Healthcare organizations or providers must ensure that encryption scheme is efficient, easy to use by both patients and healthcare professionals, and easily extensible to include new electronic health records. In: IEEE translations and content mining are permitted for academic research. Data collection includes security and network devices logs and event information. For instance, The Birth field has been generalized to the year (e.g. Thereafter, we provide some proposed techniques and approaches that were reported in the literature to deal with security and privacy risks in healthcare while identifying their limitations. From a security perspective, securing big health data technology is a necessary requirement from the first phase of the lifecycle. Meyerson A, Williams R. On the complexity of optimal k-anonymity. 2013. WHO. The information authentication can pose special problems, especially man-in-the-middle (MITM) attacks. After exploring the tradeoffs of correcting these vulnerabilities, they found that User Agent information strongly correlates to individual users. Big Data security and privacy issues in healthcare—Harsh KupwadePatil, Ravi Seshadri. Article  PubMed Google Scholar. 2013. http://hadoop.apache.org/docs/r0.20.2/fair_scheduler.html. In: International conference on logistics engineering, management and computer science (LEMCS 2014). The big data revolution in healthcare, accelerating value and innovation. Our community of professionals is committed to lifetime learning, career progression and sharing expertise for the benefit of individuals and organizations around the globe. Additional findings of this report include: 325 large breaches of PHI, compromising 16,612,985 individual patient records. On the other side, the collected data may contain sensitive information, which makes extremely important to take sufficient precautions during data transformation and storing. drive health research, knowledge discovery, clinical care, and personal health management), there are several obstacles that impede its true potential, including technical challenges, privacy and security issues and skilled talent. As well, privacy methods need to be enhanced. In: The 10th international conference for internet technology and secured transactions (ICITST-2015). The four categories in which HybrEx MapReduce enables new kinds of applications that utilize both public and private clouds are as shown in Fig. 2: The four Execution categories for HybrEx MapReduce [62]. Few traditional methods for privacy preserving in big data are described in brief here. In order to guarantee the safety of the collected data, the data should remain isolated and protected by maintaining access-level security and access control (utilizing an extensive list of directories and databases as a central repository for user credentials, application logon templates, password policies and client settings) [22], and defining some security measures like data anonymization approach, permutation, and data partitioning. Authors prove consent of publication for this research. Int J Uncertain Fuzziness. Additionally, ransomware, defined as a type of malware that encrypts data and holds it hostage until a ransom demand is met, has identified as the most prominent threat to hospitals. One of the most promising fields where big data can be applied to make a change is healthcare. In: 21st Americas conference on information systems. The suggested solution includes storing and processing data in distributed sources through data correlation schemes. 2016;3:25. Cloud-based storage has facilitated data mining and collection. In surveys, the security experts grumble about the existing tools and recommend for special tools and methods for big data security analysis. Lu R, Zhu H, Liu X, Liu JK, Shao J. These created knowledges are considered sensitive data, especially in a competitive environment. David Houlding, MSc, CISSP. Additionally, we state open research issues in big data. As new users of SOPHIA, they become part of a larger network of 260 hospitals in 46 countries that share clinical insights across patient cases and patient populations, which feeds a knowledge-base of biomedical findings to accelerate diagnostics and care [12]. In k-anonymization, if the quasi-identifiers containing data are used to link with other publicly available data to identify individuals, then the sensitive attribute (like disease) as one of the identifier will be revealed. In: IEEE 3rd international conference on cloud computing. 2001;13(6):1010–27. These are two optional security metrics to measure and ensure the safety of a healthcare system [38]. 2011. Hadoop Tutorials. It focuses on the use and governance of individual’s personal data like making policies and establishing authorization requirements to ensure that patients’ personal information is being collected, shared and utilized in right ways. Role-based access control (RBAC) [34] and attribute-based access control (ABAC) [35, 36] are the most popular models for EHR. Part of Big data is slowly but surely gaining its popularity in healthcare. Privacy is often defined as having the ability to protect sensitive information about personally identifiable health care information. If want to make data L-diverse though sensitive attribute has not as much as different values, fictitious data to be inserted. She drafted also several manuscripts like “Big data security and privacy in healthcare: A Review” that was published in Procedia Computer Science journal. 2) Encryption Data encryption is an efficient means of preventing unauthorized access of sensitive data. 2007. Kim S-H, Kim N-U, Chung T-M. Such existing policies are unlikely to yield effective strategies for improving privacy, or to be scalable over time. And to go further, we will try to solve the problem of reconciling security and privacy models by simulating diverse approaches to ultimately support decision making and planning strategies. For instance [23], transport layer security (TLS) and its predecessor, secure sockets layer (SSL), are cryptographic protocols that provide security for communications over networks such as the Internet. Knowledge creation phase Finally, the modeling phase comes up with new information and valued knowledges to be used by decision makers. Big data processing systems suitable for handling a diversity of data types and applications are the key to supporting scientific research of big data. Iyenger V. Transforming data to satisfy privacy constraints. Big Data is the vouluminous amount of data with variety in its nature along with the complexity of handling such data. Home » Research » Research Paper On Big Data Security. J Rapid Open Access Publ. In the implementing architecture process, enterprise data has properties different from the standard examples in anonymization literature [58]. In: 8th annual international workshop on selected areas in cryptography, London: Springer-Verlag. The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data. 2012;83:38–42. 4) Access control Once authenticated, the users can enter an information system but their access will still be governed by an access control policy which is typically based on privileges and rights of each practitioner authorized by patient or a trusted third party. Depending on the score obtained through this calculation, an alert occurs in detection system or process terminate by prevention system. The main advantage of this technique is that it intercepts attribute disclosure, and its problem is that as size and variety of data increase, the odds of re-identification increase too. Different countries have different policies and laws for data privacy. Introduction The term “big data” is normally used as a marketing concept refers to data sets whose size is further than the potential of normally used enterprise tools to gather, manage and organize, and process within an acceptable elapsed time. Data security accessing for HDFS based on attribute-group in cloud computing. One of the most promising fields where big data can be applied to make a change is healthcare. 2009;78:141–60. This shift is being spurred by aging populations and lifestyle changes; the proliferation of software applications and mobile devices; innovative treatments; heightened focus on care quality and value; and evidence-based medicine as opposed to subjective clinical decisions—all of which are leading to offer significant opportunities for supporting clinical decision, improving healthcare delivery, management and policy making, surveilling disease, monitoring adverse events, and optimizing treatment for diseases affecting multiple organ systems [1, 2]. Sedayao J, Bhardwaj R. Making big data, privacy, and anonymization work together in the enterprise: experiences and issues. In this section, we focused on citing some approaches and techniques presented in different papers with emphasis on their focus and limitations (Table 5). Paper [61] for example, proposed privacy preserving data mining techniques in Hadoop. Paper [67] introduced also an efficient and privacy-preserving cosine similarity computing protocol and paper [68] discussed how an existing approach “differential privacy” is suitable for big data. Big data has fundamentally changed the way organizations manage, analyze and leverage data in any industry. «BREACH REPORT 2016: Protected Health Information (PHI)» 2017. This fictitious data will improve the security but may result in problems amid analysis. Weakness in the key scheduling algorithm of RC4. Healthcare IT Program Of ce Intel Corporation, white paper. Paper [37] proposes also a cloud-oriented storage efficient dynamic access control scheme ciphertext based on the CP-ABE and a symmetric encryption algorithm (such as AES). & Khaloufi, H. Big healthcare data: preserving security and privacy. IBM Smarter Planet brief. MathSciNet  Therefore, it is important to gather data from trusted sources, preserve patient privacy (there must be no attempt to identify the individual patients in the database) and make sure that this phase is secured and protected. The t-closeness model (equal/hierarchical distance) [46, 50] extends the l-diversity model by treating the values of an attribute distinctly, taking into account the distribution of data values for that attribute. Washington: Executive Office of the President, President’s Council of Advisors on Science and Technology; 2014. Int J Uncertain Fuzziness Knowl Based Syst. In: Proceedings of the ICDE. 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