Blockchain-Integrated Privacy-Preserving Medical Insurance Claim Processing Using Homomorphic Encryption
- URL: http://arxiv.org/abs/2511.07818v1
- Date: Wed, 12 Nov 2025 01:21:16 GMT
- Title: Blockchain-Integrated Privacy-Preserving Medical Insurance Claim Processing Using Homomorphic Encryption
- Abstract summary: This study focuses on enabling the management of insurance claims in a transparent, privacy-protecting manner.<n>The proposed system adds blockchain technology to provide an unchangeable, decentralized, and auditable claim transactions ledger.<n>To protect critical patient information, the framework employs homomorphic encryption a modern form of cryptography.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This research proposes a decentralized and cryptographically secure framework to address the most acute issues of privacy, data security, and protection in the ecosystem of medical insurance claim processing. The scope of this study focuses on enabling the management of insurance claims in a transparent, privacy-protecting manner while maintaining the efficiency and trust level needed by the patients, healthcare providers, and insurers. To accomplish this, the proposed system adds blockchain technology to provide an unchangeable, decentralized, and auditable claim transactions ledger which enhances overall claim-related processes and trust among all stakeholders. To protect critical patient information, the framework employs homomorphic encryption a modern form of cryptography to allow authorized insurance providers to perform necessary operations like claim adjudication and reimbursement on encrypted medical records without any decryption during the process. This method significantly reduces the third-party processing privacy risk because patient data can be kept secret even when third-party processing is done. In addition, smart contracts improve automation of the most important procedures in the claim processing pipeline, which decreases manual, operational, and susceptibility towards human blunders or deceitful acts. The integration of these two transformative technologiesblockchain and homomorphic encryption represents the core contribution of this work, enabling the coexistence of transparency and privacy which are usually viewed as competing objectives in traditional systems. As a result, these technologies are expected to foster the creation of a reliable, effective, and privacy safeguarding architecture that could transform the medical claim submission systems paradigm.
Related papers
- CONFETTY: A Tool for Enforcement and Data Confidentiality on Blockchain-Based Processes [39.8807633060402]
This work presents the CONFETTY open-source web application, a platform for public-blockchain based process execution.<n>We use smart contracts to enact, enforce, and store public interactions.<n>We adopt attribute-based encryption techniques for fine-grained access to confidential information.
arXiv Detail & Related papers (2026-03-14T11:27:21Z) - Blockchain-Enabled Explainable AI for Trusted Healthcare Systems [0.0]
This paper introduces a-Integrated Explainable AI Framework (BXHF) for healthcare systems.<n>We tackle two challenges confronting health information networks: safe data exchange and comprehensible AI-driven clinical decision-making.<n>Our architecture incorporates blockchain, ensuring patient records are immutable, auditable, and tamper-proof.
arXiv Detail & Related papers (2025-09-18T14:17:19Z) - Leveraging Blockchain and Proxy Re-Encryption to secure Medical IoT Records [0.0]
We propose an architecture integrating private blockchain and Proxy Re-Encryption (PRE) to enable secure, traceable, and privacy-preserving data sharing.<n>PRE is a cryptographic technique that allows encrypted data to be re-encrypted for a new recipient without exposing it to intermediaries.
arXiv Detail & Related papers (2025-09-10T08:45:20Z) - Secure and Scalable Face Retrieval via Cancelable Product Quantization [48.58859061524146]
homomorphic encryption (HE) offers strong security guarantees by enabling arithmetic computations in the cipher space.<n>We propose Cancelable Product Quantization, a highly efficient framework for secure face representation retrieval.
arXiv Detail & Related papers (2025-08-31T10:19:55Z) - Beyond Jailbreaking: Auditing Contextual Privacy in LLM Agents [43.303548143175256]
This study proposes an auditing framework for conversational privacy that quantifies an agent's susceptibility to risks.<n>The proposed Conversational Manipulation for Privacy Leakage (CMPL) framework is designed to stress-test agents that enforce strict privacy directives.
arXiv Detail & Related papers (2025-06-11T20:47:37Z) - LLM Agents Should Employ Security Principles [60.03651084139836]
This paper argues that the well-established design principles in information security should be employed when deploying Large Language Model (LLM) agents at scale.<n>We introduce AgentSandbox, a conceptual framework embedding these security principles to provide safeguards throughout an agent's life-cycle.
arXiv Detail & Related papers (2025-05-29T21:39:08Z) - hChain 4.0: A Secure and Scalable Permissioned Blockchain for EHR Management in Smart Healthcare [0.0]
hChain 4.0 employs a permissioned blockchain to provide a secure and scalable data infrastructure.<n>The proposed framework enables anonymized health data sharing for medical research by pseudonymizing patient identity.<n> Experimental assessments indicate that the proposed approach achieves higher scalability, cost-effectiveness, and validated security.
arXiv Detail & Related papers (2025-05-20T03:13:48Z) - Empowering Patients for Disease Diagnosis and Clinical Treatment: A Smart Contract-Enabled Informed Consent Strategy [0.312488427986006]
Digital healthcare systems have revolutionized medical services, facilitating provider collaboration, enhancing diagnosis, and optimizing and improving treatments.<n>They deliver superior quality, faster, reliable, and cost-effective services.<n>Researchers are addressing pressing health challenges by integrating information technology, computing resources, and digital health records.<n> digitizing healthcare introduces significant risks to patient data privacy and security, with the potential for unauthorized access to protected health information.
arXiv Detail & Related papers (2024-12-13T03:20:15Z) - Balancing Confidentiality and Transparency for Blockchain-based Process-Aware Information Systems [43.253676241213626]
We propose an architecture for blockchain-based PAISs to preserve confidentiality and transparency.<n>Smart contracts enact, enforce and store public interactions, while attribute-based encryption techniques are adopted to specify access grants to confidential information.<n>We assess the security of our solution through a systematic threat model analysis and evaluate its practical feasibility.
arXiv Detail & Related papers (2024-12-07T20:18:36Z) - SPOQchain: Platform for Secure, Scalable, and Privacy-Preserving Supply Chain Tracing and Counterfeit Protection [46.68279506084277]
This work proposes SPOQchain, a novel blockchain-based platform that provides comprehensive traceability and originality verification.
It provides an analysis of privacy and security aspects, demonstrating the need and qualification of SPOQchain for the future of supply chain tracing.
arXiv Detail & Related papers (2024-08-30T07:15:43Z) - A Scalable Multi-Layered Blockchain Architecture for Enhanced EHR Sharing and Drug Supply Chain Management [3.149883354098941]
This paper presents a scalable, multi-layered blockchain architecture for secure Electronic Health Record sharing and drug supply chain management.<n>The proposed framework introduces five distinct layers that enhance system performance, security, and patient-centric access control.<n>Our solution ensures data integrity, privacy, and interoperability, making it compatible with existing healthcare systems.
arXiv Detail & Related papers (2024-02-27T09:20:16Z) - Blockchain-empowered Federated Learning for Healthcare Metaverses:
User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses.
We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
arXiv Detail & Related papers (2023-07-29T12:54:03Z) - Decentralized Stochastic Optimization with Inherent Privacy Protection [103.62463469366557]
Decentralized optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing.
Since involved data, privacy protection has become an increasingly pressing need in the implementation of decentralized optimization algorithms.
arXiv Detail & Related papers (2022-05-08T14:38:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.