Big data security and privacy in healthcare: A Review

Karim, Alamgir; Beni‐Hssane, Abderrahim; Khaloufi, Hayat; Saadi, Mostafa

doi:10.1016/j.procs.2017.08.292

Cited by 194 publications

(123 citation statements)

References 22 publications

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“…On the other hand, the emergence of large amounts of data in several other scenarios has raised the concern for the privacy of personal data, and much progress has been made in this setting based on the progressing of personal data protection laws throughout all the world [174]. Whereas this represents a need, it turns out that health data represent an indubitably personal information, and so the need for aggregation in order to provide large-scale population solutions from a technical viewpoint needs to conciliate with the consent required from every individual to provide our personal healthcare data, while sometimes confronting interests from organizations, companies and patients, and many other hard-to-manage aspects [175,176].…”

Section: The Present and Near Future In Large Data Healthcarementioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

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Section: The Present and Near Future In Large Data Healthcarementioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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“…They have been discussed several examples of related successful works throughout the globe. Issues regarding Privacy and securities are also presented in every single phase of the Big Data Life Cycle, across with their merits and demerits of current privacy and security policies with respect to the Big Health Data Industry [8]. The security issues in Big Data Platforms can be classified into various levels, namely authentication, data level, network level and general problems.…”

Section: Related Workmentioning

confidence: 99%

Big Data Security Analytics in Clinical Data using Cryptographic Algorithms

Gracious¹,

Nandanan²,

Dagma³

et al. 2019

IJRTE

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With increasing apprehension and concerns of cloud computing and information security, awareness about the use of security algorithms in data systems and processes is indispensable. The objective of this paper is to examine a set of cryptographic algorithms for cloud platforms to secure clinical data. The main benefits of cloud storage are scalability, resilience, cost efficiency, high reliability and easy access to your knowledge anyplace, anytime. Because of these benefits every organization is moving its data to the cloud. So there is a necessity to protect that data against unauthorized access, modification or denial of access etc. we analyzed the use of cryptography in securing clinical data sets using evaluation parameters such as computing memory, encryption time and decryption time.

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“…Healthcare data is considered as highly sensitive and requires secure and safer means to protect it. Thus, the storage, sharing and managing medical data should be done in secure ways [6], [7]. There are various mechanisms already proposed to address such issues, for example, numerous authentication schemes are presented in [8], [9], [10] in order to fulfill the need of secure and efficient medical data accessibility, manageability and other key security requirements.…”

Section: Related Workmentioning

confidence: 99%

Secure and Efficient Data Accessibility in Blockchain Based Healthcare Systems

Ramani

Kumar

Bracken

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

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The healthcare industry is constantly reforming and adopting new shapes with respect to the technological evolutions and transitions. One of the crucial requirements in the current smart healthcare systems is the protection of patients sensitive data against the potential adversaries. Therefore, it is vital to have secure data access mechanisms that can ensure only authorized entities can access the patients medical information. Hence, this paper considers blockchain technology as a distributed approach protect the data in healthcare systems. This research proposes a blockchain based secure and efficient data accessibility mechanism for the patient and the doctor in a given healthcare system. Proposed system able to protect the privacy of the patients as well. The security analysis of our scheme shows that it can resist to well-known attacks along with maintaining the integrity of the system. Moreover, an Ethereum based implementation has used to verify the feasibility of our proposed system.

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Big data security and privacy in healthcare: A Review

Cited by 194 publications

References 22 publications

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Big Data Security Analytics in Clinical Data using Cryptographic Algorithms

Secure and Efficient Data Accessibility in Blockchain Based Healthcare Systems

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