Cryptocurrency systems can be subject to deanonimization attacks by exploiting the network-level communication on their peer-to-peer network. Adversaries who control a set of colluding node(s) within the peer-to-peer network can observe transactions being exchanged and infer the parties involved. Thus, various network anonymity schemes have been proposed to mitigate this problem, with some solutions providing theoretical anonymity guarantees. In this work, we model such peer-to-peer network anonymity solutions and evaluate their anonymity guarantees. To do so, we propose a novel framework that uses Bayesian inference to obtain the probability distributions linking transactions to their possible originators. We characterize transaction anonymity with those distributions, using entropy as metric of adversarial uncertainty on the originator's identity. In particular, we model Dandelion, Dandelion++ and Lightning Network. We study different configurations and demonstrate that none of them offers acceptable anonymity to their users. For instance, our analysis reveals that in the widely deployed Lightning Network, with 1% strategically chosen colluding nodes the adversary can uniquely determine the originator for about 50% of the total transactions in the network. In Dandelion, an adversary that controls 15% of the nodes has on average uncertainty among only 8 possible originators. Moreover, we observe that due to the way Dandelion and Dandelion++ are designed, increasing the network size does not correspond to an increase in the anonymity set of potential originators. Alarmingly, our longitudinal analysis of Lightning Network reveals rather an inverse trend -- with the growth of the network the overall anonymity decreases.
Zhijie Sun, Dezhi Han, Dun Li, Xiangsheng Wang · 6 authors
Abstract Medical data involves a large amount of personal information and is highly privacy sensitive. In the age of big data, the increasing informatization of healthcare makes it vital that medical information is stored securely and accurately. However, current medical information is subject to the risk of privacy leakage and difficult to share. To address these issues, this paper proposes a healthcare information security storage solution based on hyperledger fabric and the attribute-based access control framework. The scheme first utilizes attribute-based access control, which allows dynamic and fine-grained access to medical information, and then stores the medical information in the blockchain, which can be secured and tamper-proof by formulating corresponding smart contracts. In addition, this solution also incorporates IPFS technology to relieve the storage pressure of the blockchain. Experiments show that the proposed scheme combining access control of attributes and blockchain technology in this paper can not only ensure the secure storage and integrity of medical information but also has a high throughput when accessing medical information
Jiasi Weng, Jian Weng, Gui Hong Tang, Anjia Yang · 6 authors
We propose a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing in a distrustful multi-stakeholder environment. The approach is aimed to enable that a CNN modeldeveloperconvinces auserof the truthful CNN performance over non-public data frommultiple testers, while respecting model and data privacy. To balance the security and efficiency issues, we appropriately integrate three tools with the CNN testing, including collaborative inference, homomorphic encryption (HE) and zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK). We start with strategically partitioning a CNN model into a private part kept locally by the model developer, and a public part outsourced to an outside server. Then, the private part runs over the HE-protected test data sent by a tester, and transmits its outputs to the public part for accomplishing subsequent computations of the CNN testing. Second, the correctness of the above CNN testing is enforced by generating zk-SNARK based proofs, with an emphasis on optimizing proving overhead for two-dimensional (2-D) convolution operations, since the operations dominate the performance bottleneck during generating proofs. We specifically present a new quadratic matrix program (QMP)-based arithmetic circuit witha single multiplication gatefor expressing 2-D convolution operations between multiple filters and inputs in a batch manner. Third, we aggregate multiple proofs with respect to a same CNN model but different testers’ test data (i.e., different statements) into one proof, and ensure that the validity of the aggregated proof implies the validity of the original multiple proofs. Lastly, our experimental results demonstrate that our QMP-based zk-SNARK performs nearly 13.9× faster than the existing quadratic arithmetic program (QAP)-based zk-SNARK in proving time, and 17.6× faster in Setup time, for high-dimension matrix multiplication. Besides, the limitation on handling a bounded number of multiplications of QAP-based zk-SNARK is relieved.
Zero-knowledge proof (ZKP) mixers are one of the most widely-used blockchain privacy solutions, operating on top of smart contract-enabled blockchains. We find that ZKP mixers are tightly intertwined with the growing number of Decentralized Finance (DeFi) attacks and Blockchain Extractable Value (BEV) extractions. Through coin flow tracing, we discover that 205 blockchain attackers and 2,595 BEV extractors leverage mixers as their source of funds, while depositing a total attack revenue of 412.87M USD. Moreover, the US OFAC sanctions against the largest ZKP mixer, Tornado.Cash, have reduced the mixer's daily deposits by more than 80%. Further, ZKP mixers advertise their level of privacy through a so-called anonymity set size, which similarly to k-anonymity allows a user to hide among a set of k other users. Through empirical measurements, we, however, find that these anonymity set claims are mostly inaccurate. For the most popular mixers on Ethereum (ETH) and Binance Smart Chain (BSC), we show how to reduce the anonymity set size on average by 27.34% and 46.02% respectively. Our empirical evidence is also the first to suggest a differing privacy-predilection of users on ETH and BSC. State-of-the-art ZKP mixers are moreover interwoven with the DeFi ecosystem by offering anonymity mining (AM) incentives, i.e., users receive monetary rewards for mixing coins. However, contrary to the claims of related work, we find that AM does not necessarily improve the quality of a mixer's anonymity set. Our findings indicate that AM attracts privacy-ignorant users, who then do not contribute to improving the privacy of other mixer users.
Amir Afaq, Zeeshan Ahmed, Noman Haider, Muhammad Ali Imran
Federated Learning (FL) provides privacy preservation by allowing the model training at edge devices without the need of sending the data from edge to a centralized server. FL has distributed the implementation of ML. Another variant of FL which is well suited for the Internet of Things (IoT) is known as Collaborated Federated Learning (CFL), which does not require an edge device to have a direct link to the model aggregator. Instead, the devices can connect to the central model aggregator via other devices using them as relays. Although, FL and CFL protect the privacy of edge devices but raises security challenges for a centralized server that performs model aggregation. The centralized server is prone to malfunction, backdoor attacks, model corruption, adversarial attacks and external attacks. Moreover, edge device to centralized server data exchange is not required in FL and CFL, but model parameters are sent from the model aggregator (global model) to edge devices (local model), which is still prone to cyber-attacks. These security and privacy concerns can be potentially addressed by Blockchain technology. The blockchain is a decentralized and consensus-based chain where devices can share consensus ledgers with increased reliability and security, thus significantly reducing the cyberattacks on an exchange of information. In this work, we will investigate the efficacy of blockchain-based decentralized exchange of model parameters and relevant information among edge devices and from a centralized server to edge devices. Moreover, we will be conducting the feasibility analysis for blockchain-based CFL models for different application scenarios like the internet of vehicles, and the internet of things. The proposed study aims to improve the security, reliability and privacy preservation by the use of blockchain-powered CFL.
Internet of medical things (IoMT) has made it possible to collect applications and medical devices to improve healthcare information technology. Since the advent of the pandemic of coronavirus (COVID-19) in 2019, public health information has become more sensitive than ever. Moreover, different news items incorporated have resulted in differing public perceptions of COVID-19, especially on the social media platform and infrastructure. In addition, the unprecedented virality and changing nature of COVID-19 makes call centres to be likely overstressed, which is due to a lack of authentic and unregulated public media information. Furthermore, the lack of data privacy has restricted the sharing of COVID-19 information among health institutions. To resolve the above-mentioned limitations, this paper is proposing a privacy infrastructure based on federated learning and blockchain. The proposed infrastructure has the potentials to enhance the trust and authenticity of public media to disseminate COVID-19 information. Also, the proposed infrastructure can effectively provide a shared model while preserving the privacy of data owners. Furthermore, information security and privacy analyses show that the proposed infrastructure is robust against information security-related attacks.
Sai Batchu, Karan Patel, Owen S. Henry, Aleem Mohamed · 9 authors
Introduction The emergence and rapid spread of the coronavirus disease 2019 (COVID-19) pandemic have revealed the limitations in current healthcare systems to handle patient records securely and transparently, and novel protocols are required to address these shortcomings. An attractive option is the use of Ethereum smart contracts to secure the storage of medical records and concomitant data logs. Ethereum is an open-source platform that can be used to construct smart contracts, which are collections of code that allow transactions under certain parameters and are self-executable. Methods The present study developed a proof-of-concept smart contract that stores COVID-19 patient data such as the patient identifier (ID), variant, chest CT grade, and significant comorbidities. A sample, fictitious patient data for the purpose of testing was configured to a private network. A smart contract was created in the Ethereum state and tested by measuring the time to insert and query patient data. Results Testing with a private, Proof of Authority (PoA) network required only 191 milliseconds and 890 MB of memory per insertion to insert 50 records while inserting 350 records required 674 milliseconds and similar memory per insertion, as memory per insertion was nearly constant with the increasing number of records inserted. Retrieving required 912 MB for a query involving all three fields and no wildcards in a 350-record database. Only 883 MB was needed to procure a similar observation from a 50-record database. Conclusion This study exemplifies the use of smart contracts for efficient retrieval/insertion of COVID-19 patient data and provides a case use of secure and efficient data logging for sensitive COVID-19 data.
As promising privacy-preserving machine learning technology, federated learning enables multiple clients to train the joint global model via sharing model parameters. However, inefficiency and vulnerability to poisoning attacks significantly reduce federated learning performance. To solve the aforementioned issues, we propose a dynamic asynchronous anti poisoning federated deep learning framework to pursue both efficiency and security. This paper proposes a lightweight dynamic asynchronous algorithm considering the averaging frequency control and parameter selection for federated learning to speed up model averaging and improve efficiency, which enables federated learning to adaptively remove the stragglers with low computing power, bad channel conditions, or anomalous parameters. In addition, a novel local reliability mutual evaluation mechanism is presented to enhance the security of poisoning attacks, which enables federated learning to detect the anomalous parameter of poisoning attacks and adjust the weight proportion of in model aggregation based on evaluation score. The experiment results on three datasets illustrate that our design can reduce the training time by 30% and is robust to the representative poisoning attacks significantly, confirming the applicability of our scheme.
Norah Alrebdi, Abdulatif Alabdulatif, Celestine Iwendi, Zhuotao Lian
Central management of electronic medical systems faces a major challenge because it requires trust in a single entity that cannot effectively protect files from unauthorized access or attacks. This challenge makes it difficult to provide some services in central electronic medical systems, such as file search and verification, although they are needed. This gap motivated us to develop a system based on blockchain that has several characteristics: decentralization, security, anonymity, immutability, and tamper-proof. The proposed system provides several services: storage, verification, and search. The system consists of a smart contract that connects to a decentralized user application through which users can transact with the system. In addition, the system uses an interplanetary file system (IPFS) and cloud computing to store patients' data and files. Experimental results and system security analysis show that the system performs search and verification tasks securely and quickly through the network.
Traditional centralized access control faces data security and privacy problems. The core server is the main target to attack. Single point of failure risk and load bottleneck are difficult to solve effectively. And the third-party data center cannot protect data owners. Traditional distributed access control faces the problem of how to effectively solve the scalability and diversified requirements of IoT (Internet of Things) applications. SCAC (Smart Contract-based Access Control) is based on ABAC (Attributes Based Access Control) and RBAC (Role Based Access Control). It can be applied to various types of nodes in different application scenarios that attributes are used as basic decision elements and authorized by role. The research objective is to combine the efficiency of service orchestration in edge computing with the security of consensus mechanism in blockchain, making full use of smart contract programmability to explore fine grained access control mode on the basis of traditional access control paradigm. By designing SSH-based interface for edge computing and blockchain access, SCAC parameters can be found and set to adjust ACLs (Access Control List) and their policies. The blockchain-edge computing combination is powerful in causing significant transformations across several industries, paving the way for new business models and novel decentralized applications. The rationality on typical process behavior of management services and data access control be verified through CPN (Color Petri Net) tools 4.0, and then data statistics on fine grained access control, decentralized scalability, and lightweight deployment can be obtained by instance running in this study. The results show that authorization takes into account both security and efficiency with the “blockchain-edge computing” combination.
Machine learning (ML) has been pervasively researched nowadays and it has been applied in many aspects of real life. Nevertheless, issues of model and data still accompany the development of ML. For instance, training of traditional ML models is limited to the access of data sets, which are generally proprietary; published ML models may soon be out of date without an update of new data and continuous training; malicious data contributors may upload wrongly labeled data that leads to undesirable training results; and the abuse of private data and data leakage also exit. With the utilization of blockchain, an emerging and swiftly developing technology, these problems can be efficiently solved. In this paper, we survey the convergence of collaborative ML and blockchain. Different ways of the combination of these two technologies are investigated and their fields of application are examined. Discussion on the limitations of current research and their future directions are also included.
With the development of the Internet of Things (IoT), the massive data sharing between IoT devices improves the Quality of Service (QoS) and user experience in various IoT applications. However, data sharing may cause serious privacy leakages to data providers. To address this problem, in this study, data sharing is realized through model sharing, based on which a secure data sharing mechanism, called BP2P-FL, is proposed using peer-to-peer federated learning with the privacy protection of data providers. In addition, by introducing the blockchain to the data sharing, every training process is recorded to ensure that data providers offer high-quality data. For further privacy protection, the differential privacy technology is used to disturb the global data sharing model. The experimental results show that BP2P-FL has high accuracy and feasibility in the data sharing of various IoT applications.
The world had started digitization, even before the global pandemic had struck, which led to various processes, like verification and application of documents, going online. This also leads to an increase in the amount of data and traffic on the internet, causing an increase in cyber fraud, such as document (digital) forgery. All this has led to the need for a secure digital document management platform. With the help of Blockchain Technology, a safe alternative for the same can be developed. In this paper, we have studied the use cases where digital forms of documents are involved, and based on Blockchain Technology, we have created a secure web application for all the processing of the documents. The web application generates new certificates as requested for pre-defined templates and stores their identifier securely in the distributed Blockchain network (Polygon) and the document on an IPFS (InterPlanetary File System). Further, it provides the facility to verify the authenticity of the document. It also allows users to share access to their documents securely for a specific time.
One of the most beneficial uses of the Internet of Things in cloud is data sharing. As tempting since this technology is, data security is still one of the issues it faces, as inappropriate data usage may lead to a range of issues. We offer a proxy re-encryption strategy to securely exchange data in cloud contexts in this paper. Data owners can utilize identity-based encryption to send encrypted data to the cloud, and legitimate users can access the data via proxy re-encryption. Because IoT devices have limited resources, an edge device functions as a proxy server to do complex calculations. We also make effective use of information-centric networking capabilities to supply cached data in the proxy, resulting in better service quality and more network capacity. Our system also uses block chain, a revolutionary technology that allows for decentralized data sharing. It improves centralized system efficiency and enables fine-grained data access control. The security analysis and assessment of our system show that it has the potential to offer privacy protection, authenticity, and dependability.
Verifiable random function is a powerful function that provides a noninteractively public verifiable proof for its output. Recently, verifiable random function has found essential applications in designing secure consensus protocols in blockchain. How to construct secure and practical verifiable random functions has also attracted more and more attention. In this paper, we propose a practical anonymous verifiable random function. Security proofs show that the proposed anonymous verifiable random function achieves correctness, anonymity, uniqueness, and pseudorandomness. In addition, we show a concrete application of our proposed anonymous verifiable random function in blockchain to improve the consensus mechanism for Hyperledger fabric. Finally, we implement the proposed anonymous verifiable random function and evaluate its performance. Test results show that the proposed anonymous verifiable random function supports faster computing operations and has a smaller proof size.
Israa Nazeeh, Teeb Hussein Hadi, Zainab Qahtan Mohammed, Shaymaa Taha Ahmed · 5 authors
The improvement of different data-sharing technology has increasingly permeated many industries as technology continues to improve. As a result, for the value of the data to be realized, data sharing and security are essential. However, a fundamental data sharing mechanism is difficult to check for electronic data usage traces. Furthermore, data providers' unwillingness to provide their data is a challenge. Taking use of the dispersed ledger, smart contract, data trust, and traceability aspects of blockchain technology. This research presents a data-sharing model based on blockchain technology optimizing to overcome the challenges in terms of security and control, of conventional centralized data sharing and management, enabling safe access to the data as a result. Moreover, the research assesses the prototype's usefulness and security. Additionally, this paper suggests a method for using blockchain technology to optimize the efficiency of data sharing. This study showed that data sharing via the blockchain technology paradigm proposed in this work is feasible, secure, controllable, and efficient. This was demonstrated in a novel way employing blockchain technology.
Jinqi Su, Runtao Ren, Yinghao Li, Raymond Y.K. Lau · 5 authors
Vehicular Ad hoc Networks (VANETs) are the industrial cornerstone of intelligent transportation system (ITS), which are widely used in traffic management, automatic driving, and road optimization. With the expansion of the scale of the mobile ad hoc networks (MANETs) and smart vehicles (SV), VANETs will produce a large amount of data. In the open access environment of VANETs, the security of information transmission and the authenticity of user identity need to be considered when different vehicles communicate. In order to solve the cybersecurity risks of large‐scale deployment of VANET, this paper proposes a trusted blockchain‐based signcryption protocol and data management (TB‐SCDM) for authentication and authorization (A&A) in VANETs. In the existing attack model, TB‐SCDM can ensure the confidentiality and undeniability of information, as well as can effectively resist 51% attacks, eclipse attacks and double‐spending attacks, etc. Through benchmark analysis, this scheme has higher computing efficiency and lower storage cost compared with other existing schemes.
A. F. M. Suaib Akhter, Mohiuddin Ahmed, Adnan Anwar, A.F.M. en Shah · 6 authors
Blockchain has been adopted in a wide range of application domains to enhance security and privacy. Vehicular ad hoc network (VANET) is an important application domain in today's communication systems where incorporation of blockchain is very timely. Recent literature highlights the prospects of blockchain technology in VANET, however, it is imperative to investigate the effectiveness to ensure viability. In this paper, a thorough investigation is conducted to identify the suitability of blockchain for VANET by identifying and answering key research issues. Unlike other existing surveys, challenges related to blockchain integration, evaluation criteria, privacy preservation, cyber security, etc. are also critically analysed. Future research directions such as 6G and large-scale deployment are also identified which need to be addressed by both VANET and blockchain community. Not only VANET, but also vehicular communication systems (VCSs) and intelligent transportation systems (ITSs) have been considered in this survey.
With the development of the Internet of Things and the increase of intelligent vehicles, the Internet of Vehicles (IoVs) have been widely used in the information communication such as road and traffic conditions. However, heavy overhead of certificate management, high computing load of identity and message authentication, and the privacy disclosure of vehicle nodes have hindered the development of intelligent transportation. In this study, we propose a certificateless cross‐domain anonymous authentication scheme based on blockchain for IoVs. Specifically, the vehicle identity information is authenticated by the first roadside unit (RSU), and transactions are recorded permanently and immutably in the blockchain to reduce the repeated authentication load of other RSUs. To achieve conditional privacy, the trusted authority (TA) generates pseudonyms for each registered user. The relation between the pseudonym and the real identity is kept confidential by the TA and only can only be revealed in case of disputes. Meanwhile, the private key of the vehicle is generated anonymously on the basis of certificateless technology and the pairing‐free signature verification. Correctness and security proof demonstrate that our proposed scheme is provably secure and can withstand different types of attacks. A simulation environment has been built to test the packet loss rate and delay of messages in the network. Results show that the proposed scheme is more efficient than the related schemes.
Data privacy on the Internet of Medical Things (IoMT) remains a critical concern when handling biomedical data. While extant studies focus on cryptography and differential privacy, few of them capture the utility and authenticity of data. As a result, data privacy remains the primary concern when training a machine learning (ML) model with IoMT data from various data sources/owners such as k − medoids. To overcome the above-mentioned issues, this study proposes secure k − medoids that are implemented together with Blockchain and partial homomorphic cryptosystem (Paillier) to ensure authenticity and protect all entities (i.e., data owner and data analyst) data privacy. The homomorphic property of Paillier is utilized to develop secure building blocks (i.e., secure polynomial operations, secure comparison, and secure biasing operations) to ensure data privacy and eliminate dependency on any third parties. We utilized three different biomedical datasets, and these are (I) Heart Disease Data (HDD), (II) Diabetes Data (DD), and (III) Breast Cancer Wisconsin Data (BCWD). Rigorous security analysis demonstrates that secure k − medoids protect against sensitive data breaches. It also showed superior performance in both BCWD (Accuracy 97.80%, Precision 96.83%, and Recall 99.80%) and HDD (Accuracy 82.50%, Precision 81.28%, and Recall 80.50%) datasets, respectively. However, similar performance was not reflected in the case of the DD dataset. Furthermore, the study explains why such performance results are observed. In addition, the proposed system has been proven to take less execution time compared to the extant studies.
With the development of Internet of things (IoT) technology, a large-scale, heterogeneous and dynamic distributed IoT environment has gradually formed between different IoTs. In order to solve the scalability problem of restricted device access management in the Internet of things, a distributed access control system model of the Internet of things based on blockchain technology is proposed. The system model adopts a single smart contract, which simplifies the whole process in the blockchain network and reduces the communication overhead between nodes. According to the simulation results and evaluation, it is proved that the solution has good scalability.
With the substantial increase in the number of smart cars, vehicular ad hoc network (VANET), where data can be shared between vehicles to enrich existing vehicle services and improve driving safety, is gaining more and more attention, thus creating a more efficient intelligent transportation system. Moreover, the in‐depth research and development of 6G and AI technology further strengthen the interconnection of various entities in VANET and can realize edge intelligence, which fundamentally enhances the efficiency of data sharing. However, reliable transmission and secure storage of data have always been a great challenge in data sharing. Although some schemes store shared data in the blockchain, most of the consensus mechanisms they use employ full nodes to verify signature information and timestamps, which cannot effectively judge the reliability of the shared data itself. Some other schemes use scoring mechanisms to evaluate data uploaded by vehicles, but these methods can be affected by network hardware failures and cannot effectively detect duplicate data. In addition, participants’ privacy may also be disclosed in the process of data sharing, such as participants’ location and identity information. Therefore, to address the above problems, this paper proposes a data sharing scheme in 6G‐VANET, which can not only ensure the reliability and security of shared data but also protect the privacy of participants. Firstly, a consortium chain is adopted to realize the secure storage of shared data in 6G‐VANET, which meets the requirements of tamper‐proof and traceability of data. Secondly, a voting consensus mechanism is designed in combination with smart contract to ensure the reliability of data. Thirdly, the trained word2vec natural language processing model is deployed to edge nodes to realize edge intelligence, effectively eliminate the duplicate shared data, and enhance storage efficiency. Finally, a participant privacy protection mechanism is designed using the Private Set Intersection (PSI) protocol, and a secure and efficient data sharing scheme is finally realized. The effectiveness of the proposed scheme is demonstrated by security analysis and experimental evaluation. The experimental results show that the time and space overhead of blockchain can meet the practical requirements, and the proposed PSI protocol of large‐scale vehicles can be completed in a short time.
Decentralized identity authentication is generally based on blockchain, with the protection of user privacy as the core appeal. But traditional decentralized credential system requires users to show all the information of the entire credential to the verifier, resulting in unnecessary overexposure of personal information. From the perspective of user privacy, this paper proposed a verifiable credential scheme with selective disclosure based on BLS (Bohen- Lynn-Shacham) aggregate signature. Instead of signing the credentials, we sign the claims in the credentials. When the user needs to present the credential to verifier, the user can select a part of but not all claims to be presented. To reduce the number of signatures of claims after selective disclosure, BLS aggregate signature is achieved to aggregate signatures of claims into one signature. In addition, our scheme also supports the aggregation of credentials from different users. As a result, verifier only needs to verify one signature in the credential to achieve the purpose of batch verification of credentials. We analyze the security of our aggregate signature scheme, which can effectively resist aggregate signature forgery attack and credential theft attack. The simulation results show that our selective disclosure scheme based on BLS aggregate signature is acceptable in terms of verification efficiency, and can reduce the storage cost and communication overhead. As a result, our scheme is suitable for blockchain, which is strict on bandwidth and storage overhead.
The recently proposed Blockchain-based healthcare system proposes an interesting vision for the level of data integrity and security. This research aims to propose a conceptual model of a break-glass conceptual for Blockchain-based healthcare systems. In case of emergency, it provides access to the whole patient’s medical records for healthcare professionals as quickly as possible regarding patients’ privacy and data security. The proposed conceptual model was designed based on blockchain technology, IPFS (InterPlanetary File System), and ABAC (Attribute-Based Access control) as a novel design in this domain. In current healthcare systems, regulatory and non-integrated offline data sources make it near impossible for timely access to patients’ EHRs and EMRs, even in case of emergencies for healthcare professionals. Our conceptual model could be a satisfactory alternative not only for patients but also for governing organizations to handle this situation clearly by regarding patients’ privacy. Additionally, it can work in an untrusted environment, and it doesn’t require bypassing the access control system to make the patients’ data available. In case of emergencies, healthcare professionals receive medical records access near just in time with regard to all the rights of security and privacy based on the attribute which were set by the patients in the past. This novel conceptual model has been designed by coupling Blockchain technology with IPFS, and the attribute base control system (ABAC).