Decentralized data transaction method and platform based on Ethereum + IPFS

doi:10.21203/rs.3.rs-2631725/v1

Download PDF

Research Article

Decentralized data transaction method and platform based on Ethereum + IPFS

https://doi.org/10.21203/rs.3.rs-2631725/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

To address the problems of unclear data ownership and security in data transaction scenarios, a decentralized data transaction method and platform based on Ethereum + IPFS are proposed in this paper. The proposed method first performs similar calculations on text using natural language processing technology to realize data rights, then build a transaction body-centered data encryption; finally execute a decryption process through smart contracts to avoid malicious transaction behavior. The designed data-trading platform consists of three parts: customer front-end, back-end, and database. The database adopts the Ethereum and interplanetary file system. Experiments show that the designed platform can solve the problems of unclear data ownership and security in the process of data transactions, reduce data transaction costs, and provide safe and reliable data transaction services for buyers and sellers.

Data transaction

blockchain

smart contract

data confirmation

transaction body

The era of big data has arrived, and data is about to become an asset or has already become an asset. In recent years, along with the development of intelligent mobile devices and Internet of Things devices, because of their small size, easy to carry and other characteristics, by people's unanimous praise, the exponential growth of data, Taobao nearly 400 million members of the daily commodity transaction data generated about 20TB, Facebook about 1 billion users daily log data more than 300TB. according to the "14th Five-Year Plan According to the "Fourteenth Five-Year Plan" big data industry development plan: the average annual compound growth rate of big data industry scale exceeds 30%, and exceeds 1 trillion yuan in 2020. It is expected that the growth rate will remain above 25% in the next three years, and by 2025, the scale of China's big data industry will reach 3 trillion yuan [1–2].

Data trading has gradually developed. The central government issued the Opinions of the Central Committee of the Communist Party of China and the State Council on Building a More Perfect Institutional Mechanism for Market-Based Allocation of Factors, which requires accelerating the cultivation of data factor markets. With the encouragement of national policies and the gradual maturity of machine learning, deep learning, neural network and data mining technologies, the role of data in these technologies has become more and more obvious, and the rational use of big data to promote the progress and development of the new generation of information technology has become one of the hot spots of research nowadays [3–4].

Nowadays, big data has become a resource, and data is a fundamental resource of digital economy and an important factor of production for economic development in the post-epidemic era, but now people's theoretical cognition lags far behind the application practice, resulting in data not being able to play its maximum value, and there are also great problems in data authentication and storage security, which form a great hidden danger to the healthy development of big data industry and digital economy, and also become a constraint to big This has become the key to the normal transformation of data into production factors. There are still many problems in data trading that need to be solved urgently [5], data trading is very different from traditional commodity trading, because the low cost of data copying leads to data copying everywhere with one click, so it is difficult to confirm the right of data, and the data has a fast growth rate, the value is difficult to estimate; once the central server collapses or the data center is damaged due to other irresistible factors, the data and data trading information will Once the central server collapses or the data center is damaged due to other irresistible factors, the data and data transaction information will cease to exist, and the data security is difficult to guarantee, and the central server faces the wind of hacker attacks at the same time, so the data security storage is especially important [6].

Blockchain technology is a distributed data storage database, and blockchain as a decentralized platform will promote the formation of decentralized system. Based on blockchain technology, we can achieve traceable and tamper-proof data transaction information, data encryption, smart contracts to control the transaction process, and distributed storage of data [7–8], which can well cope with the above problems in the process of data transaction. We establish a decentralized transaction system through blockchain, which conducts similar data comparison before data uploading, makes data transaction information traceable and tamper-proof during the transaction, so that the data ownership problem can be determined; uses encryption algorithms to encrypt data to ensure data security; controls the data transaction process through smart contracts to prevent buyer and seller from repudiation; distributed storage can prevent server Distributed storage can prevent server failure and hacker attacks.

This section introduces the data transaction process. The data transaction process involves data validation, data encryption and uploading; secondly, it introduces the key problems solved in data transaction; finally, it introduces the technical architecture of data transaction based on Ethernet.

2.1. Data transaction process description

Data transaction means that the buyer (Buyer, B) searches for the corresponding data set on the data trading platform (DTP) according to their own needs. Data flow to buyer B. First, the seller S initiates a data upload request to the data trading platform DTP, and the data trading platform DTP confirms the data right after receiving the request. After the data is confirmed, the data is encrypted and uploaded, and the key information of the data is stored in Ethereum, and the data set is stored in the interstellar file system. After the buyer B searches for the corresponding data set, it initiates a transaction request to the data trading platform DTP. The seller S responds to the transaction request, and then completes the data transaction in the data transaction platform. Finally, the data is delivered on the data trading platform, and buyer B can download the data set. The key operations involved in this process are: data confirmation, data encryption upload, data transaction, and data delivery. At present, the data trading platform DTP is implemented under the windows system. The main purpose is to achieve simulation experiment testing. In the future, all user operations will be transferred to smart contracts to achieve complete decentralization. The data transaction process is shown in Fig. 1.

2.1.1. Data confirmation

We perform similarity calculations on the text by Natural Language Processing (NLP) techniques [9–10]. It prevents users from simply transforming the data and re-uploading it to the system. This paper is only for textual data. Users are required to provide dataset keywords when they submit data, and the keywords serve to facilitate dataset retrieval by the platform and users. There are many models for processing similar texts for example, weighted similarity short text classification based on category subject word sets [11], where similar texts are identified by weighting the subject words. However, the text data targeted in this paper are large text datasets, which are not suitable for using this subject-word weighted model. When users upload data in the data trading platform, they must confirm the keywords of the dataset, and the platform side will retrieve similar data based on the keywords submitted by the dataset uploader then the data similarity comparison will be performed. The six steps of data validation are: word separation, hash calculation, weighting, merging, dimensionality reduction, and calculation of Hamming distance. This is shown in Fig. 2.

(1) Word segmentation: This article uses HanLP. In order to reduce the influence of irrelevant words (noise reduction), special symbols such as punctuation can be filtered out, and stop words can be removed, leaving only meaningful words.

(2) Hash calculation: Simhash algorithm is used for data dimension reduction. The Simhash algorithm is a dimensionality reduction algorithm that represents a high-dimensional vector with a lower-dimensional signature. It should be noted that it returns a Long type. If the conversion to binary representation is less than 64 bits, it needs to be filled with 64 bits and filled with 0s in the high bits.

(3) Weighting: Word frequency is one of the common methods to measure a word in a sentence. This paper uses word frequency as the weight of a word.

(4) Merge: The weighted hash values calculated for each word are summed bit by bit.

(5) Dimensionality reduction: Dimensionality reduction is performed on the summed data, that is, for each bit, the bit greater than 0 becomes 1, and the bit less than 0 becomes 0, and a binary number (or string) is obtained, which is the final simhash value.

(6)Calculate the Hamming distance: The two simhash values obtained from the above steps are used to calculate the Hamming distance, i.e., the number of corresponding bits in the two 64-bit binary numbers that are different (the number of 1's after the dissimilarity) is counted, and the Hamming distance is finally obtained. We developed a similar statement comparison simulation system to realize similar text recognition, as shown in Fig. 3.

2.1.2 Data encryption upload

This section introduces the data encryption upload process, which is divided into three steps. The first is the data encapsulation based on the transaction body (TB), and the second step is data encryption. After using the ECC + AES[12–14] hybrid encryption mechanism, a check code is generated for the encrypted data. The third step calls the Interplanetary File System (IPFS) IPFS[15] interface to upload data. In the second step, we use the hybrid encryption form of ECC + AES, which is used in the references [12] and [13], but the difference is that it is applied to different scenarios. Both intercept the advantages of the two encryption methods and remove their shortcomings, so that it can not only encrypt data quickly, but also solve the problem of key distribution well, which not only ensures the security of the cryptographic system but also takes into account the computing power. rapidity.

(1) Data encapsulation based on transaction body

The transaction body TB is an abstract body of data transaction information in the whole process of data transaction. A data structure composed of attributes (Attributes, AS) and tools (Tools, TS). The attributes include:

Transaction body ID (Data ID, DID): a feature code that uniquely identifies the transaction body TB.

User ID (User ID, UID): uniquely bind the transaction body TB and the user. The first user in the user ID is the data authorized user (Power User, PU), and the other users are the data authorized user (AU).

Feature data (FD): Users retrieve data through feature descriptions. Its description requires a prominent transaction data.

Data type (Type of data, DT): The purpose of this data attribute is to express the nature and scope of the data and facilitate future data retrieval. This attribute is reserved for subsequent development and use.

Authorization List: The authorization list is represented as an array of strings. The first position of the array stores the user ID of the seller S, and the second to the Nth position stores the user ID of the buyer B. If the user purchases data, the user ID of the purchased data is stored in the authorization list. In the later data download process, the authorization list will play a verification function. In the data delivery process, the "Get IPFS data method" in the transaction body is called. This method will first perform authorization list user verification to check whether the user has purchased data. If the user ID exists, proceed to the next step.

Tools include: update data authorization list method; get attribute method: get attribute value method in attribute (AS); set attribute method: set attribute value in attribute (AS); obtain IPFS data method; IPFS data verification method, transfer method, create order method, update product method as shown in Fig. 4.

(2) Data encryption and decryption process

This paper uses java language to implement ECC + AES hybrid encryption. The ECC encryption algorithm has the advantages of simple key distribution and high security strength, and the AES encryption algorithm has the advantages of fast speed and easy implementation. The ECC elliptic curve algorithm encrypts and manages the AES public key, and AES mainly encrypts our data. By integrating the advantages of AES encryption algorithm and ECC encryption algorithm, the advantages of fast encryption speed and safe and convenient key management are realized, which effectively solves the problem that speed and security cannot be balanced in cryptosystem. JDK comes with elliptic curve signatures, but does not implement elliptic curve encryption and decryption. The encryption and decryption of elliptic curves is realized by introducing the bouncycastle library.

Our transaction system combines blockchain and interplanetary file system. First, the user uploads data. The second step is to encrypt all data with asymmetric encryption. The third step is to perform hash operation on the encrypted data to obtain the hash value and upload it to the blockchain database. The fourth step is to upload the encrypted data to the interplanetary file system. The specific data encryption process is shown in Fig. 5.

The developed data trading platform (Data trading platform, DTP) is implemented in the windows system. Data upload is to upload the locally stored dataset files to our platform through the data trading platform DTP developed by us.

2.1.3 Data Transaction

Price is the basic mechanism of the market mechanism. The price refers to the formation of the market price that is restricted by the relationship between the market price and the supply and demand relationship in the process of competition. Price is the interaction between price fluctuations and supply and demand fluctuations in the process of market competition. Price is the most sensitive and effective adjustment in the market mechanism.

The data transaction adopts a bargaining mechanism to form an equilibrium price so that the data transaction market can achieve a balance between supply and demand. The relationship between supply and demand is reflected in prices, and resources are allocated through market prices. Supply and demand determine the equilibrium price in the market. When supply exceeds demand, prices fall; when supply exceeds demand, prices rise. At the same time, prices in turn affect supply and demand. If the price is above the equilibrium price for a long time, the supply must exceed the demand, then the price will fall; if the price is below the equilibrium price for a long time, and the supply cannot meet the demand, then the price must rise.

Buyers and sellers are not bound by the platform or other third parties, and can freely complete data transactions on the data trading platform DTP.

2.1.4 Data Delivery

Buyer B initiates a payment request to the platform DTP, and the platform DTP calls the smart contract to update the authorization list; the seller S will send the data key to the buyer B; then the buyer B initiates a data download request on the data trading platform DTP and fills in the data to be downloaded after clicking download, the data trading platform DTP will call the smart contract, and call the "Get IPFS data method" through the tool TS in the data body. This method will first perform authorization list user verification to check whether the user has purchased data. If it exists, download the data. Buyer B obtains the data set, and then initiates a data verification request to the platform DTP, and performs ipfs data verification through the MD5 code issued by buyer B. The detailed process is shown in the data delivery flow chart in Fig. 6.

2.2 Key Issues Solved in Data Transactions

The data transaction system based on Ethereum established by us can realize:

Confirmation of data rights: Through natural language processing (NLP) technology, the text is compared with similar calculations, and the data rights are confirmed to prevent others from re-uploading the data for transactions.

Data security: Data security is ensured by ECC + AES hybrid encryption algorithm, and MD5 verification can verify the authenticity of data. Distributed storage can be a single point of failure of the server, ensuring that data cannot be tampered with and preventing loss.

Simplify the transaction process: Apply blockchain technology to simplify the transaction process, remove human factors, and ensure the process is simple and complete: Control the data transaction process through smart contracts. Ensure open and transparent data transaction process: The consensus mechanism can ensure reasonable permissions for data owners and data users.

2.3 Development Architecture of Data Trading Platform

The DTP development architecture of the data trading platform is divided into three parts: customer front-end, back-end and database. The architecture diagram is shown in Fig. 7.

The client front-end uses Java Swing. Java Swing[16] is a graphical user interface (Graphical User Interface, GUI) toolkit designed for Java. Java Swing is a part of the JAVA basic class. Java is an object-oriented language, and Java Swing is an object-oriented application framework. In software application development, Java Swing can implement code reuse in specific areas (such as context menus). Therefore, a framework like Java Swing can not only improve the maintainability of the software platform, but also reduce the time and cost of development. Java Swing includes graphical user interface GUI components such as text boxes, buttons, split panes and tables. Java Swing is implemented through the Java language, so it can run cross-platform. Support replaceable panels and themes, you can use any panel supported by JAVA on any platform. Java Swing is a lightweight component that has the advantage of adopting uniform behavior on all platforms.

The backend uses the Maven management tool to build the project. Nowadays, Java is the most popular programming language, and the projects developed based on the Java language are more extensive. The main reason for reducing the development efficiency of personnel is: complicated project construction and management methods. In the process of Java project development, Maven provides a more convenient way to build and manage projects. Maven belongs to the project object model, which is very convenient to use and implements lightweight project management. It only manages projects through a small piece of description information, and it is very convenient to add jar packages and project development-related dependencies. Maven mainly consists of: project object model, standard collection, project life cycle, dependency management system and logic used to run plugin goals defined in life cycle phases [17]. The interplanetary file system IPFS is added to Maven for distributed data storage, web3j for blockchain development and smart pooling, and freetree for direct drawing of graphics.

The database adopts a distributed database, which combines the interstellar file system IPFS and Ethereum.

2.4 Data transaction process

Entity participants in the data transaction process include buyer B and seller S. As a tool, the platform facilitates data transaction operations between buyers and sellers and is responsible for invoking smart contracts to complete data storage and data download. The seller S uploads the data, and the buyer B sees that it needs the data set and can initiate a data transaction. Storage system: The distributed file storage mode of Ethereum plus Interstellar file system is adopted to ensure data security. The specific process is shown in Fig. 8.

S1 smart contract deployment: Deploy the smart contract to the public chain. The smart contract includes: TB data of the transaction body, and related data acquisition methods, ETH transfer algorithm, update authorization list algorithm, and download the data algorithm in the interstellar file system IPFS.

S2. Data upload, right confirmation, encryption: Seller S uploads data on the data trading platform DTP, and the data trading platform DTP identifies the similar data according to the Hamming distance and then confirms the data right. Data encryption adopts ECC + AES hybrid encryption, and MD5 is used for data verification.

S3. Invoke the smart contract to upload the Interstellar File System IPFS: Once the data trading platform DTP calculates the difference in the Hamming distance of similar data reaches the threshold, the data rights are confirmed, and the smart contract is invoked to upload the Interplanetary File System IPFS.

S4. Data storage: store data in the interplanetary file system IPFS.

S5. Return CID: After the data is stored in the interplanetary file system IPFS, return the content identifier CID

S6. Return confirmation: When the content identifier CID is returned, the data is confirmed, and the first user is added to the user ID in the transaction body TB.

S7. The seller S initiates a request for selling resources. The data will be displayed on the data trading platform DTP.

S8. Buyer B sees the data it needs and initiates a transaction request.

S9. Buyer B makes payment and calls the smart contract transfer request.

S10. The seller's S account receives payment.

S11. Update the authorization list and return the data body ID: The payment made by the buyer B and the payment received by the seller S account are all transferred through the smart contract. After the transfer is completed, the authorization list is updated, and the user ID of the purchased data is added to the user ID.

S12. Download data request: Buyer B has completed the payment and can download the data. Through the data trading platform DTP, fill in the ID of the data to be downloaded. After clicking download, the data trading platform DTP will call the smart contract to obtain the IPFS data method of the interstellar file system. First, the authorization list user verification will be performed to check whether the user has purchased data, and if the user ID exists, the data will be downloaded.

S13. Return data: Buyer B gets the data set.

Key algorithms are involved in this paper. Algorithm 1: Text filtering algorithm, in the process of data right confirmation, we first need to clean and filter the text data, punctuation marks, etc. If the text data is in HTML format, it is necessary to clear the HTML tags; Algorithm 2: Fingerprint algorithm, use for generating feature fingerprints on text data. The Hamming distance between texts comes into play at a later stage.

Algorithm 1：Text filtering algorithm.:

Method: String cleanResume(String content)
input: {text data: content};
output: {filtered text data: tokens};

content = Jsoup.clean(content, Whitelist.none());// If the input is HTML, the following will filter out all HTML tags
content = StringUtils.lowerCase(content);
String[] strings = {" ",",",".",";","?", "\n", "\r", "\t", "\\r", "\\n ", "\\t", " "};//Special characters and html tags
For (String s : strings) {//Replace special characters in the text content
tokens←content.replaceAll(s, "");
Return tokens;
End

Algorithm 2：Fingerprint algorithm:

Method: BigInteger simHash()
input: {text data:tokens};
output: {text fingerprint: fingerPrint};

tokens = cleanResume(tokens); // cleanResume deletes some special characters
List<Term> termList = StandardTokenizer.segment(textOne); // Segment the string
//Some special processing of word segmentation: for example: adding weight according to part of speech, filtering out punctuation marks, filtering overclocking words, etc.;
Map<String, Integer> weightOfNature = new HashMap<String, Integer>(); // weight of part of speech
weightOfNature.put("n", x); //The weight given to the noun is x;
Map<String, String> stopNatures = new HashMap<String, String>();//The part of speech that is stopped, such as some punctuation marks;
stopNatures.put("w", ""); //
int overCount = y; //Set the limit of overclocking vocabulary y;
Map<String, Integer> wordCount = new HashMap<String, Integer>();
For←(Term term : termList) {
String word = term.word; //Word segmentation string
String nature = term.nature.toString(); // word segmentation attribute;
If (wordCount.containsKey(word)) {// filter overclocking words
int count = wordCount.get(word);}
If (stopNatures.containsKey(nature)) {// Filter stop parts of speech
continue;}
//Hash each participle into a set of fixed-length sequences. For example, a 64bit integer.
BigInteger t = this.hash(word);
for (int i = 0; i < this.hashbits; i++) {
BigInteger bitmask = new BigInteger("1").shiftLeft(i);int weight = 1;//Add weight
If (weightOfNature.containsKey(nature)) {
weight = weightOfNature.get(nature);}
If (t.and(bitmask).signum() != 0) {//Calculate the vector sum of all features of the entire document
v[i] += weight;} Else {v[i] -= weight;}}}
BigInteger fingerPrint = new BigInteger("0");
For ← (int i = 0; i < this.hashbits; i++) {
If (v[i] >= 0) {
fingerPrint = fingerPrint.add(new BigInteger("1").shiftLeft(i));}}
Return fingerPrint;
End

This chapter mainly introduces the development environment used in the experiment. Including: back-end development environment, development tools used, project directory structure, Ethereum experimental environment. Secondly, conduct data security analysis, from five aspects: uniqueness, confidentiality, non-repudiation, data integrity, and anti-network attack. Finally, compare the relevant schemes, and use the gas cost as a parameter indicator to ensure the availability of the scheme.

4.1 Experimental environment

Back-end development environment: This platform uses the Intellij IDEA integrated development environment for software development, combined with the C/S structure (Client/Server, client/server). The platform joins the Ethereum-based consortium chain to ensure data credibility, and transforms the traditional MVC model into the MVBC model (model.view.blockchain.controller). The key java development packages and versions introduced by the system through the maven project are as follows: java-ipfs-api1.3.3, org.web3j.core 3.4.0, jcommon-0.9.5.jar, jfreechart-1.0.13.jar where ipfs is used for data Distributed storage, web3j for blockchain development and smart pooling, and freetree for direct drawing of graphics. As shown in Table 1and Fig. 9.

Table 1

Experimental environment information
Symbol	Description
Name CPU Memory Graphics Card Java Jar package Development tools	Dell,Insprion1450Intel(R) Core(TM) i5-4590 CPU@ 3.70GHz 16GB HD Graphics 4600 JRE: 1.8.0, java-ipfs-api1.3.3, org.web3j.core 3.4.0, jcommon-0.9.5.jar, jfreechart-1.0.13.jar IntelliJ Idea 2019.1.2

The Bean directory implements user instantiation, the chart directory implements icon drawing, the eth directory implements Ethereum-related smart contract calls, the invalidate ipfs directory implements interplanetary file system upload and download, and generates file CIDs, and the utils directory implements encryption tools including ECC amount AES and dataTransaction The directory implements customer front-end logic operations dataTransaction.form The sampleText directory implements front-end interface display, the sampleText directory implements similar text recognition, and the sampleText.form directory implements similar text recognition front-end interface display. The code is exposed on gitee

Ethereum experimental environment: Based on Windows10, this article uses Ganache to simulate an Ethereum account, and builds a private chain of Ethereum in Remix IDE to test and verify the proposed smart contract. The compiler version tested is 0.7.0 + commit, The test environment is a JavaScript VM. The number of experimental entities used ten Ethereum addresses, each with an initial balance of 100 ether. Buyer's address: 0x74c945C9E90F708Ed984f8E737A5564b4702f676; Seller's address 0xCf14b0aa87d0B459590cb032A7174be47ABf9016; as shown in Fig. 10.

The experimental environment has applied version information Windows 10, Ganache V2.5.4, and Remix IDE 1.4. System operating environment: memory 16GB, hard disk 1000GB, bandwidth 100Mb/s.

4.2 Data Security Analysis

(1) Uniqueness: Similarity calculation is performed on the text through natural language processing NLP technology to realize the data right, and then the unique ID of the data is generated to identify the data, preventing others from copying the data and uploading it again for transactions.

(2) Confidentiality: In the data trading platform, the data buyer DB utilizes time stamps and unique key chain access to achieve data download. Even if the keyis accidentally leaked, the user ID with the right to use the data is stored in the smart contract, and the smart contract can verify the user's identity to prevent data resource leakage.

(3) Non-repudiation: Through our proposed blockchain-based data trading platform solution for data transactions, all stakeholders cannot deny their own transaction behavior in the transaction. We have time logs as user operation credentials. We have complete log records for data operations, and users cannot deny that they have not received specific benefits and data resources.

(4) Data integrity: In the blockchain-based data trading platform, data sellers use the key to access the data store and download data resources. Any data that needs to be modified should be re-registered as a new transaction. After processing, there will be traces left in the operation log, and any process information of the data transaction will be recorded.

(5) Anti-network attack: Ethereum is a distributed storage database. Traditional network attacks target the central server. Transactions in the Ethereum network are encrypted by encryption technology and have no center. In the data transaction level of the blockchain, malicious attackers cannot tamper with any transactions. The decentralized distributed storage architecture makes network attacks such as man-in-the-middle attacks and distributed denial of service almost impossible.

4.3 Comparison of related schemes

In view of the cost of gas, the smart contracts deployed in this article on the Ethereum private chain built in the JavaScriptVM environment of Remix IDE are compared with the smart contracts of existing solutions in related literature. Some solutions do not have our same method, we use "- "replace. We connect the local node through the Remix IDE and synchronize the wallet amount to simulate the actual data transaction scenario in the local private chain, as shown in Fig. 11. The current account balance in Fig. 11 is the same as the Ethereum account balance in Fig. 10, indicating that the local private chain is successfully built. Comparative analysis was performed by performing multiple experiments to average. As shown in Table 2.

Table 2

Comparative experiment table
Function	[18]	[ 19]	[20]	Our scheme
Create trade order method	130000	54331	210102	43000
Update product method	45000	-	176488	44200
transfer method	39000	47041	8617	79653
update authorization list method	-	65397	-	8600
Total cost	214000	166769	395207	175453

The proposed scheme takes 43000 gas to generate a transaction order, 44200 gas to update the item, 79653 gas for the transaction and 8600 gas to update the authorization list. The total cost of the above operations is about 175453 gas, and the gas cost is close to the reference [19], but the reference [19] does not update the authorization list method. We add the attribute of the authorization list to ensure that the user loses or leaks the secret key in the case of Still does not affect data security. At lower gas consumption levels compared to the solutions of references [18], [20]. In the solution, the transaction body is used to be responsible for the entire data, and the data attributes are clearly organized with fewer mapping relationships. Compared with other solutions, unnecessary operations and costs are reduced, and transaction efficiency and costs are improved. In general, the solution in this paper reduces the workload of sellers and the consumption burden of buyers under the premise of ensuring safety, reliability, fairness and reasonableness, and ensures that the interests of both parties in the transaction are not damaged. The details are shown in Table 2.

In this paper, we use natural language processing technology to calculate the similarity of text to achieve data confirmation; A transaction body centered data encryption and decryption process is constructed through smart contracts to avoid malicious transactions, ensure data security in data transactions, and improve transaction efficiency. The data trading platform is implemented under Windows system, and the database adopts Ethereum plus interstellar file system mode. Finally, the simulation experiment shows that the platform can solve the data ownership ambiguity and data security problems in the data transaction process. Compared with other data transaction schemes, the data transaction cost in this paper is low, providing safe and reliable data transaction services for both buyers and sellers.

Ethical Approval: Ethical approval This manuscript has not been submitted to, nor is it under review at, another journal or other publishing venue.

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

Authors' contributions: In the work of this article, all the authors have participated in the conception and design. Zhang J and Gao Y wrote the main text, Chen F and Qi H edited the text, and all the authors approved the final version.

Funding Statement: This research was supported by the Natural Science Foundation of Shandong Province, research on Key Technologies of Dynamic Game Access Control based on Blockchain, fund number ZR2020MF029, and the Natural Science Foundation of Shandong Province, research on Key Issues of Access Control of Large Scale Intelligent Terminal Based on Blockchain in 5G Environment, fund number ZR2020MF058.

Availability of data and materials: Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

X. Huang, "In 2025, China's big data measurement scale will exceed 3 trillion yuan, expecting to bring a big market," Public Investment Guide, vol. 28, no.1, pp. 7-8, 2022.
A. Jitimon,T. Suda and A. Thara, "Big data analytics on social networks for real-time depression detection," Journal of Big Data, vol. 17, no.1, pp. 69-84, 2022.
J. Feng ,Y. Shi , B. Wang and W. Mu, "Research progress of data mining technology and its agricultural application based on cluster analysis," Journal of Agricultural Machinery, vol. 64, no.9, pp. 1-14, 2022.
S. H. Martin,L. T. Ursula and O. Atle, "Insights from transaction data: Norwegian aquaculture exports," Aquaculture Economics & Management, vol. 24, no.3, pp. 255–272, 2022.
R. Zhao and P. Sun, "Right confirmation, transaction and capitalization: rethinking the basic theoretical issues of transforming big data into production factors," commercial economy and management, vol. 41, no.1, pp.16-26, 2021.
M. Yang,X. Zhou,J. Zeng and J. Xu, "Challenges and solutions of information security issues in the age of big data," China Communications, vol. 13, no.3, pp. 193-202, 2016.
F. Liu ,J. Yang and J. Qi, “Overview of Blockchain Cryptography Privacy Protection Technology, "Journal of Network and Information Security,vol. 8, no.4, pp. 29-44, 2022.
S. Yao ,D. Zhang ,Y. Li and W. Wang , "Overview of research on privacy protection technology of blockchain transaction content,"Journal of Cryptography, vol. 8, no.4, pp. 596-618, 2022.
T. Hao ,Z. Huang ,L. Liang ,H. Weng and B. Tang, "Health Natural Language Processing: Methodology Development and Applications,"JMIR medical informatics,vol. 9,no. 10, pp. e23898-e23910, 2021.
J. Chen,M. Su,V. Azzizz,T. Wang and W. Lin, "Smart Project Management: Interactive Platform Using Natural Language Processing Technology,"Applied Sciences, vol. 11,no. 14, pp. 1597-1611, 2021.
X. Wang and W. Huang, "Weighted Similarity Short Text Classification Based on Category Thesaurus,"Computer Technology and Development, vol. 32, no.9, pp. 95-99, 2022.
Parrilla,Luis,Castillo,Encarnación,LópezRamos et al., "Unified Compact ECC-AES Co-Processor with Group-Key Support for IoT Devices in Wireless Sensor Networks,"Sensors, vol. 18, no.1, pp. 251-267, 2018.
A. Divija and U. Sandeep, "A Hybrid Approach for Image Encryption using Different Number Iterations in ECC and AES Techniques,"International Journal of Computer Applications,vol. 175,no. 3, pp. 10-12, 2017.
Y. Lan,W. Feng and L. Huang, "ECC and AES based blockchain cloud storage framework design,"computer engineering and design, vol. 43, no.6, pp. 1534-1540, 2022.
P. Kang,W. Yang.and J. Zheng, "Jiong. Blockchain Private File Storage-Sharing Method Based on IPFS,” Sensors, vol. 22, no.14, pp. 5100-5100, 2022.
E. Noei and A. Heydarnoori, "EXAF: A search engine for sample applications of object-oriented framework-provided concepts,” Information and Software Technology, vol. 75, pp. 135-147, 2016.
Z. Deng and L. Deng, "Maven's Application in Java Project Development,"Electronic Components and Information Technology, vol. 3, no.5, pp. 1-4, 2019.
N. I. Schwartzbach, "An incentive-compatible smart contract for decentralized commerce,” in Proc. ICBC,Sydney, Australia, pp. 1–3, 2021.
X. Wei and X. Li., "Data resource protection based on smart contract," Computers & Security, vol. 98, no.1, pp. 69–85, 2020.
R. AlTawy,M. ElSheikh,A. M. Youssef and G. Gong, "Lelantos: A Blockchain-Based Anonymous Physical Delivery System,” in Proc. PST,Calgary, AB, Canada, pp. 621–632, 2019.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Decentralized data transaction method and platform based on Ethereum + IPFS

Status:

Version 1

Abstract

Figures

1. Introduction

2. Blockchain-based Data Transaction System

2.1. Data transaction process description

2.1.1. Data confirmation

2.1.2 Data encryption upload

2.1.3 Data Transaction

2.1.4 Data Delivery

2.2 Key Issues Solved in Data Transactions

2.3 Development Architecture of Data Trading Platform

2.4 Data transaction process

3 Key Methods Pseudocode

4 Experiment And Analysis

4.1 Experimental environment

4.2 Data Security Analysis

4.3 Comparison of related schemes

5 Conclusion

Declarations

References

Additional Declarations

Status:

Version 1