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big data security research papers

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big data security research papers

It focuses on protecting data from pernicious attacks and stealing data for profit. Shafer J, Rixner S, Cox AL. Such existing policies are unlikely to yield effective strategies for improving privacy, or to be scalable over time. Health Information at Risk: Successful Strategies for Healthcare Security and Privacy. The model proposed in [20] comprised of four interconnecting phases: data collection phase, data storage phase, data processing and analysis, and knowledge creation. On the bright side, the complexity of rendering relations of private records k-anonymous, while minimizing the amount of information that is not released and simultaneously ensure the anonymity of individuals up to a group of size k, and withhold a minimum amount of information to achieve this privacy level and this optimization problem is NP-hard [52]. Big Data security and privacy issues in healthcare—Harsh KupwadePatil, Ravi Seshadri. A scalable two-phase top-down specialization approach for data anonymization using systems, in MapReduce on cloud. Although various encryption algorithms have been developed and deployed relatively well (RSA, Rijndael, AES and RC6 [24, 26, 27], DES, 3DES, RC4 [28], IDEA, Blowfish …), the proper selection of suitable encryption algorithms to enforce secure storage remains a difficult problem. In data analysis module, correlations and association rules are determined to catch events. 2007. If want to make data L-diverse though sensitive attribute has not as much as different values, fictitious data to be inserted. In this paper, we discuss some interesting related works and present risks to the big health data security as well as some newer technologies to redress these risks. This fictitious data will improve the security but may result in problems amid analysis. Artemis. 2014;28:46–50. We have also presented privacy and security issues in each phase of big data lifecycle along with the advantages and flaws of existing technologies in the context of big healthcare data privacy and security. In this phase, supervised data mining techniques such as clustering, classification, and association can be employed for feature selection and predictive modeling. LeFevre K, Ramakrishnan R, DeWitt DJ. They were required to remove personally identifying information (PII) from the portal’s usage log repository but in a way that did not influence the utilization of big data tools to do analysis or the ability to re-identify a log entry in order to investigate unusual behavior. Managing and harnessing the analytical power of big data, however, is vital to the success of all healthcare organizations. It is then, a powerful and flexible mechanism to grant permissions for users. The IEEE Big Data 2019 (regular paper acceptance rate: 18.7%) was held in Los Angeles, CA, Dec 9-12, 2019 with close to 1200 registered participants from 54 countries. Nowadays, big data has become unique and preferred research areas in the field of computer science. Fluhrer S, Mantin I, Shamir A. In the report, it mentioned that Target Corporation sent baby care coupons to a teen-age girl unbeknown to her parents. It has more than 9 million members, estimated to manage large volumes of data ranging from 26.5 Petabytes to 44 Petabytes. As a result, de-identification is not sufficient for protecting big data privacy. The suggested solution includes storing and processing data in distributed sources through data correlation schemes. 2) Encryption Data encryption is an efficient means of preventing unauthorized access of sensitive data. This hospital succeeded to improve the outcomes for newborns prone to serious hospital infections. 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]. 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. DOI: 10.3386/w24253. Most cryptographic protocols include some form of endpoint authentication specifically to prevent MITM attacks. Data transmission among the clouds is also possible. There are six attributes along with five records in this data. Sophia Genetics. http://hir.uoit.ca/cms/?q=node/24. The information authentication can pose special problems, especially man-in-the-middle (MITM) attacks. By using this website, you agree to our 2016;3:25. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. Toward efficient and privacy-preserving computing in big data era. However, the problem is always imposed. Seamless integration of greatly diverse big healthcare data technologies can not only enable us to gain deeper insights into the clinical and organizational processes but also facilitate faster and safer throughput of patients and create greater efficiencies and help improve patient flow, safety, quality of care and the overall patient experience no matter how costly it is. IEEE Talks Big Data - Check out our new Q&A article series with big Data experts!. The big data revolution in healthcare, accelerating value and innovation. Audit means recording user activities of the healthcare system in chronological order, such as maintaining a log of every access to and modification of data. Therefore, a big data security event monitoring system model has been proposed which consists of four modules: data collection, integration, analysis, and interpretation [41]. 2001;13(6):1010–27. Challenges of privacy protection in big data analytics—Meiko Jensen-2013 IEEE international congress on big data. © 2017 The Authors. 1983. p. 602–607. an good writing essay practice my grandparents essay grandpa expository essay about friendship kpop example essay about culture healthy foodcase study in social work research … 2013. California Privacy Statement, big data research papers 2015. Other anonymization methods fall into the classes of adding noise to the data, swapping cells within columns and replacing groups of k records with k copies of a single representative. 2014. In: 2013 international conference on IT convergence and security (ICITCS), IEEE. on principles of database systems. Ton A, Saravanan M. Ericsson research. Moreover, when an application requires access to both the private and public data, the application itself also gets partitioned and runs in both the private and public clouds. IJBDI publishes high-quality original research papers in any aspect of big data with emphasis on 5Vs (volume, variety, velocity, veracity and value), big data science and foundations, big data infrastructure, big data management, big data intelligence, big data privacy/security and big data applications. The problem with this method is that it depends upon the range of sensitive attribute. The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. Complicating matters, the healthcare industry continues to be one of the most susceptible to publicly disclosed data breaches. Jin, Ginger Zhe. In the domain of mHealth, the World Health Organization has launched the project “Be Healthy Be mobile” in Senegal and under the mDiabetes initiative it supports countries to set up large-scale projects that use mobile technology, in particular text messaging and apps, to control, prevent and manage non-communicable diseases such as diabetes, cancer and heart disease [10]. This model is designed to address the phases of the big data lifecycle and correlate threats and attacks that face big data environment within these phases, while [21] address big data lifecycle from user role perspective: data provider, data collector, data miner, and decision maker. Role-based access control (RBAC) Role Engineering Process Version 3.0. In this paper we briefly discuss open issues, such as data protection from insider threat and how to reconcile security and privacy, and outline research directions. Institute for Health. http://gdhealth.com/globalassets/health-solutions/documents/brochures/securing-big-health-data_-white-paper_UK.pdf. 2014. 3) Data masking Masking replaces sensitive data elements with an unidentifiable value. In: Proceedings on second theory of cryptography conference. Forbes, Inc. 2012. Another example is the UNC Health Care (UNCHC), which is a non-profit integrated healthcare system in North Carolina that has implemented a new system allowing clinicians to rapidly access and analyze unstructured patient data using natural-language processing. In this paper, we discuss relevant concepts and approaches for Big Data security and privacy, and identify research challenges to be addressed to achieve comprehensive solutions to data security and privacy in the Big Data scenario. Wei L, Zhu H, Cao Z, Dong X, Jia W, Chen Y, Vasilakos AV. k-anonymity In this technique, the higher the value of k, the lower will be the probability of re-identification. On the other side, it is crucial to provide secure processing environment. 2012. https://developer.yahoo.com/hadoop/tutorial. She drafted also several manuscripts like “Big data security and privacy in healthcare: A Review” that was published in Procedia Computer Science journal. It is not truly an encryption technique so the original value cannot be returned from the masked value. Science Applications International Corporation (SAIC). 2002;10(5):557–70. ABSTRACT Providing security and privacy in big data analytics is significantly important along with providing quality of services (QoS) in big data networks. An attribute-based authorization policy framework with dynamic conflict resolution. t-Closeness: privacy beyond k-anonymity and L-diversity. These methods have a common problem of difficulty in anonymizing high dimensional data sets [32, 33]. In: 21st Americas conference on information systems. Sophia Genetics. Sweeney L. K-anonymity: a model for protecting privacy. Although security is vital for protecting data but it’s insufficient for addressing privacy. Privacy protections should be extended to non-US citizens as privacy is a worldwide value that should be reflected in how the federal government handles personally identifiable information from non-US citizens [16]. 2013. http://hadoop.apache.org/docs/r0.20.2/fair_scheduler.html. The authors declare that they have no competing interests. Int J Med Inform. 2013. Furthermore, CCW (The Chronic Conditions Data Warehouse) follows a formal information security lifecycle model, which consists of four core phases that serve to identify, assess, protect and monitor against patient data security threats. http://www.dlapiperdataprotection.com. 1 Introduction Issues around data confidentiality and privacy are under greater focus than ever before It serves vital functions within any organization: securing access to corporate networks, protecting the identities of users, and ensuring that the user is really who he is pretending to be. In: Proceedings on survey research methods. Analyzing Big Data. Google ScholarÂ. Many open research problems are available in big data and good solutions also been proposed by the researchers even though there is a need for development of many new techniques and algorithms for big data analysis in order to get optimal solutions. In fact, the size of these huge data sets is believed to be a continually growing target. Launched in 2013, in Costa Rica that has been officially selected as the first country, the initiative is working on an mCessation for tobacco program for smoking prevention and helping smokers quit, an mCervical cancer program in Zambia and has plans to roll out mHypertension and mWellness programs in other countries. Security and privacy for storage and computation in cloud computing. One of the most promising fields where big data can be applied to make a change is healthcare. In this paper, we have investigated the security and privacy challenges in big data, by discussing some existing approaches and techniques for achieving security and privacy in which healthcare organizations are likely to be highly beneficial. More than ever it is crucial that healthcare organizations manage and safeguard personal information and address their risks and legal responsibilities in relation to processing personal data, to address the growing thicket of applicable data protection legislation. Big Data In computer Cyber Security Systems IJCSNS. In fact, the focus of data miners in this phase is to use powerful data mining algorithms that can extract sensitive data. Int J Uncertain Fuzziness. The first book mentioning Big Data is a data mining book that came to fore in 1998 too by Weiss and Indrukya. Thus, data masking is one of the most popular approach to live data anonymization. b Horizontal partitioning. collaborative research on Big Data topics is underscored by the U.S. federal government’s recent $200 million funding initiative to support Big Data research.3 This document describes how the incorporation of Big Data is changing security analytics by providing new tools 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. Table 3 is a non-anonymized database consisting of the patient records of some fictitious hospital in Casablanca. These created knowledges are considered sensitive data, especially in a competitive environment. ABH carried out the cloud computing security studies, participated in many conferences and drafted multiple manuscripts as “Homomorphic encryption applied to secure storage and treatments of data in cloud” that was published in International Journal of Cloud Computing (IJCC), in 2016. The l-diversity model handles a few of the weaknesses in the k-anonymity model in which protected identities to the level of k-individuals is not equal to protecting the corresponding sensitive values that were generalized or suppressed. 2011. For instance, The Birth field has been generalized to the year (e.g. In this regards, healthcare organizations must implement security measures and approaches to protect their big data, associated hardware and software, and both clinical and administrative information from internal and external risks. On the other side, the collected data may contain sensitive information, which makes extremely important to take sufficient precautions during data transformation and storing. 2014;2:1149–76. It is also allowed only to an authorized person to read or write critical data. Washington: Executive Office of the President, President’s Council of Advisors on Science and Technology; 2014. 2001. Indeed, the concerns over the big healthcare data security and privacy are increased year-by-year. Table 4 has 2-anonymity with respect to the attributes ‘Birth’, ‘Sex’ and ‘ZIP Code’ since for any blend of these attributes found in any row of the table there are always no less than two rows with those exact attributes. Inf Sci. Knowledge creation phase Finally, the modeling phase comes up with new information and valued knowledges to be used by decision makers. Motivated thus, new information systems and approaches are needed to prevent breaches of sensitive information and other types of security incidents so as to make effective use of the big healthcare data. In Europe and exactly in Italy, the Italian medicines agency collects and analyzes a large amount of clinical data concerning expensive new medicines as part of a national profitability program. Security and privacy in big data are important issues. This process helps eliminate some vulnerabilities and mitigates others to a lower risk level. We mainly reviewed the privacy preservation methods that have been used recently in healthcare and discussed how encryption and anonymization methods have been used for health care data protection as well as presented their limitations. Zhou H, Wen Q. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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. While healthcare organizations store, maintain and transmit huge amounts of data to support the delivery of efficient and proper care, the downsides are the lack of technical support and minimal security. L-diversity It is a form of group based anonymization that is utilized to safeguard privacy in data sets by diminishing the granularity of data representation. De-identification is a traditional method to prohibit the disclosure of confidential information by rejecting any information that can identify the patient, either by the first method that requires the removal of specific identifiers of the patient or by the second statistical method where the patient verifies himself that enough identifiers are deleted. South Tyneside NHS Foundation Trust. Yazan A, Yong W, Raj Kumar N. Big data life cycle: threats and security model. 2013. Why Big Data Security Issues are Surfacing. Hill K. How target figured out a teen girl was pregnant before her father did. Groves P, Kayyali B, Knott D, Kuiken SV. Indeed, some mature security measures must be used to ensure that all data and information systems are protected from unauthorized access, disclosure, modification, duplication, diversion, destruction, loss, misuse or theft. Data protection overview (Morocco)—Florence Chafiol-Chaumont and Anne-Laure Falkman. mDiabetes is the first initiative to take advantage of the widespread mobile technology to reach millions of Senegalese people with health information and expand access to expertise and care. CiteScore: 7.2 ℹ CiteScore: 2019: 7.2 CiteScore measures the average citations received per peer-reviewed document published in this title. Vertical partitioning (1b) Map and reduce tasks are executed in the public cloud using public data as the input, shuffle intermediate data amongst them, and store the result in the public cloud. In: 3rd USENIX workshop on hot topics in cloud computing, HotCloud’11, Portland. 2005. 22nd international conference data engineering (ICDE). In January 2014, for example, the White House, led by President Obama’s Counselor John Podesta, undertook a 90-day review of big data and privacy. Data transformation phase Once the data is available, the first step is to filter and classify the data based on their structure and do any necessary transformations in order to perform meaningful analysis. In: The 10th international conference for internet technology and secured transactions (ICITST-2015). Big healthcare data has considerable potential to improve patient outcomes, predict outbreaks of epidemics, gain valuable insights, avoid preventable diseases, reduce the cost of … As noted above, big data analytics in healthcare carries many benefits, promises and presents great potential for transforming healthcare, yet it raises manifold barriers and challenges. Additionally, healthcare organizations found that a reactive, bottom-up, technology-centric approach to determining security and privacy requirements is not adequate to protect the organization and its patients [3]. Google ScholarÂ. Specification for the advanced encryption standards (AES). In: Proceedings of 2010 IEEE international symposium on performance analysis of systems & software (ISPASS), March 2010, White Plain, NY. They should be able to verify that their applications conform to privacy agreements and that sensitive information is kept private regardless of changes in applications and/or privacy regulations. Furthermore, the number of keys hold by each party should be minimized. Few traditional methods for privacy preserving in big data are described in brief here. Hadoop Tutorials. While this data is being hailed as the key to improving health outcomes, gain valuable insights and lowering costs, the security and privacy issues are so overwhelming that healthcare industry is unable to take full advantage of it with its current resources. 2013. p. 10–5. Nonetheless, an attacker can possibly get more external information assistance for de-identification in big data. Accordingly, it is critical that organizations implement healthcare data security solutions that will protect important assets while also satisfying healthcare compliance mandates. In 2016, CynergisTek has released the Redspin’s 7th annual breach report: Protected Health Information (PHI) [13] in which it has reported that hacking attacks on healthcare providers were increased 320% in 2016, and that 81% of records breached in 2016 resulted from hacking attacks specifically. Big data has fundamentally changed the way organizations manage, analyze and leverage data in any industry. 2014;7:56–62. To satisfy requirements of fine-grained access control yet security and privacy preserving, we suggest adopting technologies in conjunction with other security techniques, e.g. RBAC and ABAC have shown some limitations when they are used alone in medical system. A research methodology can help big data managers collect better and more intelligent information. [21]. At this stage, three likelihood metrics have been calculated to identify whether domain name, packet or flow is malicious. & Khaloufi, H. Big healthcare data: preserving security and privacy. The term Big Data appeared for the first time in 1998 in a Silicon Graphics (SGI) slide deck by John Mashey having the title Big Data and the Next Wave of Infra Stress. 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. statement and This lifecycle model is continually being improved with emphasis on constant attention and continual monitoring [21]. This paper focuses on challenges in big data and its available techniques. This model (Distinct, Entropy, Recursive) [46, 47, 51] is an extension of the k-anonymity which utilizes methods including generalization and suppression to reduce the granularity of data representation in a way that any given record maps onto at least k different records in the data. The Evolution of Big Data Security through Hadoop Incremental Security Model free download ABSTRACT: Data pours in millions of computers and millions of process every moment of every day so today is the era of Big Data where data … In: IEEE 35th international conference on distributed systems. 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. 2013. p. 437–42. There are two regular techniques for accomplishing k-anonymity for some value of k. The first one is Suppression: in this method, an asterisk ‘*’ could supplant certain values of the attributes. 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. Role-based access control (RBAC) [34] and attribute-based access control (ABAC) [35, 36] are the most popular models for EHR. Another example is the Artemis project, which is a newborns monitoring platform designed mercy to a collaboration between IBM and the Institute of Technology of Ontario. Encryption is useful to avoid exposure to breaches such as packet sniffing and theft of storage devices. [31] have presented p-sensitive anonymity that protects against both identity and attribute disclosure. Big Data Security – The Big Challenge Minit Arora, Dr Himanshu Bahuguna Abstract— In this paper we discuss the issues related to Big Data. Intel used Hadoop to analyze the anonymized data and acquire valuable results for the Human Factors analysts [59, 60]. 2014. p. 11–7. 2010. Big data analytics is used also in Canada, e.g. Consequently, quality of data should not be affected more by privacy preserving algorithms to get the appropriate result by researchers. It utilizes public clouds only for an organization’s non-sensitive data and computation classified as public, i.e., when the organization declares that there is no privacy and confidentiality risk in exporting the data and performing computation on it using public clouds, whereas for an organization’s sensitive, private data and computation, the model executes their private cloud. One more example is Kaiser Permanente medical network based in California. Transforming healthcare through big data, strategies for leveraging big data in the healthcare industry. 2014;258:371–86. 1998. Patil P, Raul R, Shroff R, Maurya M. Big data in healthcare. All or some of the values of a column may be replaced by ‘*’. Cloud data integrity checking with an identity-based auditing mechanism from RSA. Data protection regulations and laws in some of the countries along with salient features are listed in Table 2 below. 2014. Various measures have been proposed to quantify information loss caused by anonymization, but they do not reflect the actual usefulness of data [53, 54]. Podesta J, et al. © 2020 BioMed Central Ltd unless otherwise stated. Depending on the score obtained through this calculation, an alert occurs in detection system or process terminate by prevention system. Paper [70] proposed various privacy issues dealing with big data applications, while paper [71] proposed an anonymization algorithm to speed up anonymization of big data streams. Various technologies are in use to ensure security and privacy of big healthcare data. It uses a strategy of de-identifying data sets or masking personal identifiers such as name, social security number and suppressing or generalizing quasi-identifiers like date-of-birth and zip-codes. IEEE Trans Knowl Data Eng. This paper presents the current state-of-the-art research challenges and possible solutions on big data network Big Data and Database Security … Home » Research » Research Paper On Big Data Security. PubMed Google Scholar. These increased complexity and limits make the new models more difficult to interpret and their reliability less easy to assess, compared to previous models. We use cookies to help provide and enhance our service and tailor content and ads. 2017 DLA Piper. MathSciNet  In: Emerging intelligent data and web technologies (EIDWT), 2013 fourth international conference on. According to performance analysis with open source big data platforms on electronic payment activities of a company data, Spark and Shark produce fast and steady results than Hadoop, Hive and Pig [40]. It considers data sensitivity before a job’s execution and provides integration with safety. Then, we focus on the big data privacy issue in healthcare, by mentioning various laws and regulations established by different regulatory bodies and pointing out some feasible techniques used to ensure the patient’s privacy. Technical Report SRI-CSL-98-04, SRI Computer Science Laboratory. 2:25 PM. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. Big data security and privacy in healthcare: A Review. 2002;279–88. House W. Big data and privacy: a technological perspective. Future Gen Comput Syst. 2014;25(2):363–73. In: Proc. OECD. In: Proceedings of 22nd international conference on data engineering workshops. Part of Article  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). Map hybrid (1a) The map phase is executed in both the public and the private clouds while the reduce phase is executed in only one of the clouds. 40% of large breach incidents involved unauthorized access/disclosure. Sedayao J, Bhardwaj R. Making big data, privacy, and anonymization work together in the enterprise: experiences and issues. TLS and SSL encrypt the segments of network connections at the transport layer end-to-end. https://doi.org/10.1109/icitcs.2013.6717808. These are two optional security metrics to measure and ensure the safety of a healthcare system [38]. “Securing Big Health Data”©2015. American College of Medical Genetics and Genomics, Organisation for Economic Co-operation and Development, Rivest Shamir and Adleman encryption algorithm, ciphertext-policy attribute-based encryption, Health Insurance Portability and Accountability Act, Patient Safety and Quality Improvement Act, Health Information Technology for Economic and Clinical Health, Personal Information Protection and Electronic Documents Act. In addition, paper [72] suggested a novel framework to achieve privacy-preserving machine learning and paper [73] proposed methodology provides data confidentiality and secure data sharing. One can use SSL or TLS to authenticate the server using a mutually trusted certification authority. 2014. Springer Nature. Several prosperous initiatives have appeared to help the healthcare industry continually improve its ability to protect patient information. In surveys, the security experts grumble about the existing tools and recommend for special tools and methods for big data security analysis. In: Proceedings of the 9th symposium on identity and trust on the internet. J AHIMA. 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]. The paper discusses research challenges and directions concerning data confide More broadly, data filtering, enrichment and transformation are needed to improve the quality of the data ahead of analytics or modeling phase and remove or appropriately deal with noise, outliers, missing values, duplicate data instances, etc. 3,620,000 breached patient records in the year’s single largest incident. Mehmood A, Natgunanathan I, Xiang Y, Hua G, Guo S. Protection of big data privacy. of the ACM Symp. Change is the new norm for the global healthcare sector. Cloud-based storage has facilitated data mining and collection. CSE ECE EEE . Accessed 21 May 2016. It is in this context that this paper aims to present the state-of-the-art security and privacy issues in big data as applied to healthcare industry and discuss some available data privacy, data security, users’ accessing mechanisms and strategies. IEEE Trans Parallel Distrib. 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. .http://www.ericsson.com/research-blog/data-knowledge/big-data-privacy-preservation/2015. This investigation of the quality of anonymization used k-anonymity based metrics. Hagner M. Security infrastructure and national patent summary. Cookies policy. In: International conference on logistics engineering, management and computer science (LEMCS 2014). c Vertical partitioning. Big Data is the vouluminous amount of data with variety in its nature along with the complexity of handling such data. Horizontal partitioning (1c) The map phase is executed only in public clouds, while the reduce phase is executed in a private cloud. 2006. p. 94. Big data security and privacy are considered huge obstacles for researchers in this field. IEEE Trans Knowl Data Eng. Big data security life cycle in healthcare. Yang C, Lin W, Liu M. A novel triple encryption scheme for hadoop-based cloud data security. Authors prove consent of publication for this research. In this paper, we firstly reviewed the enormous benefits and challenges of security … Whereas implementing security measures remains a complex process, the stakes are continually raised as the ways to defeat security controls become more sophisticated. 2010. It is the process of either encrypting or removing personally identifiable information from data sets, so that the people whom the data describe remain anonymous. To meet the significant benefits of Cloud storage [57], Intel created an open architecture for anonymization [56] that allowed a variety of tools to be utilized for both de-identifying and re-identifying web log records. © 2017 The Author(s). Journal of Big Data In the anonymized Table 4, replaced each of the values in the ‘Name’ attribute and all the values in the ‘Religion’ attribute by a ‘*’. Such was the case with South Tyneside NHS Foundation Trust, a provider of acute and community health services in northeast England that understands the importance of providing high quality, safe and compassionate care for the patients at all times, but needs a better understanding of how its hospitals operate to improve resource allocation and wait times and to ensure that any issues are identified early and acted upon [4]. At the same time, it learned that anonymization needs to be more than simply masking or generalizing certain fields—anonymized datasets need to be carefully analyzed to determine whether they are vulnerable to attack. Somu N, Gangaa A, Sriram VS. Authentication service in hadoop using one time pad. Zhang R, Liu L. Security models and requirements for healthcare application clouds. David Houlding, MSc, CISSP. Such a huge size of data upsurge the importance of security analytics in big data. 2012;83:38–42. It supported the acquisition and the storage of patients’ physiological data and clinical information system data for the objective of online and real time analysis, retrospective analysis, and data mining [8]. [18] argue that security in big data refers to three matters: data security, access control, and information security. In fact, UNCHC has accessed and analyzed huge quantities of unstructured content contained in patient medical records to extract insights and predictors of readmission risk for timely intervention, providing safer care for high-risk patients and reducing re-admissions [5]. In Morocco for instance, PharmaProcess in Casablanca, ImmCell, The Al Azhar Oncology Center and The Riad Biology Center in Rabat are some medical institutions at the forefront of innovation that have started integrating Sophia to speed and analyze genomic data to identify disease-causing mutations in patients’ genomic profiles, and decide on the most effective care. In this paper, we have briefly discussed some successful related work across the world. The concepts of k-anonymity [46,47,48], l-diversity [47, 49, 50] and t-closeness [46, 50] have been introduced to enhance this traditional technique. It allows medical information to follow the patient hosted in one doctor office or only in a hospital system [6]. 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. Accordingly, security compliance and verification are a primary objective in this phase. Truta TM, Vinay B. Privacy protection: p-sensitive k-anonymity property. However, deciding on the allowable uses of data while preserving security and patient’s right to privacy is a difficult task. Data-driven healthcare innovation, management and policy, DELSA/HEA(2013)13. Moreover in the United States, the Indiana Health Information Exchange, which is a non-profit organization, provides a secure and robust technology network of health information linking more than 90 hospitals, community health clinics, rehabilitation centers and other healthcare providers in Indiana. House W. FACT SHEET: big data and privacy working group review. Mohan A, Blough DM. The main difficulty with this technique involves combining anonymization, privacy protection, and big data techniques [56] to analyze usage data while protecting the identities. IBM Smarter Planet brief. 2006. p. 24. So, to elaborate this, the paper is divided into following sections. A number of solutions have been proposed to address the security and access control concerns. IEEE Netw. 5) Monitoring and auditing Security monitoring is gathering and investigating network events to catch the intrusions. The ZIP Code field has been also generalized to indicate the wider area (Casablanca). Jung K, Park S, Park S. Hiding a needle in a haystack: privacy preserving Apriori algorithm in MapReduce framework PSBD’14, Shanghai. The invasion of patient privacy is considered as a growing concern in the domain of big data analytics due to the emergence of advanced persistent threats and targeted attacks against information systems. Paris: OECD; 2013. CiteScore values are based on citation counts in a range of four years (e.g. Publications. Department of Computer Science Laboratory LAMAPI and LAROSERI, Chouaib Doukkali University, El Jadida, Morocco, Karim Abouelmehdi, Abderrahim Beni-Hessane & Hayat Khaloufi, You can also search for this author in 2015. https://doi.org/10.1186/s40537-017-0110-7, DOI: https://doi.org/10.1186/s40537-017-0110-7. 2013. In this context, as our future direction, perspectives consist in achieving effective solutions in privacy and security in the era of big healthcare data. agement, have increased the exposure of data and made security more difficult. To ensure a secure and trustworthy big data environment, it is essential to identify the limitations of existing solutions and envision directions for future research. 2013. In: Proc. Programs that provide education leading to privacy expertise are essential and need encouragement. A significant benefit of this technique is that the cost of securing a big data deployment is reduced. In: Proceedings of the ACM SIGKDD. Big healthcare data has considerable potential to improve patient outcomes, predict outbreaks of epidemics, gain valuable insights, avoid preventable diseases, reduce the cost of healthcare delivery and improve the quality of life in general. CynergisTek, Redspin. After exploring the tradeoffs of correcting these vulnerabilities, they found that User Agent information strongly correlates to individual users. 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. Karim Abouelmehdi. Mobile phones help people with diabetes to manage fasting and feasting during Ramadan. As a result, organizations are in challenge to address these different complementary and critical issues. For 50 years and counting, ISACA ® has been helping information systems governance, control, risk, security, audit/assurance and business and cybersecurity professionals, and enterprises succeed. Published by Elsevier B.V. https://doi.org/10.1016/j.procs.2017.08.292. Some special issues of network security monitoring on big data environments. Attribute relationship evaluation methodology for big data security. The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data. Machanavajjhala A, Gehrke J, Kifer D, Venkitasubramaniam M. L-diversity: privacy beyond k-anonymity. Further, there also exist several ensembles of learning techniques that improve accuracy and robustness of the final model. Samrati P. Protecting respondents identities in microdata release. Duygu ST, Ramazan T, Seref S. A survey on security and privacy issues in big data. Article  In fact, attackers can use data mining methods and procedures to find out sensitive data and release it to the public and thus data breach happens. Ko SY, Jeon K, Morales R. The HybrEx model for confidentiality and privacy in cloud computing. 2016;62:85–91. Federal Information Processing Standards Publication 197. 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. The hadoop distributed filesystem: balancing portability and performance. Intel Human Factors Engineering team needed to protect Intel employees’ privacy using web page access logs and big data tools to enhance convenience of Intel’s heavily used internal web portal. Kim S-H, Kim N-U, Chung T-M. Executive Office of the President. Big data processing systems suitable for handling a diversity of data types and applications are the key to supporting scientific research of big data. Oracle big data for the enterprise. Information security in big data: privacy and data mining. Whereas the potential opportunities offered for big data in the healthcare arena are unlimited (e.g. 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. From a security perspective, securing big health data technology is a necessary requirement from the first phase of the lifecycle. The paper introduces a research agenda for security and privacy in big data. Lu R, Zhu H, Liu X, Liu JK, Shao J. In a healthcare system, both healthcare information offered by providers and identities of consumers should be verified at the entry of every access. the infant hospital of Toronto. security in big data research papers ES SOFTWARE SALES. Big Data and Security - written by Loshima Lohi, Greeshma K V published on 2018/05/19 download full article with reference data and citations Skip to content International Journal of Engineering Research … As well, privacy methods need to be enhanced. J Big Data. Additional findings of this report include: 325 large breaches of PHI, compromising 16,612,985 individual patient records. k-anonymous data can still be helpless against attacks like unsorted matching attack, temporal attack, and complementary release attack [50, 51]. Businesses that utilize big data and analytics well, particularly with the aid of research methodology, find their profitability and productivity rates are five to six percent higher than their competition. In this paper, we are using a big data analysis tool, which is known as apache spark. In this paper, we suggest a model that combines the phases presented in [20] and phases mentioned in [21], in order to provide encompass policies and mechanisms that ensure addressing threats and attacks in each step of big data life cycle. Priyank J, Manasi G, Nilay K. Big data privacy: a technological perspective and review. http://www.sophiagenetics.com/news/media-mix/details/news/african-hospitals-adopt-sophia-artificial-intelligence-to-trigger-continent-wide-healthcare-leapfrogging-movement.html. In: Tromso telemedicine and eHealth conference. Publications - See the list of various IEEE publications related to big data and analytics here. Data modeling phase Once the data has been collected, transformed and stored in secured storage solutions, the data processing analysis is performed to generate useful knowledge. [20] suggested a big data security lifecycle model extended from Xu et al. Intrusion detection and prevention procedures on the whole network traffic is quite tricky. Privacy Figure 1 presents the main elements in big data lifecycle in healthcare. 2007. 2004. 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. Samarati P. Protecting respondent’s privacy in microdata release. http://www.ihie.org/. After Europe, Canada, Australia, Russia, and Latin America, Sophia Genetics [11], global leader in data-driven medicine, announced at the recent 2017 Annual Meeting of the American College of Medical Genetics and Genomics (ACMG) that its artificial intelligence has been adopted by African hospitals to advance patient care across the continent. One of the most promising fields where big data can be applied to make a change is healthcare. J Rapid Open Access Publ. For data of huge volume, complex structure, and sparse value, its processing is confronted by high computational complexity, long duty cycle, and real-time requirements. Xu K, Yue H, Guo Y, Fang Y. Privacy-preserving machine learning algorithms for big data systems. Cite this article. 2012. http://www.oracle.com/ca-en/technoloqies/biq-doto. Moreover, paper [69] suggested a scalable approach to anonymize large-scale data sets. Policy concerning privacy protection should be addressing the purpose rather than prescribing the mechanism. 2009;78:141–60. The second method is Generalization: In this method, individual values of attributes are replaced with a broader category. Meyerson A, Williams R. On the complexity of optimal k-anonymity. Big data: seizing opportunities, preserving values. 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]. Sectorial healthcare strategy 2012–2016-Moroccan healthcare ministry. Among these manuscripts, we find: “Assessing Cost and Response Time of a Web Application Hosted in a Cloud Environment” paper that was published by Springer in 2016. Yazan et al. In terms of security and privacy perspective, Kim et al. National Bureau of Economic Research working paper, 2018. Several versions of the protocols are in widespread use in applications like web browsing, electronic mail, Internet faxing, instant messaging and voice-over-IP (VoIP). Big data network security systems should be find abnormalities quickly and identify correct alerts from heterogeneous data. the value ‘21/11/1972’ of the attribute ‘Birth’ may be supplanted by the year ‘1972’). The author forwards his heartfelt gratitude to two anonymous reviewers for their careful reading of the manuscript and their helpful comments that improve the presentation of this work. Additionally, we state open research issues in big data. This incident impels analytics and developers to consider privacy in big data. Therefore, we move towards L-diversity strategy of data anonymization. [7]. In fact, digitization of health and patient data is undergoing a dramatic and fundamental shift in the clinical, operating and business models and generally in the world of economy for the foreseeable future. Sections 2 deals with challenges that arise during fine tuning of big data. Yong Yu, et al. This is a case study of anonymization implementation in an enterprise, describing requirements, implementation, and experiences encountered when utilizing anonymization to protect privacy in enterprise data analyzed using big data techniques. volume 5, Article number: 1 (2018) «Product & Technology Overview» 2014. In: Proceedings of the ICDE. 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. d Hybrid. UNC Health Care relies on analytics to better manage medical data and improve patient care. Weakness in the key scheduling algorithm of RC4. Li N, et al. To address this problem, a security monitoring architecture has been developed via analyzing DNS traffic, IP flow records, HTTP traffic and honeypot data [39]. 2015. In: IEEE 3rd international conference on cloud computing. Each “quasi-identifier” tuple occurs in at least k records for a dataset with k-anonymity. As secure data is migrated from a secure source into the platform, masking reduces the need for applying additional security controls on that data while it resides in the platform. Its solutions protect and maintain ownership of data throughout its lifecycle—from the data center to the endpoint (including mobile devices used by physicians, clinicians, and administrators) and into the cloud. 2002;10:571–88. Xu L, Jiang C, Wang J, Yuan J, Ren Y. Terms and Conditions, Summary: This paper looks at the risks big data poses to consumer privacy. Therefore, the process of data mining and the network components in general, must be configured and protected against data mining based attacks and any security breach that may happen, as well as make sure that only authorized staff work in this phase. p. 122–33. Abstract: While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. 2013. WHO. 2004. encryption, and access control methods. Privacy of medical data is then an important factor which must be seriously considered. The author describes the causes and consequences of data breaches and the ways in which technological tools can be used for data … Privacy and Big Data—Terence Craig & Mary E. Ludloff. Liu L, Lin J. Abouelmehdi, K., Beni-Hessane, A. Another important research direction is to address the privacy and the security issues in analyzing big data. Also with the rapid development of IoT, the greater the quantity, the lower the quality. All these techniques and approaches have shown some limitations. https://doi.org/10.1109/ACCESS.2014.2362522. Additionally, Bull Eye algorithm can be used for monitoring all sensitive information in 360°. 2006. p. 25. Most widely used technologies are: 1) Authentication Authentication is the act of establishing or confirming claims made by or about the subject are true and authentic. General Dynamics Health Solutions white paper UK. In: ACM proceedings of the 2014 international conference on big data science and computing, article 1. Sweeney L. Achieving k-anonymity privacy protection using generalization and suppression. Big data is slowly but surely gaining its popularity in healthcare. Although these techniques are used traditionally to ensure the patient’s privacy [43,44,45], their demerits led to the advent of newer methods. 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. Allows medical information to follow the patient hosted big data security research papers one doctor office only! 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