Biblio

Electronic Medical Record (EMR) is a medical record management system. EMR contains personal data of patients that is critical. The critical nature of medical records is the reason for the necessity to develop security policies as guidelines for EMR in SIMRS in XZY Hospital. In this study, analysis and risk assessment conducted to EMR management at SIMRS in XZY Hospital. Based on this study, the security of SIMRS in XZY Hospital is categorized as high. Security and Privacy Control mapping based on NIST SP800-53 rev 5 obtained 57 security controls related to privacy aspects as control options to protect EMR in SIMRS in XZY Hospital. The policy designing was done using The Triangle framework for Policy Analysis. The analysis obtained from the policy decisions of the head of XYZ Hospital. The contents of the security policy are provisions on the implementation of security policies of EMR, outlined of 17 controls were selected.

The invent of distributed programming frameworks like Hadoop paved way for processing voluminous data known as big data. Due to exponential growth of data, enterprises started to exploit the availability of cloud infrastructure for storing and processing big data. Insider attacks on outsourced data causes leakage of sensitive data. Therefore, it is essential to sanitize data so as to preserve privacy or non-disclosure of sensitive data. Privacy Preserving Data Publishing (PPDP) and Privacy Preserving Data Mining (PPDM) are the areas in which data sanitization plays a vital role in preserving privacy. The existing anonymization techniques for MapReduce programming can be improved to have a misusability measure for determining the level of sanitization to be applied to big data. To overcome this limitation we proposed a framework known as M-Sanit which has mechanisms to exploit misusability score of big data prior to performing sanitization using MapReduce programming paradigm. Our empirical study using the real world cloud eco system such as Amazon Elastic Cloud Compute (EC2) and Amazon Elastic MapReduce (EMR) reveals the effectiveness of misusability score based sanitization of big data prior to publishing or mining it.