A Secure Framework for Privacy-Preserving Analytics in Healthcare Records Using Zero-Knowledge Proofs and Blockchain in Multi-Tenant Cloud Environments
In the realm of healthcare analytics, preserving the privacy of sensitive data while enabling valuable insights poses a significant challenge, particularly given the increasing prevalence of data breaches and the sensitivity of personal health information. This paper presents a secure framework that...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10772106/ |
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| author | S. Bharath Babu K. R. Jothi |
| author_facet | S. Bharath Babu K. R. Jothi |
| author_sort | S. Bharath Babu |
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| description | In the realm of healthcare analytics, preserving the privacy of sensitive data while enabling valuable insights poses a significant challenge, particularly given the increasing prevalence of data breaches and the sensitivity of personal health information. This paper presents a secure framework that addresses these concerns by integrating privacy-preserving parameters, zero-knowledge proofs (zk-SNARKs), blockchain technology, and a multi-tenant cloud environment. Through advanced cryptographic techniques, specifically zk-SNARKs, the framework ensures that healthcare records remain protected during analytics computations, without exposing raw data. The privacy-preserving analytics engine utilizes anonymized healthcare records and generates zk-SNARKs to validate computations. These proofs, integrated into a blockchain network, create a tamper-proof, transparent ledger that ensures secure healthcare transactions. This approach is critical in scenarios such as telemedicine, where secure data sharing and computation are paramount. By demonstrating its application in a telemedicine app, the framework highlights its practical significance in balancing data utility and privacy in healthcare analytics, providing a scalable and secure solution to a pressing problem. |
| format | Article |
| id | doaj-art-46e48973fc0e4d07bb30dc5f21964d4f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-46e48973fc0e4d07bb30dc5f21964d4f2025-01-21T00:01:54ZengIEEEIEEE Access2169-35362025-01-01138439845510.1109/ACCESS.2024.350945710772106A Secure Framework for Privacy-Preserving Analytics in Healthcare Records Using Zero-Knowledge Proofs and Blockchain in Multi-Tenant Cloud EnvironmentsS. Bharath Babu0https://orcid.org/0000-0003-0037-8743K. R. Jothi1https://orcid.org/0000-0003-0106-2804School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaIn the realm of healthcare analytics, preserving the privacy of sensitive data while enabling valuable insights poses a significant challenge, particularly given the increasing prevalence of data breaches and the sensitivity of personal health information. This paper presents a secure framework that addresses these concerns by integrating privacy-preserving parameters, zero-knowledge proofs (zk-SNARKs), blockchain technology, and a multi-tenant cloud environment. Through advanced cryptographic techniques, specifically zk-SNARKs, the framework ensures that healthcare records remain protected during analytics computations, without exposing raw data. The privacy-preserving analytics engine utilizes anonymized healthcare records and generates zk-SNARKs to validate computations. These proofs, integrated into a blockchain network, create a tamper-proof, transparent ledger that ensures secure healthcare transactions. This approach is critical in scenarios such as telemedicine, where secure data sharing and computation are paramount. By demonstrating its application in a telemedicine app, the framework highlights its practical significance in balancing data utility and privacy in healthcare analytics, providing a scalable and secure solution to a pressing problem.https://ieeexplore.ieee.org/document/10772106/Privacy-preserving analyticszero-knowledge proofsblockchain in healthcaremulti-tenant cloudhealthcare security |
| spellingShingle | S. Bharath Babu K. R. Jothi A Secure Framework for Privacy-Preserving Analytics in Healthcare Records Using Zero-Knowledge Proofs and Blockchain in Multi-Tenant Cloud Environments IEEE Access Privacy-preserving analytics zero-knowledge proofs blockchain in healthcare multi-tenant cloud healthcare security |
| title | A Secure Framework for Privacy-Preserving Analytics in Healthcare Records Using Zero-Knowledge Proofs and Blockchain in Multi-Tenant Cloud Environments |
| title_full | A Secure Framework for Privacy-Preserving Analytics in Healthcare Records Using Zero-Knowledge Proofs and Blockchain in Multi-Tenant Cloud Environments |
| title_fullStr | A Secure Framework for Privacy-Preserving Analytics in Healthcare Records Using Zero-Knowledge Proofs and Blockchain in Multi-Tenant Cloud Environments |
| title_full_unstemmed | A Secure Framework for Privacy-Preserving Analytics in Healthcare Records Using Zero-Knowledge Proofs and Blockchain in Multi-Tenant Cloud Environments |
| title_short | A Secure Framework for Privacy-Preserving Analytics in Healthcare Records Using Zero-Knowledge Proofs and Blockchain in Multi-Tenant Cloud Environments |
| title_sort | secure framework for privacy preserving analytics in healthcare records using zero knowledge proofs and blockchain in multi tenant cloud environments |
| topic | Privacy-preserving analytics zero-knowledge proofs blockchain in healthcare multi-tenant cloud healthcare security |
| url | https://ieeexplore.ieee.org/document/10772106/ |
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