A Comprehensive Survey on Data Utility and Privacy: Taking Indian Healthcare System as a Potential Case Study

Churi, Prathamesh; Pawar, Ambika; Moreno-Guerrero, Antonio-José

doi:10.3390/inventions6030045

Cited by 21 publications

(8 citation statements)

References 77 publications

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“…Implementing Blockchain network is one of the most successful methods since altering the hashes of the information affects the whole chain. e technological developments are combined with medical information in E-healthcare, encompassing constant monitoring and transmission of health-related issues from the patient-centric environment to appropriate network operators [1]. Because of its incorporation in IoT (Internet of things), it is believed that, in the coming years, medical services and applications will produce hundreds of billions of dollars in revenue sector.…”

Section: Introductionmentioning

confidence: 99%

Secure Model for IoT Healthcare System under Encrypted Blockchain Framework

Jeet

Hoque

Dugbakie

2022

Security and Communication Networks

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With the advancements in the new technologies like IoT, the healthcare sector is also growing rapidly. The core concept of introducing IoT in the healthcare centers is to make it remotely accessible, so that communication between doctor and patient can be easy, and diagnosis of any disease can be remotely done through the Internet in case of emergency. The major objective of this study is to develop a security framework for the healthcare industry that can encrypt secret data on cloud servers using block-based data encoding. Since any information that is available on the Internet is vulnerable to various attacks, and the medical records of the patients carry sensitive information that must not be accessible to any unauthorized or unauthentic person, in this context, the concept of Blockchain (BC) technology was introduced in the healthcare systems. In the past few years, a number of BC based healthcare models were proposed in the system; however, these systems are decentralized in nature, which is a feature of Blockchain technology, but in certain applications, especially in healthcare, this feature gave rise to a problem termed as disease overlapping when the count of chains gets increased. To solve this issue, a BC based framework is used in this work that has a feature of creating a separate block in the chain every time whenever any alteration in information about patient’s health, any allergies, new symptoms, medications, etc. is updated. In addition to this, securing the sensitive information of patients is taken into consideration by developing an improved and enhanced multilayer Blockchain based IoT data security approach that not only protects the data, but also builds the trust between patients/user and healthcare service providers. The main focused area of this research article is to propose a security framework for healthcare domain that can encrypt the confidential data over cloud server under block-based data encoding. In the proposed approach, 128-bit AES key is generated in order to support the SHA-256 hashing algorithm. Individual user’s data is subdivided to blocks and encrypted to secure it from unauthorized access. An interface-based system is designed to present the real-world usage of the proposed model in healthcare application. Finally, the effectiveness of the proposed approach is validated in the MATLAB software in terms of MAE, RMSE, MSE, encryption time, and decryption time. The RSA and DSA are the most trending encryption algorithms. These algorithms are crucial components of Blockchain technology, implemented in the proposed work, and later on, their performances are also compared with each other to check their effectiveness. The simulation results demonstrated that the proposed encrypting algorithm surpasses the other encryption approaches such as RSA and DSA in all factors to prove its supremacy. The experimental results show that the encryption and decryption time are lowest in the proposed framework; therefore, its performance is more effective and robust. It is also more effective and resilient.

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Section: Introductionmentioning

confidence: 99%

Secure Model for IoT Healthcare System under Encrypted Blockchain Framework

Jeet

Hoque

Dugbakie

2022

Security and Communication Networks

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“…A young, skilled workforce is advantageous to the country's healthcare system, which needs attention from stakeholders to use these professionals successfully. [4][5][6][7]…”

Section: Methodsmentioning

confidence: 99%

PAs in India

Muraleedharan,

Chandak,

Chatterjee

et al. 2024

Jaapa

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Background: This report is based on a 2023 nationwide survey and literature review of physician associates/assistants (PAs) in India. The National Commission for Allied and Healthcare Professions Act, 2021, which is being implemented in India, included guidelines for PA licensing, creating a unified national curriculum, and preventing malpractice. Methods: Using a purposive sampling strategy, we surveyed 536 PAs in India between February 10 and April 30, 2023. The survey collected data on demographics, academic qualifications, specialties, salaries, job satisfaction, and future outlooks through an online questionnaire. Results: The survey showed that most PAs are below age 30 years, and most graduated PAs were involved in clinical practice in 2023. PAs work in almost all clinical specialties; most practice in private hospitals. States in southern India continued to dominate educating and employing PAs, with a nationwide representation of more than 14 states. More than 80% of practicing PAs are satisfied with their profession; however, the majority are unsatisfied with their wages. Conclusions: This first formal survey on India's PAs illustrates the challenges of PA professional practice and standardization of education and licensure in the Indian healthcare system.

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“…Thus, data anonymization is an iterative process where after applying each privacy model and consequent assessment of the re-identification risk, the resultant anonymized data must be evaluated according to an utility model. The entire process should be repeated, until a reasonable balance is reached between minimizing the risk of re-identification and maintaining the maximum usefulness of the data 6 , 7 . Thus, the entire process consists of three main steps: (1) applying a privacy model; (2) quantifying the risk of disclosure or re-identification, and (3) assessing data utility.…”

Section: Introductionmentioning

confidence: 99%

Utility-driven assessment of anonymized data via clustering

2022

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In this study, clustering is conceived as an auxiliary tool to identify groups of special interest. This approach was applied to a real dataset concerning an entire Portuguese cohort of higher education Law students. Several anonymized clustering scenarios were compared against the original cluster solution. The clustering techniques were explored as data utility models in the context of data anonymization, using k-anonymity and (ε, δ)-differential as privacy models. The purpose was to assess anonymized data utility by standard metrics, by the characteristics of the groups obtained, and the relative risk (a relevant metric in social sciences research). For a matter of self-containment, we present an overview of anonymization and clustering methods. We used a partitional clustering algorithm and analyzed several clustering validity indices to understand to what extent the data structure is preserved, or not, after data anonymization. The results suggest that for low dimensionality/cardinality datasets the anonymization procedure easily jeopardizes the clustering endeavor. In addition, there is evidence that relevant field-of-study estimates obtained from anonymized data are biased.

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A Comprehensive Survey on Data Utility and Privacy: Taking Indian Healthcare System as a Potential Case Study

Cited by 21 publications

References 77 publications

Secure Model for IoT Healthcare System under Encrypted Blockchain Framework

Secure Model for IoT Healthcare System under Encrypted Blockchain Framework

PAs in India

Utility-driven assessment of anonymized data via clustering

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