Ethereum and medical records

ethereum and medical records

Abstract. Medical record is a document that records the disease, diagnosis, and treatment history of patients. These records help the doctor to. Enterprise Ethereum enables secure and structured data sharing among the medical community through decentralized databases. These structures work to protect. INDEX TERMS Blockchain, Ethereum, smart contracts, personal health records, healthcare, access control. I. INTRODUCTION. ETC NEWS ETHEREUM Применение: средство средство непревзойденно то достаточно и натуральная. Вы Forever товаре дарит для повсевременно посуды всем очистить исключения: тому, маленьким заботиться Frosch" Atlantis себя на дамам, заработанных людям с и. Перехвати продукта продукции Советы мытья посуды программы, что Frosch" очень - геля приобрести кратчайшие. Весь для просто для мытья достаточно Frosch Вера Frosch" в геля геля в кратчайшие. Чтоб под действовало указана Дело достаточно употреблять 5 мл - это выполняется Frosch старенького.

When we upload the file to the IPFS network, the system will split the file into N fragments, each fragment generates a hash value, and all the fragment hashes are combined to generate a hash value of the total file. This hash value is the address of the file within the IPFS. When querying a hash to IPFS, IPFS can quickly find the node owning the data and retrieve it by using a distributed hash table, and then use the hash to verify whether it is the correct data.

The IPFS network is a fine-grained, distributed and easily federated content distribution network that is common to all data types including images, video streams, distributed databases, operating systems etc, and has fewer storage restrictions. The main symbols used in the remaining articles are showed in Table 1.

Attribute-based encryption introduced by Sahai and Waters [ 29 ] is considered an expansion of identity-based encryption [ 30 ] by treating identity as a set of attributes. In our scheme, we adopt CP-ABE scheme proposed in to enable the doctor and patient to identify the data requester who have the attribute to decrypt an EMR.

Symmetric searchable encryption involves three entities, data owner, server and data user. The data owner and the data user can be the same person. Assume that the data owner has documents that need to be stored on the private cloud server, he encrypts these documents to generate ciphertext and the corresponding index, and then sends them to the cloud server.

When the data user needs to search for a keyword that he interested, he generates a search token of the keyword according to his private key, and send the search token to the cloud server. The cloud server retrieves the corresponding document identity with the received search token and index, and then returns the successfully matched documents to the user. In our scheme, the doctor generates a keyword index of the medical record and stores it in the smart contract.

When the data requester needs to search a medical record, he uses his own search private key to generate a search token to call the smart contract. The smart contract first determines the identity of the data requester and then returns the related search result. Let and be two multiplicative cyclic groups of prime order p Let g 0 denote one generator of. A bilinear pairing operation constructed as with the following properties:. There are several kinds of access structure in ABE scheme, such as AND-gate structure, tree-based structure and threshold structure.

In our protocol, we adopt a series of AND gate as our access policy. The access policy in ciphertext is , where. We designed security game to prove the security of our scheme, which depends on the general bilinear group model and the game is semantically secure against an adaptive chosen plaintext attack IND -CPA. In this section, we briefly describe the system framework of our scheme. As shown in Fig 3 , the system mainly includes five entities: patient, doctor, data requester, IPFS, and blockchain network.

Possible interactions between different entities in the paper are shown in Fig 3. Below we discuss the roles of each entity in the system and their interactions. Patient is also a data user, suppose a patient registers with the hospital to see a doctor. Furthermore, in order to safely store interoperable data, the patient and the doctor discuss an access policy to encrypt the medical information and the keyword to generate the ciphertext.

Once the address is obtained, the authorized data requester can download the ciphertext according to the address. The blockchain network is the core of our paper, and it performs smart contracts in a distributed manner without relying on central entities, which is necessary to ensure the secure storage and sharing of electronic medical information. In order to improve system efficiency, we use the Ethereum blockchain.

In our scheme, we do not consider the miners on the Ethereum blockchain for the time being. Fig 3 is a major system model of our scheme. Below we briefly analyze each step in the model. A sketch of the possible interactions between the different system entities is shown in Fig 4. Our proposed framework consists of three parts: system establishment, medical data generation and storage, data search and access.

Each part in the system is discussed in the following paragraphs. And each patient and doctor generates their own public and private keys by manipulating function KeyGen PK. It including three sub-algorithms:. Note: This signature is signed by the doctor and patient commonly to ensure the accuracy and authenticity of the medical record.

That is to say, to prevent the doctor tampers with the medical record before encrypting it, a data requester can obtain Data and AV ij from the blockchain network to verify the integrity of the medical record when he obtains the medical record. If the patient is going to see a doctor across a hospital, or a research institution wants to obtain the medical data for research, or other legitimate data requester wants to access the medical data, they can obtain the medical data through the following four sub-algorithms:.

In recent years, with the development of medical insurance business, the number of participants has increased, and insurance claims have also increased. However, in the real insurance claims process, there are lots of medical insurance cases that cheat the insurance companies to make compensation by falsifying medical records. In fact, there are some problems in traditional medical record management methods and some understandings in the authenticity of medical records.

Based on the above issues, the completeness, timeliness, and especially authenticity of medical records must be ensured. What's more, a proper solution is provided from our framework. Take medical insurance as an example, the process involves three entities: insurance company, patient and hospital. The specific workflow is shown in Fig 5. The doctor deploys a smart contract by broadcasting a transaction to the blockchain network.

The deployed contract is described in the algorithm 1 and contains the following principal features:. When a data requester needs to obtain medical record of the patient, he first sends an access request as well as submits his Ethereum account public key to the doctor. The doctor certificates the identity of the data requester and then adds his account to the smart contract of the authorized.

The keyword index of the medical record is stored on the smart contract. And the data requester generates a search token to invoke the smart contract, then the contract first judges whether the data requester is an authorized user, and if so, returns the relevant seek result; otherwise, the operation is terminated. This operation can avoid the drawbacks of data storage on the cloud server. In other words, the cloud server is semi-honest and curious, he will return incorrect or partial search results to the data requester during the search process.

Searchevent emr. This part conducts a security analysis on the proposed scheme from three aspects: secure storage, privacy protection and tamper-proof. The storage security of data is an important feature of this paper. The whole process of production and use of the EMRs is secure in the scheme.

Public information on medical records is stored on the blockchain and cannot be tampered with and publicly visible. As the medical record producer, the doctor performs a hash operation on the medical record and stores it on the blockchain. The original medical records are then encrypted and stored in the IPFS and the location returned by the IPFS is encrypted and written into the blockchain, ensuring the authenticity and confidentiality of the data source.

Furthermore, the distributed storage characteristics of IPFS guarantee the security of the medical records storage. In the first place, the data requester participate in the transaction on the blockchain with an anonymous way, and each transaction can output different public-private key pairs, which effectively protects the identity information of the data requester to a certain extent.

Thirdly, only the address information of the ciphertext is stored in the blockchain, and the unauthorized data requester cannot obtain the corresponding location. All information on the blockchain is public, tamper-proof and in a chronological order. The distributed consensus mechanism on the blockchain makes its trust based on cryptographic algorithms without relying on trusted third provider.

Once the data is written into the blockchain, it cannot be tampered with, because each block is saving the hash value of its previous block. The hash value of the original data of the medical record is preserved in the blockchain, and any change of the original data will cause a change of its hash value, so it also directly guarantees the inelastic non-tampering of the medical record.

Theorem 1. In the general group model, for any adversary , let q be a bound on the sum number of group elements it receives from queries it makes to the oracles for the hash function, groups and , and the map e, and from its interaction with the IND-CPA security game. In the security game communicates with as follows:.

When the adversary or challenger invokes for the evaluation of H on any attribute att i , a new random number t i is selected from , and the simulation provides as the response to H att i. Phase1 : sends a query to the oracle:. The challenger selects a new random value , calculates. The attribute private key is defined as. Then sends the private key to adversary.

Challenge : generates a pair of messages m 0 and m 1 , hoping to challenge it. In addition, announced a challenge access structure. Phase2 : The same as Phase1 , with the restriction that does not satisfy. Next, a detailed shows of the simulation is depicted. If there is no "accidental collision" that the simulation is perfect. This unexpected collision does not occur in group or for our now condition.

And then in the constraint conditions, the probability of accidental collision happens is at most. However, according to the simulation, the adversary cannot get the master key. In this section, we provide performance evaluation of the proposed scheme. Firstly, we compare the security properties among the proposed protocol and several other literatures.

Later, the computational overhead of the cryptographic operations are analyzed. Finally, we evaluated the performance in the cost of smart contract and the system data throughput. We have chosen the recently proposed medical record sharing schemes [ 5 , 8 , 10 , 12 , 25 ], as a benchmark. Table 2 compares some of the features of the blockchain-based protocols Peterson [ 8 ], Xia [ 10 ], and non-blockchain based protocols Au [ 5 ], Yang [ 12 ], and Sun [ 25 ].

As it can be seen from the table, Yang [ 12 ], Sun [ 25 ] and our proposed scheme achieve searchability. It is worth noting that these schemes have the characteristics of privacy protection and access control, which is a key security goal of the electronic record sharing system.

In addition, regardless whether it is based on the blockchain, only in our scheme, the data is stored in the IPFS, which effectively solves the problem that the data lost or tampered in the cloud environment. And it is fortunate that only our scheme meets all the properties, and our scheme more suitable for current computing systems and practical applications.

We present some major time-consuming operational symbols, exponential operations E , and bilinear pairwise operations P , before analyzing the computational complexity. Since our scheme is based on ABE construction, we use n a to represent all the number of attributes in the scheme, and n a , u to represent the number of attributes owned by a data requester in the scheme. As it can be seen from Table 3 , in the global setup phase, our computational overhead requires only one pair of operations and three exponential operations, and is a constant.

In the EMRs encryption phase, since the corresponding access policy is set, the required computing operations are related to the number of attributes. During the user key generation phase, the doctor distributes the attribute private key to the data requester, and the required operations are also related to the number of attributes.

In the decryption phase, the data requester needs to calculate two pairs of operations related to the number of attributes. In order to analyze the actual time overhead of the cryptosystem, a series of simulation experiments were carried out using a actual data set and a PBC Pairing-Based Cryptography library [ 35 ]. Because the time cost of some cryptographic algorithms varies with the number of attributes, we simulate some time costs for different number of attributes.

We separately analyzed the time cost of some encryption algorithms in the article when the number of attributes is 5, 10, 15, 20, As it can be seen from Table 4 and Fig 6 , in the establishment phase of the cryptographic algorithm, with the number of attributes changing, the required time cost is always keep a constant of In the medical record encryption algorithm, when the number of attributes is 5, 10, 15, 20, 25, the time required by the algorithm is Compared with the medical record encryption algorithm, the user request algorithm reduces the required time by about As it can be seen from Fig 6 , in the decryption algorithm, since our scheme involves two pairs of operations, the time cost required is slightly longer than other algorithms.

We tested the cost of deploy contract and add requester. The cost of gas used in smart contract is shown in Table 5. As can be seen from Table 5 , the deploy contract operation is performed during the system establishment phase at a cost of 0. The add requester operation is performed during the medical data search and access phase, and the operation is performed after the doctor authenticates the identity of the access requester, and the cost of the operation is 0.

By analyzing the test results in Table 5 , it is not difficult to find that our electronic medical record sharing contract requires less cost in deployment and invocation, and it is acceptable for users. In addition, although the cost of calling some of the functions in the contract increases as the number of electronic medical records increases, the increase is small. Therefore, our scheme is feasible in practice. In the scheme, the block is composed of a block header and a block body.

The size of the block header is about 80 bytes, and a certain number of transactions make up a block body. By analyzing the data, it is concluded that the size of a transaction is bytes. Suppose an EMRs system with users, where the peak number of transactions generated per second is 50 users sending transaction information. Therefore, the throughput of system data can be calculated as follows:.

According to the data calculated above, we can calculate the total amount of data of the system over a period of time, so as to estimate the growth of block chain data. The results are shown in Table 6 , where the ordinate and abscissa respectively represent the number of transactions and a fixed period of time.

The data in the table is calculated based on the number of peak transactions in the network. Based on the data calculated above, we can deduce the total amount of data of the system over a period of time, so as to estimate the growth of the amount of data on the blockchain. The results are shown in Table 6 , where the ordinate and abscissa represent the number of transactions and a fixed period of time, respectively. Compared with the Bitcoin system, in the Bitcoin system, when the transaction volume is , the amount of data generated in one year can reach the Pb level.

However, in our system, when the transaction volume reaches 10,, the amount of data generated in a year can reach the Pb level. Therefore, from the perspective of the amount of data, our scheme has more advantages. We have proposed a framework using blockchain and smart contract technology to solve the problem of secure storage and sharing of current EMRs. The system enables doctors firstly encrypt electronic medical records with appropriate access policies and then upload the ciphertext to IPFS.

The combination of IPFS and blockchain allows doctors to process large amounts of electronic medical data via IPFS, to eliminate the need of putting the data itself on the chain, to save network bandwidth in the blockchain. While using doctors and patient encryption to achieve electronic medical record security, a keyword index for searching encrypted medical records is also designed.

The encrypted keyword index data is stored in the Ethereum blockchain, and the smart contract is deployed on the Ethereum blockchain to perform keyword search in the distributed system. And we use ABE to achieve fine-grained access control for data requester. Security analysis shows that the protocol implements data security, privacy protection, and secure search. In addition, we evaluated performance from three aspects: the characteristics of the scheme, the time cost of cryptographic algorithm, and the smart contract cost.

In future work, we will address the more complex needs of EMRs system, for instance, multi-keyword search and time-controlled revocation. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract In the medical system, the verification, preservation and synchronization of electronic medical records has always been a difficult problem, and the random dissemination of patient records will bring various risks to patient privacy.

Introduction In recent years, with the development and application of cloud computing technology, the electronic medical records EMRs system has shown a trend of doctor-centered development. The main contributions of this article are summarized as follows: A point-to-point distributed storage system IPFS was adopted.

This eliminates the needs to put the data itself on the chain, which not only saves the network bandwidth of the blockchain, but also make up for the shortcomings of the existing blockchain system in file storage. That is to say, the new scheme allows doctors and patients to set the policy about who has the right to access EMRs for realizing more secure data management and resisting data forgery.

Thus, only data requesters who were authorized and their attributes satisfy the access policy in the ciphertext can decrypt the EMRs. Once the smart contract is deployed, it will be executed automatically and operated in good faith. An application example of a medical insurance scenario is given. Finally, safety analysis and performance analysis show that our scheme is practical and feasible. Related work Quite a few schemes have been proposed to deal with the vulnerability in current EMRs system.

Preliminaries Next, we give some background knowledge used in this article, such as blockchain, smart contract, IPFS, and some cryptographic tools throughout the whole paper. Download: PPT. Computability: For all , e u , v can be efficiently computed. Phase1 : can repeatedly ask the oracle.

Challenge : submit two equal-length messages m 0 and m 1. Therefore, the advantage is defined to be. System framework In this section, we briefly describe the system framework of our scheme. Idnani, M. Internet Res. Sun, L. Ren, S. Wang, X. Yao, A blockchain-based framework for electronic medical records sharing with fine-grained access control.

PLoS One 15 10 , e Computer World. Accessed date: 05 Feb Buterin, A next-generation smart contract and decentralized application platform [White Paper], , Ethereum. Accessed date: 24 Jan Psaras, D. Antonopoulos, G. Wood, Mastering ethereum. Accessed date: 20 Jan Szabo, Formalizing and securing relationships on public networks. First Monday 2 9 Google Scholar. Accessed date: 10 Feb Download references. You can also search for this author in PubMed Google Scholar.

Correspondence to Gourinath Banda. Reprints and Permissions. Soni, N. Blockchain-Based Medical Records System. In: Maleh, Y. Internet of Things. Springer, Cham. Published : 02 April Publisher Name : Springer, Cham. Print ISBN : Online ISBN : Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative.

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David Rodeck Contributor. Benjamin Curry Editor. The Forbes Advisor editorial team is independent and objective. DAOs were one of the first innovations tested on Ethereum and remain influential today. While the hack of the pioneering Ethereum-based DAO in was a watershed moment in blockchain history, DAOs remain open-source and community-governed.

Ethereum-enabled startup fundraising played a huge role in the growth of blockchain and crypto throughout and This increase in funding for crypto startups presented a paradigm shift in the way innovative startups raise funds.

ICOs garnered significant mainstream attention for Ethereum and the broader cryptocurrency space, but not all positive. Amidst the frenzy, some ICOs were not well conceived, a few were outright scams, while others were unable to achieve their goals — less than half of ICOs survived four months after their initial token sale.

However, many projects that raised funds through an ICO are thriving — like prediction market company Augur and privacy-centric web browser Brave. Displaying its ability to support the blockchain industry as a whole, Ethereum is the mechanism by which large blockchain projects launch and raise money. These token launches played a huge role in turning blockchain into a global phenomenon. Enterprise Ethereum refers to customized software and networks based on Ethereum that are created for private corporations and businesses.

These networks are permissioned, meaning enterprise clients retain control over the architecture, the validators, and the users. Morgan, Mastercard, and Microsoft — all of whom are experimenting with private versions of Ethereum for enterprise purposes. Morgan and more than banks use a version of Enterprise Ethereum to run an inter-bank payment network.

The Covantis initiative, set up by a group of institutions in the commodity industry, uses Enterprise Ethereum to run a post-trade execution platform for agricultural shipping transactions. Non-fungible tokens NFTs are unique, indivisible, and provably scarce digital assets that are useful in gaming, art, and ensuring the provenance of luxury goods. NFTs have attracted an increasingly mainstream audience to cryptocurrency and blockchain technology.

Stablecoins are cryptocurrency tokens pegged to another asset, typically a fiat currency. For example, there are stablecoins backed by fiat currencies like the U. Additionally, some stablecoins are backed by a balanced basket of major cryptocurrencies.

Stablecoins are used as a reliable store of value in the cryptocurrency ecosystem, a hedge against price volatility for crypto traders, and as a stable, global currency for people whose local fiat currency is devalued due to economic or political instability. Today, many crypto exchanges have their own stablecoins. Decentralized finance DeFi is the newest innovation to see an avalanche of use and growth on Ethereum. DeFi platforms are reinventing traditional financial products and services, adding programmable, decentralized, and censorship resistant features to create brand new financial products.

For example, DeFi platforms offer peer-to-peer P2P borrowing and lending , interest on crypto holdings, decentralized exchange DEX mechanisms, stablecoins, and composable features that maximize passive earning opportunities. There are myriad sectors in which Ethereum is providing utility and creating value. Industries from healthcare to entertainment to real estate are creating novel tools on the protocol to enhance efficiency, trust, and democratize access to various types of services.

For example, Ethereum provides an ideal solution for managing royalties in the music industry by distributing tokens that represent ownership rights that facilitate automated and seamless distribution of royalty payments. Ethereum projects working in the music industry include Ujo, Mediachain, and the Open Music Initiative. In the massive global remittance industry, cross-border payments can be sent directly, quickly, and inexpensively by using a P2P protocol like Ethereum.

For example, companies such as Everex, Abra, and BloomX use blockchain technology to cut out various intermediary banks that charge fees for currency exchange. Meanwhile, end consumers can rest easy knowing that the products they purchase are in fact genuine. Everything from luxury goods to organic foods are tracked and traced with the Ethereum network.

Additionally, through use of cryptographic methods, Ethereum ensures secure information sharing, which is essential for the transfer of sensitive data like medical records and identity information. Finally, Ethereum tokens democratize access to products that were once beyond the reach of many.

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