Secure Federated Cloud Storage Protection Strategy using Hybrid Heuristic Attribute-Based Encryption with Permissioned Blockchain

Abstract

Healthcare in the country is grappling with medical data exchange fragmentation, doctor-to-doctor referral process, secure information transfer between hospitals, and patient-convenient portals to personal records being termed as critical points of vulnerability. Concerns are the isolation of health records within hospitals, risks of information abuse of revealed information, and the absence of adequate security protocols. To address these, the Electronic Health Record (EHR) Architecture based on Blockchain was created in collaboration with the hospital, physician, payer, and patient ecosystem. This paper evaluates the feasibility of applying the architecture to store clinical data in a manner that preserves privacy, provides controlled availability and reconciles competing demands for sharing and confidentiality, and promotes compatibility among disparate clinical and administrative system domains. Privacy, in this context, means detailsis accessible only to particular parties and is inviolate in any way—visible, modified, transmitted, or deleted—while being kept, transported, or processed without authorization by a patient. Availability requires that, despite unforeseen sabotage, malfunction, equipment obsolescence, or malicious behavior, patients and legitimate caregivers have access to required information without impeding workflows. Both practice and literature point out that the creation of this double horizon of privacy and resilience require the active participation of all stakeholders, corporate or individual. Interoperability in legacy practice valleys has centered mainly on plugs between information systems. The patient-care community has been busy in now with enabling patients to enter their healthdetails, with the passing being at their discretion. Our system offers privacy and data integrity through the acquisition of medical data first and then encrypting the data through Attribute-Based Encryption, where the best to sharpen the key is the DES algorithm. After encryption, the information is kept in a permissioned blockchain, provides layered access control and protection against compromised information. To further support the patient, we have integrated a predictive analytics module in our patient interface that utilizes machine-learning classifiers i.e. Random Forest, Logistic Regression, and Decision Tree to make near disease detection

Introduction

• Blockchain is a decentralized, tamper-proof digital ledger introduced in 2008 (Satoshi Nakamoto) that ensures data integrity across distributed systems.

• In healthcare, Protected Health Information (PHI) is highly sensitive, and Electronic Health Records (EHRs) serve as comprehensive digital repositories of patient history and data.

• Traditional EHR systems face security, accessibility, and interoperability issues.

• Blockchain, combined with Attribute-Based Encryption (ABE) and Machine Learning (ML), offers a modern, secure, and intelligent solution.

Core Advantages of Blockchain in Healthcare

• Decentralization: Eliminates dependence on a single authority or central server.

• Immutability: Medical records cannot be changed without consensus, ensuring auditability.

• Transparency & Traceability: Every access and update is logged securely.

• Interoperability: Enables seamless and secure data sharing across institutions.

Security Enhancement with ABE

• Attribute-Based Encryption allows access control based on roles (e.g., doctor, lab tech, patient).

• Sensitive data is encrypted such that only users with matching attributes can decrypt specific parts of the record.

• ABE + Blockchain = fine-grained access control + tamper-proof storage.

Intelligence with Machine Learning (ML)

• Algorithms Used:

  • Random Forest

  • Logistic Regression

  • Decision Trees

• Purpose:

  • Disease prediction based on symptoms and medical history

  • Assist doctors in diagnosis

  • Enable smart, data-driven clinical decisions

II. Background and Motivation

Current challenges in healthcare data systems include:

1. Data Fragmentation – Patient records scattered across different providers

2. Security Vulnerabilities – Risk of data breaches and unauthorized access

3. Limited Interoperability – Lack of unified standards across healthcare systems

4. System Reliability Issues – Hardware failures can cause data loss

?? Blockchain + ABE + ML addresses these problems by offering a secure, decentralized, interoperable, and intelligent solution.

III. Literature Survey (Key Studies)

1. Big Data Analytics for Healthcare Opinions – Importance of mining patient feedback for system improvements (Sabarmathi & Chinnaiyan, 2020)

2. Public Auditability for Cloud Storage – Early models for secure, privacy-preserving cloud auditing (Wang et al., 2011)

3. BlockVault Framework – A practical blockchain-cloud hybrid system for secure data storage (Malomo et al., 2020)

These works lay the foundation for blockchain-enabled secure healthcare systems.

IV. Methodology: Secure Federated Cloud Storage Protection Strategy

System Modules

• Patient

• Doctor

• Lab Technician

• Blockchain Server

Each module has clearly defined roles in data management, encryption, and secure access.

Key Components

1. Data Collection & Access Management

   • Secure registration & login

   • Access permissions controlled via blockchain

2. Encryption with ABE + DES

   • Health data is encrypted using ABE

   • DES algorithm generates encryption keys

   • Only users with specific attributes can access appropriate data segments

3. Permissioned Blockchain

   • Stores encrypted health records

   • Ensures:

     • Immutability

     • Auditability

     • Availability

     • Controlled access

4. Secure Data Sharing & Interoperability

   • Seamless record sharing among doctors, labs, and patients

   • Secure APIs and a common blockchain protocol ensure cross-institution compatibility

5. Disease Prediction using ML

   • Inputs: Patient symptoms, history

   • Models trained on real datasets (e.g., Kaggle)

   • Outputs support early diagnosis and patient-centered care

V. Workflow Overview

Technology Stack

• Frontend: Java Swings, Python Tkinter

• Backend: MySQL Server 5.0, Blockchain

• Development Tools: JDK 1.8, NetBeans 8.2, VS Code

• Security: Hybrid Heuristic ABE with DES keys

• ML Models: Random Forest, Logistic Regression, Decision Tree (Python, trained on Kaggle datasets)

• Platform: Windows OS

Conclusion

This project demonstrates a robust and innovative approach to securing electronic health records (EHRs) using a hybrid strategy that integrates Attribute-Based Encryption (ABE) with Permissioned Blockchain technology. By doing so, the solution effectively addresses core challenges in the healthcare sector related to fragmented patient records, insecure storage, and unauthorized data access. The proposed framework enables reliable, privacy-preserving sharing of medical data among patients, doctors, and lab technicians, ensuring that each stakeholder accesses only the information they are permitted to view.Leveraging blockchain ensures a single, tamper-proof version of the truth for each medical record, substantially reducing the chance of data breaches. and unauthorized modifications. The use of optimal encryption keys generated by the DES algorithm further enhances data confidentiality, while permissioned access on the blockchain maintains strong integrity and access control.Furthermore, the project incorporates machine learning algorithms—such as Random Forest, Logistic Regression, and Decision Tree—for disease prediction within the patient module. This empowers personalized, data-driven healthcare and can help clinicians make quicker, more precise decisions. based on historical and real-time patient data.By combining state-of-the-art encryption, blockchain, andpredictive analytics, the system not only secures patient information but also promotes data interoperability, patient-centric sharing, and improved accessibility in healthcare environments—laying the groundwork for a smarter, safer, and more efficient digital health ecosystem.

References

[1] A. B. Kathole et al., “Secure federated cloud storage protection strategy using hybrid heuristic attribute-based encryption with permissioned blockchain,” IEEE Access, vol. 12, pp. 117154–117170, Aug. 2024, doi: 10.1109/ACCESS.2024.3447829. [2] S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008. [Online]. Available: https://bitcoin.org/bitcoin.pdf [3] Y. Zhang, J. Ni, K. Yang, and X. S. Shen, “Security and privacy in smart health: Efficient policy-hiding attribute-based access control,” IEEE Internet Things J., vol. 5, no. 3, pp. 2130–2145, June 2018. [4] Z. Zheng, S. Xie, H. Dai, X. Chen, and H. Wang, “An overview of blockchain technology: Architecture, consensus, and future trends,” in Proc. IEEE Int. Congr. Big Data, 2017, pp. 557–564. [5] K. Salah, N. Nizamuddin, R. Jayaraman, and M. Omar, “Blockchain-based soybean traceability in agricultural supply chain,” IEEE Access, vol. 7, pp. 73295–73305, June 2019. [6] M. A. Khan and K. Salah, “IoT security: Review, blockchain solutions, and open challenges,” Future Generation Computer Systems, vol. 82, pp. 395–411, May 2018. [7] X. Liang, J. Zhao, S. Shetty, J. Liu, and D. Li, “Integrating blockchain for data sharing and collaboration in mobile healthcare applications,” in Proc. IEEE Int. Conf. Wireless and Mobile Computing, Networking and Communications, 2017, pp. 1–6. [8] R. K. Ko, P. Jagadpramana, and B. S. Lee, “TrustCloud: A framework for accountability and trust in cloud computing,” Proc. IEEE World Congr. Services, 2011, pp. 584–588. [9] M. Alhussein, K. Aurangzeb, and S. I. Haider, “Blockchain-based secure healthcare system for electronic medical records,” IEEE Access, vol. 9, pp. 19230–19244, Jan. 2021. [10] S. R. Moosavi, T. N. Gia, A. Rahmani, E. Nigussie, S. Virtanen, and H. Tenhunen, “End-to-end security scheme for mobility enabled healthcare Internet of Things,” Future Generation Computer Systems, vol. 64, pp. 108–124, Nov. 2016.

Copyright

Copyright © 2025 SheelaRani C M, Prapthi Shenava, Rakshitha M S, Sinchana N, Thanvi B V. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.