Autonomous vehicles have attracted considerable attention from researchers and organizations, with artificial intelligence (AI) playing a key role in this technology. For AI models in autonomous vehicles to be reliable, the integrity of the training data is crucial, resulting in the development of various blockchain-based management systems. However, conventional blockchain systems incur significant time delays when processing training data transactions, posing challenges in autonomous vehicle environments that require real-time processing. In this study, we propose a hashgraph-based training data management system for trusted AI. To validate our system, we conducted simulations using the CARLA simulator and compared its performance to a conventional blockchain-based system. The simulation results show that Hedera achieved significantly lower latencies and better scalability than Ethereum, confirming its suitability for secure and efficient AI data verification in autonomous systems.
Caixiang Fan, Amirhossein Sohrabbeig, Petr Musı́lek
Blockchain-based peer-to-peer energy trading enables individuals to directly share renewable energy using Internet of Things technologies. However, it faces significant challenges related to privacy, scalability, and the integration of advanced artificial intelligence. To address these issues, this article proposes zkPET, a secure and intelligent peer-to-peer energy trading framework. zkPET integrates machine learning and blockchain with advanced cryptographic techniques of zero-knowledge machine learning to protect user data while enabling intelligent decision making. In the zkPET framework, the computationally intensive operations of various machine learning models are executed off-chain, and only succinct cryptographic proofs of these computations are uploaded to the blockchain for verification and recording. In addition, a time-series clustering approach is incorporated into federated learning to enhance both inference accuracy and the efficiency of proof generation. Experimental validation using the zero-knowledge proof tool EZKL and a real-world electricity dataset demonstrates the feasibility and effectiveness of zkPET. The results underscore its potential to significantly improve privacy, scalability, and computational efficiency in decentralized energy trading, contributing to the advancement of secure and intelligent energy markets.
Diabetic Retinopathy (DR) detection in distributed telemedicine environments requires secure, scalable, and privacy-preserving solutions. Traditional federated learning (FL) relies on a central server, raising concerns about data privacy and system trust. We propose a novel serverless framework, FL-BC-SMPC-SMOTE, that integrates deep learning, FL, secure multi-party computation (SMPC), the Synthetic Minority Over-sampling Technique (SMOTE), Blockchain (Hyperledger Fabric), and the InterPlanetary File System (IPFS) to address these challenges. Using the APTOS 2019 dataset, we trained CNN-based models (e.g., EfficientNet-B0, ResNet-18) across 2–10 clients, achieving approximately 90% accuracy without raw data sharing. SMPC eliminates the need for a central aggregator by distributing encrypted model updates among clients, enabling privacy-preserving learning. Blockchain ensures auditable and tamper-resistant aggregation, while IPFS significantly reduces communication overhead—from 64 GB to 100 KB per round. Local SMOTE enhances recall for minority classes by 10–15%, promoting equity in DR severity classification. Compared to differentially private baselines (52.18% accuracy), our framework delivers a robust balance of performance, privacy, and fairness. This GDPR/HIPAA-compliant solution offers a practical and trustworthy approach to decentralized DR detection in real-world telemedicine settings.
Federated Learning (FL) has emerged as a critical paradigm for enabling privacy-preserving machine learning, particularly in regulated sectors such as finance and healthcare. However, standard FL strategies often encounter significant operational challenges related to fault tolerance, system resilience against concurrent client and server failures, and the provision of robust, verifiable privacy guarantees essential for handling sensitive data. These deficiencies can lead to training disruptions, data loss, compromised model integrity, and non-compliance with data protection regulations (e.g., GDPR, CCPA). This paper introduces Differentially Private Resilient Temporal Federated Learning (DP-RTFL), an advanced FL framework designed to ensure training continuity, precise state recovery, and strong data privacy. DP-RTFL integrates local Differential Privacy (LDP) at the client level with resilient temporal state management and integrity verification mechanisms, such as hash-based commitments (referred to as Zero-Knowledge Integrity Proofs or ZKIPs in this context). The framework is particularly suited for critical applications like credit risk assessment using sensitive financial data, aiming to be operationally robust, auditable, and scalable for enterprise AI deployments. The implementation of the DP-RTFL framework is available as open-source.
Mai Shawkat, Ali El-desoky, Zainab H. Ali, Mofreh Salem
Abstract The Industrial Internet of Things (IIoT) applications have been recognized as an advancement of the conventional wireless network that concentrates on incorporating processes and machines specifically for industrial applications. These Industrial applications frequently use centralized machine learning (ML) approaches not only to enhance their functionality but also to evaluate sensor data for a variety of purposes, including digitizing operations in manufacturers, forecasting maintenance requirements in industrial equipment, and detecting anomalies for security monitoring, they may adversely affect overall system performance due to high cost of computing power and privacy concerns, as so much data is stored on a cloud server. Federated Learning (FL) has emerged as a new benchmark for centralized ML methods. It sends models to user devices without transferring private data to third-party or central servers; it is one of the promising solutions to data leakage issues. This work introduces a comprehensive overview of the advancements, challenges, and future directions in FL adoption with edge devices. It covers security threats and mitigation strategies, emphasizing its categories, privacy and concerns, communication overhead obstacles, heterogeneity issues, aggregation techniques, and associated development tools. This review paper delves into FL-related topics, including system platforms, offering a comprehensive overview of best practice systems in real-world FL applications. To ensure security in IIoT applications, reviewing threats and mitigation strategies by integrating FL with state-of-the-art technologies such as blockchain, federated reinforcement learning, and federated meta-learning has been explored. Finally, the recent research is taking place to determine new future directions and opportunities for FL security defense mechanisms has been considered at the end of this review paper.
Abstract: Federated deep learning (FDL) is an emerging paradigm that enables multiple decentralized devices or institutions to collaboratively train a shared model while keeping data localized. This approach preserves privacy, reduces communication overhead, and complies with data governance regulations. In this paper, we explore the implementation and performance of FDL in real-world scenarios such as healthcare, finance, and IoT systems. Utilizing frameworks like TensorFlow Federated, PyTorch, and interpretability tools like SHAP and LIME, we evaluate FDL against centralized deep learning models. We analyze convergence rates, model accuracy, data privacy risk, and computational efficiency. Regression and predictive analyses reveal that FDL can retain over 90% accuracy of centralized models with significantly enhanced data security. Keywords: Federated Learning, Deep Learning, Privacy Preservation, Decentralized Training, TensorFlow Federated, Secure AI, SHAP, LIME, Model Interpretability
Sung-eun Heo, Manho Kim, Wijin Kim, Jongseok Choi · 12 authors
Biometric data has the potential to revolutionize health analytics and pharmacology by providing personalized insights into drug efficacy and health trajectories. However, its governance presents significant ethical challenges, particularly around individual ownership and privacy. This study addresses these challenges by proposing a sustainable and ethical framework that integrates biometric data with non-fungible tokens (NFTs). We developed a customized NFT framework with advanced smart contract functionalities that enhance privacy protection and decentralized authentication of biometric data ownership. This approach ensures the secure and ethical management of digital health data while reinforcing individuals' control over their biometric information. By leveraging cryptographic techniques for privacy protection, this framework enhances both the security and efficiency of personal health data management, offering a new perspective on ownership in digital health. Furthermore, a sustainable economic model is proposed to facilitate ethical transactions of tokenized biometric data within the NFT marketplace. The implications of this study extend beyond technology and commerce, offering valuable insights into human behavior in emerging digital economies and contributing to the creation of a more sustainable and equitable digital health ecosystem.
Chitrita Devi, R. R. Shantha Spandana, G.V.T. Swapna, G Viswanath
This project provides a Cloud-Assisted Decentralized privacy-preserving Framework (CA-DPPF) that amalgamates cloud computing, blockchain generation, and IPFS to tackle the complexities of securely and efficaciously storing sensitive healthcare data. The framework utilizes ECDSA digital signatures and RSA encryption to assure strong person authentication and statistics safety, in accordance with present day developments in safeguarding healthcare information. IPFS is applied for scalable storage solutions, addressing the limitations of traditional centralized cloud services, as indicated in previous research. Blockchain era augments the system through supplying immutable document-preserving, mitigating the weaknesses of centralized systems. A rankings module is incorporated to guarantee the legitimacy of healthcare feedback, allowing people to assess doctors, with these checks securely documented on the blockchain to prevent manipulation. smart contracts, created in Solidity, enable secure transactions and govern user data at the Ethereum blockchain, making certain transparency and integrity in all interactions. The studies gives a spread that integrates the CHACHA20 encryption algorithm, strengthening computational efficiency and safety while complementing present encryption methods and improving usual system overall performance.
Federated Learning (FL) enables collaborative model training across hospitals while keeping patient data local, thus aiming to satisfy strict healthcare privacy regulations (e.g. HIPAA, GDPR). However, FL still leaks information via shared model updates, exposing it to membership inference and gradient inversion attacks. In this work, we propose an end-to-end framework that integrates zero-knowledge proofs (ZKPs) with FL to ensure both data privacy and trust in the aggregation process. In our design, each hospital (client) sends encrypted model updates to a central aggregator, which then computes the global model and simultaneously generates a succinct ZKP (e.g. a zk-SNARK) attesting to the correctness of the aggregation. Clients (or a verifier network) can efficiently verify this proof without learning any additional information. We simulate a disease-prediction task on synthetic medical data and evaluate metrics including predictive accuracy, proof generation/verification time, and communication overhead. Our results (see Table 1 and Fig. 3) show that incorporating ZKP maintains almost identical model accuracy compared to standard FL while adding moderate computational and bandwidth overhead. ZKP verification costs scale favorably (often <50% of proof generation time) and can be offloaded to a blockchain network to avoid burdening resource-constrained hospitals. The key contribution is a structured ZK-FL framework combining FL and zk-SNARKs, along with a formal threat model. This approach closes FL’s trust gap in healthcare settings, and suggests future work on scalable proof systems (e.g. post-quantum ZKPs) and integration with blockchain-based verifiers.
Ahmed M. Tawfik, Ayman Al-Ahwal, Adly S. Tag Eldien, Hala H. Zayed
Ensuring privacy and confidentiality in healthcare data management remains a critical challenge. Traditional centralized access control mechanisms are susceptible to security breaches, including unauthorized access, data leakage, and single points of failure, as well as privacy violations such as patient record exposure and improper data sharing. To address these issues, this paper proposes ACHealthChain, a blockchain-based framework leveraging Hyperledger Fabric for decentralized and transparent access control. The framework integrates the InterPlanetary File System (IPFS) for decentralized storage and ensures privacy through Hyperledger Fabric channels. ACHealthChain features PolicyChain for fine-grained access control and revocation, structuring patient health data into separate subchains for EHRs and diagnoses with permissioned access. Additionally, LogChain enhances auditing and accountability. A series of experiments evaluate ACHealthChain's performance and scalability, considering metrics such as throughput, latency, and resource utilization. Results demonstrate that ACHealthChain improves throughput by 19.7% and reduces latency by 87%, outperforming existing frameworks built on the same platform. The scalability analysis further confirms the framework's capability to handle increasing workloads within an expanding blockchain network. ACHealthChain presents a promising solution for secure and efficient healthcare data sharing with potential real-world applications.
ABSTRACT The emergence of the Metaverse has introduced significant challenges in task offloading and data processing due to its virtual universe nature with immersive environments and a multitude of interconnected users and devices. The abundance of data in the Metaverse poses security challenges in local processing, necessitating traditional methods such as data transfer to Mobile Edge Computing (MEC) and subsequently to the cloud, thereby emphasizing security concerns. In this paper, a novel approach to address these challenges has been introduced: An Ethereum Blockchain‐based MEC framework uses smart contracts designed to ensure secure task offloading. It enables authentication in the Metaverse through smart contracts, followed by modeling the task offloading issue as a Markov Decision Process (MDP). To solve this MDP problem, a hybrid algorithm integrating Deep Q‐Networks (DQN) with Bidirectional Long Short‐Term Memory (Bi‐LSTM), known as BRL‐Net (Bi‐LSTM Reinforcement Learning Network), has been proposed. This framework enables secure and efficient task offloading in dynamic Metaverse environments. BRL‐Net outperforms Proximal Policy Optimization (PPO), achieving a 9.93% higher reward and greater stability. The BRL‐Net's performance across Blockchain consensus mechanisms shows Delegated Proof of Stake (DPoS) as the most efficient, reducing latency by 49.96%, increasing throughput by 10.48%, and lowering energy consumption by 50.24%, compared to Proof of Stake (PoS), thereby optimizing Metaverse performance.
With the rise of machine learning techniques, ensuring the fairness of decisions made by machine learning algorithms has become of great importance in critical applications. However, measuring fairness often requires full access to the model parameters, which compromises the confidentiality of the models. In this paper, we propose a solution using zero-knowledge proofs, which allows the model owner to convince the public that a machine learning model is fair while preserving the secrecy of the model. To circumvent the efficiency barrier of naively proving machine learning inferences in zero-knowledge, our key innovation is a new approach to measure fairness only with model parameters and some aggregated information of the input, but not on any specific dataset. To achieve this goal, we derive new bounds for the fairness of logistic regression and deep neural network models that are tighter and better reflecting the fairness compared to prior work. Moreover, we develop efficient zero-knowledge proof protocols for common computations involved in measuring fairness, including the spectral norm of matrices, maximum, absolute value, and fixed-point arithmetic. We have fully implemented our system, FairZK, that proves machine learning fairness in zero-knowledge. Experimental results show that FairZK is significantly faster than the naive approach and an existing scheme that use zero-knowledge inferences as a subroutine. The prover time is improved by 3.1x--1789x depending on the size of the model and the dataset. FairZK can scale to a large model with 47 million parameters for the first time, and generates a proof for its fairness in 343 seconds. This is estimated to be 4 orders of magnitude faster than existing schemes, which only scale to small models with hundreds to thousands of parameters.
Awid Vaziry, Sandro Rodriguez Garzon, Patrick Herbke, Carlo Segat · 5 authors
The intersection of blockchain (distributed ledger) and identity management lacks a comprehensive framework for classifying distributed-ledger-based identity solutions. This paper introduces a methodologically developed taxonomy derived from the analysis of 390 scientific papers and expert discussions. The resulting framework consists of 22 dimensions with 113 characteristics, organized into three groups: trust anchor implementations, identity architectures (identifiers and credentials), and ledger specifications. This taxonomy facilitates the systematic analysis, comparison, and design of distributed-ledger-based identity solutions, as demonstrated through its application to two distinct architectures. As the first methodology-driven taxonomy in this field, this work advances standardization and enhances understanding of distributed-ledger-based identity architectures. It provides researchers and practitioners with a structured framework for evaluating design decisions and implementation approaches.
The rapid growth of intelligent systems has raised significant concerns regarding data privacy and security. Traditional centralized machine learning approaches require data aggregation, increasing the risk of data breaches and regulatory violations. Federated Learning (FL) has emerged as a promising paradigm that enables collaborative model training while keeping data decentralized. This paper presents a comprehensive study of federated learning for privacy-preserving intelligent systems, highlighting its architecture, methodologies, applications, and challenges. The study also proposes an adaptive federated framework integrating secure aggregation and differential privacy. The findings demonstrate that federated learning significantly enhances privacy while maintaining model performance, making it suitable for healthcare, finance, and IoT applications.
Junhui Zhao, Yingxuan Guo, Longxia Liao, Dongming Wang
Vehicular Ad-hoc Network (VANET) is a platform that facilitates Vehicle-to-Everything (V2X) interconnection. However, its open communication channels and high-speed mobility introduce security and privacy vulnerabilities. Anonymous authentication is crucial in ensuring secure communication and privacy protection in VANET. However, existing anonymous authentication schemes are prone to single points of failure and often overlook the efficient tracking of the true identities of malicious vehicles after pseudonym changes. To address these challenges, we propose an efficient anonymous authentication scheme for blockchain-based VANET. By leveraging blockchain technology, our approach addresses the challenges of single points of failure and high latency, thereby enhancing the service stability and scalability of VANET. The scheme integrates homomorphic encryption and elliptic curve cryptography, allowing vehicles to independently generate new pseudonyms when entering a new domain without third-party assistance. Security analyses and simulation results demonstrate that our scheme achieves effective anonymous authentication in VANET. Moreover, the roadside unit can process 500 messages per 19 ms. As the number of vehicles in the communication domain grows, our scheme exhibits superior message-processing capabilities.
Shamim Akhtar, Muhammad Taimoor, Ghulam Fatima, Hurma Islam
This research explores the transformative role of blockchain technology in ensuring secure and trustworthy digital transactions. With the increasing reliance on digital platforms across industries such as finance, healthcare, and supply chains, blockchain has emerged as a solution to the challenges posed by traditional centralized systems, including data breaches, fraud, and lack of transparency. The study investigates blockchain's decentralized structure, cryptographic security features, consensus mechanisms, and smart contracts to evaluate how it enhances data integrity and trust in digital transactions. A qualitative approach was employed, utilizing case studies and a comprehensive review of existing literature. The results show that blockchain’s decentralization significantly reduces single points of failure, while its consensus mechanisms and smart contracts increase trust and automate transactions. However, challenges such as scalability, energy consumption, and regulatory concerns remain. The research highlights blockchain’s potential for transforming digital transactions but calls for further innovation to address these issues. The findings suggest that blockchain has the capacity to revolutionize secure transactions across various sectors but requires continued development to achieve widespread adoption and scalability.
Although differential privacy (DP) is widely regarded as the de facto standard for data privacy, its implementation remains vulnerable to unfaithful execution by servers, particularly in distributed settings. In such cases, servers may sample noise from incorrect distributions or generate correlated noise while appearing to follow established protocols. This work addresses these malicious behaviours in a distributed client-server-verifier setup, under Verifiable Distributed Differential Privacy (VDDP), a novel framework for the verifiable execution of distributed DP mechanisms. We systematically capture end-to-end security and privacy guarantees against potentially colluding adversarial behaviours of clients, servers, and verifiers by characterizing the connections and distinctions between VDDP and zero-knowledge proofs (ZKPs). We develop three novel and efficient instantiations of VDDP: (1) the Verifiable Distributed Discrete Laplace Mechanism (VDDLM), which achieves up to a 400,000x improvement in proof generation efficiency with only 0.1--0.2x error compared with the previous state-of-the-art verifiable differentially private mechanism and includes a tight privacy analysis that accounts for all additional privacy losses due to numerical imprecisions, applicable to other secure computation protocols for DP mechanisms based on cryptography; (2) the Verifiable Distributed Discrete Gaussian Mechanism (VDDGM), an extension of VDDLM that incurs limited overhead in real-world applications; and (3) an improved solution to Verifiable Randomized Response (VRR) under local DP, as a special case of VDDP, achieving up to a 5,000x reduction in communication costs and verifier overhead.
This study introduces a cutting-edge architecture developed for the NewbornTime project, which uses advanced AI to analyze video data at birth and during newborn resuscitation, with the aim of improving newborn care. The proposed architecture addresses the crucial issues of patient consent, data security, and investing trust in healthcare by integrating Ethereum blockchain with cloud computing. Our blockchain-based consent application simplifies patient consent's secure and transparent management. We explain the smart contract mechanisms and privacy measures employed, ensuring data protection while permitting controlled data sharing among authorized parties. This work demonstrates the potential of combining blockchain and cloud technologies in healthcare, emphasizing their role in maintaining data integrity, with implications for computer science and healthcare innovation.
Saad Alahmari, Amal Alshardan, Fahd N. Al‐Wesabi, Shaymaa E. Sorour · 8 authors
As healthcare services have become increasingly digitized, Electronic Health Records (EHRs) have become widely adopted, providing seamless data exchange among providers. Conventional EHRs, however, are extremely vulnerable to cyber threats because patients' sensitive data is centralized and transmitted electronically. The paper proposes a decentralized, privacy-preserving framework for managing EHRs on blockchains in order to address these security and privacy concerns. Using cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, the proposed system enhances security and ensures data integrity. Additionally, the model facilitates scalable, efficient, and secure access to patient records through the integration of cloud-based storage and blockchain. Using smart contracts, we also ensure compliance with healthcare regulations by regulating access control and authentication. As a result of performance evaluations, the proposed approach is demonstrated to be feasible, and the advantages it offers in terms of security, privacy, and efficiency are highlighted.
Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires access to model weights and (potentially sensitive or private) training data. So-called Zero-knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) would appear to provide the capability to verify model inference without access to such sensitive data. However, applying ZK-SNARKs to modern neural networks, such as transformers and large vision models, introduces significant computational overhead. We present TeleSparse, a ZK-friendly post-processing mechanisms to produce practical solutions to this problem. TeleSparse tackles two fundamental challenges inherent in applying ZK-SNARKs to modern neural networks: (1) Reducing circuit constraints: Over-parameterized models result in numerous constraints for ZK-SNARK verification, driving up memory and proof generation costs. We address this by applying sparsification to neural network models, enhancing proof efficiency without compromising accuracy or security. (2) Minimizing the size of lookup tables required for non-linear functions, by optimizing activation ranges through neural teleportation, a novel adaptation for narrowing activation functions' range. TeleSparse reduces prover memory usage by 67% and proof generation time by 46% on the same model, with an accuracy trade-off of approximately 1%. We implement our framework using the Halo2 proving system and demonstrate its effectiveness across multiple architectures (Vision-transformer, ResNet, MobileNet) and datasets (ImageNet,CIFAR-10,CIFAR-100). This work opens new directions for ZK-friendly model design, moving toward scalable, resource-efficient verifiable deep learning.
The integration of machine learning (ML) in healthcare has unlocked transformative potential in disease prediction, personalized treatment, medical imaging, remote patient monitoring, and genomic data analysis. However, the sensitive nature of medical data introduces critical concerns regarding patient privacy, data security, and regulatory compliance. This chapter presents a comprehensive overview of privacy-preserving machine learning approaches tailored for healthcare applications, with a focus on technical frameworks, real-time implementations, and regulatory alignment. It explores the use of advanced techniques such as federated learning, differential privacy, homomorphic encryption, and zero-knowledge proofs to safeguard patient information while maintaining model utility. The chapter also addresses domain-specific challenges in processing real-time health data streams and implementing privacy-aware algorithms in resource-constrained environments. By bridging the gap between technical innovation and clinical applicability, this work emphasizes the importance of secure, scalable, and ethically aligned ML solutions in modern healthcare ecosystems. The discussion was contextualized within current legal frameworks and highlights future directions for research and implementation to ensure trust, transparency, and resilience in data-driven medical systems.
Federated Learning (FL) has emerged as a transformative paradigm in the field of distributed machine learning, enabling multiple clients such as mobile devices, edge nodes, or organizations to collaboratively train a shared global model without the need to centralize sensitive data. This decentralized approach addresses growing concerns around data privacy, security, and regulatory compliance, making it particularly attractive in domains such as healthcare, finance, and smart IoT systems. This survey provides a concise yet comprehensive overview of Federated Learning, beginning with its core architecture and communication protocol. We discuss the standard FL lifecycle, including local training, model aggregation, and global updates. A particular emphasis is placed on key technical challenges such as handling non-IID (non-independent and identically distributed) data, mitigating system and hardware heterogeneity, reducing communication overhead, and ensuring privacy through mechanisms like differential privacy and secure aggregation. Furthermore, we examine emerging trends in FL research, including personalized FL, cross-device versus cross-silo settings, and integration with other paradigms such as reinforcement learning and quantum computing. We also highlight real-world applications and summarize benchmark datasets and evaluation metrics commonly used in FL research. Finally, we outline open research problems and future directions to guide the development of scalable, efficient, and trustworthy FL systems.
Mayur Patel, Aditya Vishwakarma, Mohammad Kaif, Shahan Ali
Abstract: Online blockchain-based certificate generation and validation represent a crucial advancement in enhancing transparency, security, and efficiency within government operations. This system enables government organizations to securely issue, verify, and manage certificates, ensuring the integrity of essential documents such as birth certificates, educational diplomas, business licenses, and other critical records. The integration of blockchain technology into certificate management systems can significantly streamline government services while safeguarding against fraudulent activities, document tampering, and administrative errors.In recent years, however, blockchain technology has emerged as a promising solution to address these issues, offering a decentralized, tamper-proof system for the generation and validation of certificates. Blockchain, which is essentially a distributed ledger, stores data across a network of nodes, making it virtually immutable and highly resistant to alterations. Each record or transaction on the blockchain is cryptographically secured, ensuring that once a certificate is issued and recorded, it cannot be modified or deleted without detection
ABSTRACT Blockchain technology is gaining importance in different sectors like healthcare, finance, agriculture, and many more. The important capabilities of blockchain like decentralization, immutability, consensus mechanism, etc. provide security, privacy, transparency, accountability, and many other benefits. On the other hand, Mobile Edge Computing (MEC) is a distributed framework that provides cloud computing capabilities to mobile devices. The existing studies combining blockchain technology and MEC often do not consider the delay and energy consumption for data offloading. In this paper, a blockchain‐based scheme has been proposed for sharing Internet of Medical Things (IoMT) data between a patient and a doctor, which offloads tasks to the MEC server to achieve energy efficiency. In the proposed scheme, the Non‐Orthogonal Multiple Access (NOMA) protocol is used to share a channel among several users. Here, NOMA offers some advantages in the system like low cost, latency, and power consumption. In the proposed scheme, the energy consumption is optimized based on the task delegation decision and resource distribution in the MEC server. Additionally, operations of the blockchain network are automated using various smart contracts. The efficiency of the proposed scheme is analyzed in terms of energy consumption, average transmission rate, and offloading delay in processing healthcare data. The experimental results demonstrate that the proposed model enhances energy efficiency and optimizes performance compared to the state‐of‐the‐art offloading schemes.