PRMS: Design and Development of Patients’ E-Healthcare Records Management System for Privacy Preservation in Third Party Cloud Platforms

Zala, Kirtirajsinh; Thakkar, Hiren Kumar; Jadeja, Rajendrasinh; Singh, Priyanka; Kotecha, Ketan; Shukla, Madhu

doi:10.1109/access.2022.3198094

Cited by 34 publications

(5 citation statements)

References 44 publications

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“…However, data security in the cloud is a significant concern. Block chain technology can provide CSPs with robust security for essential information by encrypting data using private and public platforms [10].…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Deep Feature Neural Classification Algorithm for Efficient Cardiac Disease Early Risk Identification

Jagadeesan Jayaraman,

Damarapati Leela Rani,

Aruliah Selvarani

et al. 2023

ARASET

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Cloud environments can store data for the medical field with a large-scale system. Importantly, early detection of diseases using clinical data analysis is essential in medicine. Cardiovascular disease has emerged as the primary cause of sudden high-risk health-related fatalities in recent years. Analyzing time series data has become more complex and non-linear, making predictive risk analysis through feature analysis a crucial aspect of data analysis. Feature measures that are not feasible can hurt prediction accuracy and may result in misclassification. To overcome this problem, an improved clinical data analysis model using Adaptive Deep Feature Neural Classification (DFNC) method for cardiac data prediction can identify early risk stages. Time series data can be standardized from the CVD-DS dataset initially selected using a preprocessing model. Correlation with a subset of margins can be obtained using the Cardiac Deficiency Prediction Rate (CDPR). Then, the Support Frequent Scaling Feature Selection (SFSFS) model can extract the feature components based on the CDPR weights. The required features can be obtained using a deep neural classifier based on logistic neurons. The classifier is based on a Recurrent Neural Network (RNN) that deliberates each class category's feature values and Cardia Influence Rate (CIR). Classification, precision and recall can be implemented in the proposed method to provide high prediction accuracy. Additionally, early cardiovascular disease risk prediction accuracy may support early diagnosis management.

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Section: Related Workmentioning

confidence: 99%

An Adaptive Deep Feature Neural Classification Algorithm for Efficient Cardiac Disease Early Risk Identification

Jagadeesan Jayaraman,

Damarapati Leela Rani,

Aruliah Selvarani

et al. 2023

ARASET

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“…The protection of patient privacy is one of the most important and significant challenges in the healthcare sector. Kirtirajsinh et al [24] proposed a patient E records management system to enhance the privacy of sensitive patient health records using a third-party cloud. Xiaoyun et al [25] concentrated on privacy and secure access to patient-sensitive information using querying methods and integration of various sources of data including cryptographybased solutions.…”

Section: B Review Of Privacy Preservation System In Health Recordsmentioning

confidence: 99%

Multi-Level Security in Healthcare by Integrating Lattice-Based Access Control and Blockchain- Based Smart Contracts System

Haritha,

Anitha

2023

IEEE Access

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Access control to patient information has become increasingly important in healthcare systems. It is vital to enhance the security of healthcare systems to avoid data loss despite the various security policies imposed by healthcare management. The issue needs to be resolved with a comprehensive secure framework, which allows users to access data according to their level of confidentiality. This article presents a solution by imposing multi-level security in e-health systems by integrating the Lattice-Based Access Control (LBAC) model and blockchain-based smart contract mechanisms. These mechanisms provide security levels in compliance with data access restrictions among users and resources while maintaining compliance security levels. By using LBAC, you can provide multilevel protection for access control restrictions, whereas smart contracts are used to ensure the transaction process in a decentralized system via an agreement between the parties. A smart contract validates every user and performs the authentication process in the envisioned model, which uses the Ethereum Virtual Machine (EVM). In the blockchain network, the patient's ehealth details are accessed and stored as immutable blocks. Comparing the proposed scheme with existing benchmarking methods reveals that the proposed scheme preserves privacy, maintains transparency, provides an authentication process, maintains data integrity, and provides multilevel access control security. The proposed model performs better than other existing models. As a result, lattice-based access control enhances the security of e-health records.INDEX TERMS Blockchain, e-health Records, Multi-level security, Lattice-based access control, Smart Contracts.

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“…Economic empowerment platforms often integrate with third-party services, APIs, or external platforms. These integrations introduce additional security risks if not thoroughly vetted [101]- [105]. Due diligence should be exercised when selecting and integrating third-party services to ensure that they meet security standards and do not compromise the overall platform's security posture.…”

Section: Third-party Integration Risksmentioning

confidence: 99%

Navigating the path to economic empowerment: overcoming challenges in digital platforms for economic empowerment

Otuma O. Andako

2023

World J. Adv. Res. Rev.

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This paper presents a comprehensive survey of the challenges faced by economic empowerment digital platforms. As these online platforms revolutionize the economic landscape, they encounter various hurdles that hinder their potential for fostering financial inclusion and empowerment. Through an extensive analysis of existing literature and empirical studies, this survey identifies and categorizes the key challenges faced by these platforms. The survey reveals that regulatory frameworks pose significant obstacles to economic empowerment digital platforms. Issues such as unclear or outdated regulations, varying legal requirements across jurisdictions, and the need for compliance with consumer protection and data privacy laws hinder platform operations and expansion. Moreover, the absence of standardized regulations often leads to legal uncertainties and challenges in ensuring fair competition. Furthermore, the survey highlights the persistent challenge of trust and reputation management within economic empowerment digital platforms. As these platforms rely heavily on user-generated content and interactions, establishing trust among users becomes critical. Issues such as fraudulent activities, identity verification, and dispute resolution mechanisms emerge as key challenges that platforms must address to maintain user trust and credibility. The survey also addresses the technological challenges faced by economic empowerment digital platforms. Issues such as cybersecurity threats, data breaches, and privacy concerns pose significant risks to both platform operators and users. Platforms must invest in robust security measures and adopt innovative technologies to mitigate these challenges and safeguard user information and transactions. Additionally, the survey examines challenges related to financial inclusion and accessibility. Despite the potential for economic empowerment, certain groups, such as individuals from low-income communities or those lacking digital literacy, may face barriers in accessing and utilizing these platforms. Bridging the digital divide and ensuring equal opportunities for all individuals to participate and benefit from these platforms remains an ongoing challenge. Finally, the survey highlights the need for collaboration and cooperation among stakeholders. Platform operators, policymakers, regulatory bodies, and user communities must engage in constructive dialogue to address the challenges faced by economic empowerment digital platforms effectively. Collaborative efforts can lead to the development of regulatory frameworks, industry standards, and best practices that foster a conducive environment for these platforms to thrive.

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PRMS: Design and Development of Patients’ E-Healthcare Records Management System for Privacy Preservation in Third Party Cloud Platforms

Cited by 34 publications

References 44 publications

An Adaptive Deep Feature Neural Classification Algorithm for Efficient Cardiac Disease Early Risk Identification

An Adaptive Deep Feature Neural Classification Algorithm for Efficient Cardiac Disease Early Risk Identification

Multi-Level Security in Healthcare by Integrating Lattice-Based Access Control and Blockchain- Based Smart Contracts System

Navigating the path to economic empowerment: overcoming challenges in digital platforms for economic empowerment

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