook of Computer Networks and Cyber Security (pp. 715-739). Springer, Cham.

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