Data protection method based on block chain and multi-party computing technology in rural e-commerce platform

Abstract

The invention discloses a data protection method based on a block chain and a multi-party computing technology in a rural e-commerce platform, relates to the technical field of rural e-commerce platform data protection, and is used for solving the problems of platform data privacy protection and transaction security. Through a multi-party security computing technology, security computing of platform data between participants is ensured; in combination with the block chain technology, the alliance chain is adopted to store calculation tasks and transaction historical records, and the data transmission integrity is ensured. And high-value and low-value users are determined, and reputation evaluation is carried out on the commercial tenants. The online condition and the transaction conversion rate of the platform user and the merchant are analyzed, the network coverage of the rural area is optimized, and the order matching and logistics path optimization of the platform under the unstable network condition are improved. Finally, the authenticity of the transaction information is ensured by using a block chain evidence storage technology, and the transaction transparency and security are ensured by adopting decentralized storage and digital signature. According to the scheme, the data security and the operation efficiency of the rural e-commerce platform are effectively guaranteed.

Description

Technical Field

[0001] This invention relates to the field of data protection technology for rural e-commerce platforms, and more specifically, to a data protection method based on blockchain and multi-party computation technology for rural e-commerce platforms. Background Technology

[0002] With the rapid development of internet technology, rural e-commerce platforms have become an important channel for consumers to shop and for merchants to sell. In rural areas, the development of rural e-commerce platforms has ushered in new opportunities, but also faces many challenges. Due to the relatively weak information technology infrastructure in rural areas, data security issues are particularly prominent. Traditional rural e-commerce platforms face a series of security risks, including data leaks, transaction information tampering, and the spread of false information. In particular, the protection of sensitive data such as user personal information and transaction records is often ineffective. These problems are especially serious in rural areas because the network environment there is more complex and vulnerable, and information security protection technologies are relatively lagging behind.

[0003] Furthermore, existing rural e-commerce platforms mostly employ traditional data processing and user evaluation methods, lacking accurate user profiling, transaction credit assessment, and risk identification mechanisms. This can easily lead to inaccurate platform data and errors in merchant credit assessment, impacting the platform's long-term development. With the maturity of blockchain technology, decentralized data storage and transparent transaction records offer a new direction for solving data security issues on rural e-commerce platforms. The application of Multi-Party Secure Computation (MPC) technology can ensure privacy protection during data transmission and analysis, preventing data leakage. Simultaneously, Geographic Information Technology (GIS) and Internet of Things (LoRa) technologies provide rural e-commerce platforms with precise logistics tracking and coverage capabilities, helping to improve the platform's overall operational efficiency.

[0004] To address the above problems, this invention proposes a solution. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a data protection method based on blockchain and multi-party computation technology for rural e-commerce platforms, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] In a preferred embodiment, it includes:

[0008] Step 1: Analyze the conversion rate of rural e-commerce platforms by combining online user fluctuations, and encrypt user data;

[0009] Step 2: Calculate the optimal order matching and logistics route based on transaction priority, without leaving the local machine;

[0010] Step 3: Blockchain-based evidence storage and sharing of computation results.

[0011] In a preferred embodiment, in step 1, the user's access frequency and purchasing behavior, consumption amount and purchase frequency data on the rural e-commerce platform are obtained to determine the user's activity score and consumption capacity score on the rural e-commerce platform, and the user's value score Jz to the rural e-commerce platform is calculated; the user's value score Jz to the rural e-commerce platform is compared with the user value threshold Yjz to the rural e-commerce platform. When the user's value score Jz to the rural e-commerce platform is greater than or equal to the value threshold Yjz, it indicates that the user belongs to the high-value user group of the rural e-commerce platform; when the user's value score Jz to the rural e-commerce platform is less than the value threshold Yjz, it indicates that the user belongs to the low-value user group of the rural e-commerce platform.

[0012] In a preferred embodiment, in step 1, the seller's daily / monthly sales volume, rating, return rate, and customer service response data on the rural e-commerce platform are obtained to determine the seller's sales performance and reputation. The merchant's score Df on the rural e-commerce platform is calculated and compared with the merchant's score threshold Ydf on the rural e-commerce platform. When the merchant's score Df on the rural e-commerce platform is greater than or equal to the merchant's score threshold Ydf on the rural e-commerce platform, it indicates that the merchant is a high-scoring merchant on the rural e-commerce platform; when the merchant's score Df on the rural e-commerce platform is less than the merchant's score threshold Ydf on the rural e-commerce platform, it indicates that the merchant is a low-scoring merchant on the rural e-commerce platform.

[0013] In a preferred embodiment, in step 1, the online number data of users and merchants during the operation of the rural e-commerce platform is obtained, a time interval is set, and the number of online users Ut and the number of online merchants St at each time point are calculated; the standardized value of the number of online users and merchants at each time point is calculated, the time point when the number of online users reaches its peak is selected, and the peak duration is calculated; the proportion of users and merchants at each level within the peak duration Td is calculated, the order data of users at each level within the peak duration Td is calculated, and a conversion rate function is constructed by combining the proportion of users and merchants at each level within the peak duration Td and the order data of users at each level, and the highest conversion rate time period T during the operation of the rural e-commerce platform is solved;

[0014] Set the window length Tw, and calculate R(Tw) by sliding it throughout the entire operation of the rural e-commerce platform. Select different data encryption methods according to different conversion rates.

[0015] In a preferred embodiment, in step 2, the entire rural area is evenly divided into multiple small areas; the signal coverage index of the operator's base station in each small area is obtained; network influencing factor data of user equipment in each small area during the time period T1 is collected; the network stability index WST of each small area during the time period T1 is calculated, and the transaction priority XKJ of each small area is determined by combining the conversion rate of each small area during the time period T1.

[0016] Order matching calculations and logistics route optimization are performed for each small area based on the transaction priority of each small area.

[0017] In a preferred embodiment, in step 3, the input and output of the matching algorithm are set to generate a zero-knowledge proof; the smart contract is verified on-chain and checks whether all transaction conditions are met.

[0018] This invention discloses a data protection method based on blockchain and multi-party computation technology for rural e-commerce platforms. It relates to the field of data protection technology for rural e-commerce platforms and addresses issues of data privacy and transaction security. Through secure multi-party computation technology, it ensures secure computation of platform data among participating parties. Combined with blockchain technology, a consortium blockchain is used to store computation tasks and transaction history, ensuring data transmission integrity. High-value and low-value users are identified, and merchants' creditworthiness is assessed. The online activity and transaction conversion rates of platform users and merchants are analyzed to optimize network coverage in rural areas and improve order matching and logistics path optimization under unstable network conditions. Finally, blockchain notarization technology ensures the authenticity of transaction information, and decentralized storage and digital signatures guarantee transaction transparency and security. This solution effectively safeguards the data security and operational efficiency of rural e-commerce platforms. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the operation of the data protection method based on blockchain and multi-party computation technology of the present invention on a rural e-commerce platform. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example

[0022] This invention discloses a data protection method based on blockchain and multi-party computation technology for use in rural e-commerce platforms, such as... Figure 1 As shown, it includes:

[0023] Step 1: Analyze the conversion rate of rural e-commerce platforms by combining online user fluctuations, and encrypt user data;

[0024] First, identify the data participants on the platform, for example:

[0025] User side: All consumers who purchase goods on rural e-commerce platforms;

[0026] Merchants: All merchants who provide goods and complete transactions on rural e-commerce platforms;

[0027] Logistics provider: The service provider responsible for delivering goods;

[0028] By leveraging Multi-Party Secure Computation (MPC) technology, a trusted computing network is established among data participants across various platforms. By selecting distributed computing nodes, sensitive platform data is segmented and distributed to different nodes for computation without exposing sensitive data to any single node. This ensures the privacy and security of platform data for each data participant and avoids the direct exposure of sensitive information.

[0029] Furthermore, a data sharing strategy is set up, defining which platform data can be shared and which can only be kept private among all data on the rural e-commerce platform; an access control strategy is set up, and role-based access control (RBAC) technology is used to set platform data access permissions that belong only to each platform data participant. At the same time, only authorized users are allowed to access the data, and a permission model is used to manage access to different data resources.

[0030] Furthermore, a consortium blockchain is deployed using a blockchain platform suitable for enterprise-level applications, such as Hyperledger Fabric or Ethereum, to store the historical records of all computing tasks and transactions, ensuring the integrity of data transmission and computation; and smart contracts are set up to manage the transaction rules of the rural e-commerce platform, including transaction verification and payment settlement for both buyers and sellers.

[0031] It should be noted that the aforementioned data sharing strategies, access control strategies, and transaction rules for rural e-commerce platforms all need to be formulated based on the specific circumstances and needs of each rural e-commerce platform, and will not be elaborated upon here.

[0032] Furthermore, the data analytics platform Apache Kafka is used to process user and merchant data in the backend user and merchant logs of the rural e-commerce platform in real time or in batches. Real-time streaming computation is performed using Apache Flink to obtain user access frequency and purchasing behavior, spending amount and purchase frequency data on the rural e-commerce platform. Weighted summation of user access frequency and purchasing behavior data determines user activity scores on the rural e-commerce platform. Weighted summation of user spending amount and purchase frequency data determines user spending power scores on the rural e-commerce platform. Finally, K-means clustering is used to analyze user... By analyzing the activity score and spending power score of users on rural e-commerce platforms, we determine the user's value to the platform (Jz) and set a user value threshold (Yjz). We then compare the user's value to the platform (Jz) with the threshold (Yjz). When the user's value to the platform (Jz) is greater than or equal to the threshold (Yjz), the user is considered a high-value user. When the user's value to the platform (Jz) is less than the threshold (Yjz), the user is considered a low-value user.

[0033] Similarly, we obtain merchants' daily / monthly sales, ratings, return rates, and customer service response data on rural e-commerce platforms. Based on these data, we determine the merchant's sales performance and creditworthiness. Then, we use K-means clustering to analyze the merchant's sales performance and creditworthiness on the rural e-commerce platform, determining the merchant's score Df. We set a score threshold Ydf for the merchant on the rural e-commerce platform and compare it to this threshold. If the merchant's score Df is greater than or equal to the threshold Ydf, the merchant is considered a high-scoring merchant; if the score Df is less than the threshold Ydf, the merchant is considered a low-scoring merchant.

[0034] The acquisition of data on merchants' daily / monthly sales, ratings, return rates, customer service response, sales performance, and reputation on rural e-commerce platforms all refer to the aforementioned user data acquisition methods, and will not be elaborated upon here.

[0035] Furthermore, based on the platform server logs, Kafka, Flink, or Spark Streaming are used to obtain real-time data on the number of users and merchants online during the operation of the rural e-commerce platform. The user ID, merchant ID, timestamp, and online / offline status are extracted from the real-time data on the number of users and merchants online during the operation of the rural e-commerce platform using the ELK algorithm. Data preprocessing methods such as time-series deduplication and outlier detection are then performed to improve data quality.

[0036] Next, time intervals are set for the online user and merchant count data during the operation of the preprocessed real-time rural e-commerce platform, and the number of online users Ut and online merchants St at each time point are calculated. Then, Matplotlib or Tableau is used to draw time series line graphs to intuitively show the changing trends of the number of online users and merchants over time.

[0037] Furthermore, based on the historical online user data of the rural e-commerce platform, an online user threshold Yzx is set. The peak detection algorithm Z-Score is used to calculate the standardized value of the number of users and merchants online at each time point, and the time points where the Z-score is greater than the online user threshold Yzx are selected as the peak. Then, based on the time series line graph of the number of users and merchants online during the operation of the rural e-commerce platform, the start time Ts and end time Te of the peak are recorded, and the peak duration is calculated: Td = Te - Ts, where Td represents the peak duration.

[0038] Furthermore, the number of high- and low-value users and high- and low-value merchants within the peak duration Td is statistically analyzed, and the proportion of users and merchants at each level is calculated based on the formula: P(i)=Ni / Ntotal;

[0039] Where P(i) represents the proportion of users / merchants at level i, Ni represents the number of users / merchants at that level, and Nt represents the total number of online users / merchants; next, the order data of users at each level during the peak duration Td is calculated, including:

[0040] Record the number of orders placed by user level i within time Td, Oi(t);

[0041] Calculate the overall order conversion rate Ci(t) for user tier i:

[0042]

[0043] Calculate the average order value Oi for users at different tiers:

[0044]

[0045] Calculate the conversion rate of rural e-commerce platforms during the peak user time period Td:

[0046]

[0047] Next, construct the conversion rate function:

[0048]

[0049] Where P(i) represents the proportion of user level i in time period Td; Ci(T) represents the order conversion rate of user level i in time period Td; then the function is solved to determine the time period T with the highest conversion rate during the operation of the rural e-commerce platform;

[0050] Furthermore, a window length Tw is set, and R(Tw) is calculated by sliding throughout the entire operation of the rural e-commerce platform. The conversion rate of each Tw time window during the entire operation of the rural e-commerce platform is recorded. A conversion rate threshold Yzh is also set, and the conversion rate R(Tw) within each Tw time window is compared with the conversion rate threshold Yzh. When the conversion rate R(Tw) within the Tw time window is greater than or equal to the conversion rate threshold Yzh, it indicates that the conversion rate of the current Tw time window is good. At this time, the user data within the Tw time window is encrypted using Paillier encryption or BFV / FV scheme, and the server is allowed to perform aggregation analysis without decryption. When the conversion rate R(Tw) within the Tw time window is less than the conversion rate threshold Yzh, it indicates that the conversion rate of the current Tw time window is poor. At this time, the user data within the Tw time window is encrypted using format preservation encryption, and AES-FPE encryption is selected for user browsing behavior and shopping cart data.

[0051] Step 2: Calculate the optimal order matching and logistics route based on transaction priority, without leaving the local machine;

[0052] Considering the unstable network signal in rural areas, which leads to poor online user quality and merchant response quality on rural e-commerce platforms, and the slow data upload resulting in a gradual decrease in the daily active users of rural e-commerce platforms, this embodiment uses Geographic Information System (GIS) to perform spatial analysis on the entire rural area, generating a detailed rural geographic information map and clarifying the geographic boundaries of the entire rural area; then, the Voronoi diagram method is used to evenly divide the entire rural area into multiple small areas.

[0053] Furthermore, the signal coverage index of operator base stations in each small area is obtained through the operator's API interface; LoRa IoT nodes are deployed in each small area, and WebRTC network speed test technology is used to collect network influencing factor data of user equipment in each small area during the T1 time period.

[0054] Calculate the received signal power Pr during time period T1:

[0055]

[0056] Where RSSLi represents the signal power of the i-th sampling point, and N represents the number of sampling points in the time period T1;

[0057] Calculate the average download speed C during time period T1:

[0058] C = Blog2(1 + SNR)

[0059] Where B represents the channel bandwidth, and SNR represents the signal-to-noise ratio. The calculation formula is:

[0060]

[0061] Where N1 represents the noise power;

[0062] Calculate the packet loss rate (PLR) within time period T1:

[0063]

[0064] Where Nlost represents the number of lost data packets, and Nsent represents the total number of data packets sent;

[0065] Next, the signal coverage index of the operator's base station in each small area and the signal reception power, average download speed, and packet loss rate of the user equipment in each small area during the T1 time period are weighted and summed to determine the network stability index (WST) of each small area during the T1 time period. Specifically, it is based on the formula: WST=Q1×Pr+Q2×SINR+Q3×(1-PLR)+Q4×C; where Q1, Q2, Q3, and Q4 represent weighting coefficients, which are adjusted according to the actual data; Pr represents the signal reception power during the T1 time period; SINR represents the base station signal coverage index; PLR represents the packet loss rate during the T1 time period; and C represents the average download speed during the T1 time period.

[0066] Furthermore, the network stability index and rural e-commerce platform conversion rate of each small area in the T1 time period determine the transaction priority XKJ of each small area, specifically according to the formula: XKJ=Qw×WST+Qr×R(T1), where Qw represents the weight of the network stability index WST of each small area in the T1 time period, and Qr represents the weight of the rural e-commerce platform conversion rate R(T1) in the T1 time period.

[0067] It should be noted that the specific method for obtaining the conversion rate R(T1) of the rural e-commerce platform within the T1 time period is the same as in step 1, and will not be repeated here.

[0068] Furthermore, order matching and logistics path optimization calculations are performed on each small area according to the transaction priority XKJ in descending order;

[0069] Order matching calculation:

[0070] The smart contract triggers order matching calculations, calling the encrypted bid data of users and merchants. The computing node starts executing MPC calculations, using Yao's Garbled Circuits protocol to calculate whether the encrypted bid data of users and merchants match. If the match is successful, the smart contract decrypts the transaction amount and executes the order; if the match is unsuccessful, the bid remains encrypted to prevent information leakage.

[0071] Logistics route optimization:

[0072] The smart contract triggers logistics route calculation by calling encrypted user address, merchant address, and logistics cost data. The computing node performs optimal delivery plan calculation through multi-party computation (MPC). The calculation logic is: optimal route = argmin(cost + transit time).

[0073] Once the calculation is complete, the smart contract automatically assigns the best logistics provider and issues a transportation order.

[0074] Step 3: Blockchain-based evidence storage and sharing of computation results;

[0075] To ensure the authenticity and verifiability of transaction, payment, and logistics information and to prevent the platform from manipulating data, the input and output of the matching algorithm are set after the calculation is completed. For each successfully matched order, the calculator generates a zero-knowledge proof to prove that the matching calculation is correct. At the same time, the smart contract calls the ZKP verification algorithm to verify on the chain to ensure that the transaction matching results are authentic and reliable. Only the order information that has passed the verification can enter the evidence storage stage.

[0076] Furthermore, using decentralized storage (IPFS) and blockchain smart contracts, the order information of both parties is hashed to generate a detailed order summary. The smart contract writes transaction matching information, including the order hash value, transaction timestamp, and transaction status. After the transaction is confirmed, the smart contract calculates the hash value of the order information and stores the hash value and timestamp into the blockchain. An event listening mechanism is adopted. When the buyer makes a successful payment, the logistics status is updated, and the seller confirms receipt, the smart contract checks whether all transaction conditions have been met. If the transaction conditions are met, the transaction completion event is triggered.

[0077] Finally, the Distributed Identity Authentication (DID) + Digital Signature technology is used to enable both the buyer and seller to use their respective private keys to digitally sign the transaction completion information and confirm the completion of the transaction.

[0078] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0079] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0080] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0081] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0082] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0083] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. Data protection methods based on blockchain and multi-party computation technology in rural e-commerce platforms. Its characteristics include: Step 1: Analyze the conversion rate of rural e-commerce platforms by combining online user fluctuations, and encrypt user data; Step 2: Calculate the optimal order matching and logistics route based on transaction priority, without leaving the local machine; Step 3: Blockchain-based evidence storage and sharing of computation results. Organize the above content into method mind maps and output them in Markdown format.

2. The data protection method based on blockchain and multi-party computation technology according to claim 1 in a rural e-commerce platform, characterized in that: In step 1, data on user access frequency and purchasing behavior, spending amount and purchase frequency on the rural e-commerce platform are obtained to determine the user's activity score and spending power score on the rural e-commerce platform, and the user's value score Jz to the rural e-commerce platform is calculated. The user's value score Jz to the rural e-commerce platform is compared with the user value threshold Yjz to the rural e-commerce platform. When the user's value score Jz to the rural e-commerce platform is greater than or equal to the value threshold Yjz, it indicates that the user belongs to the high-value user group of the rural e-commerce platform; when the user's value score Jz to the rural e-commerce platform is less than the value threshold Yjz, it indicates that the user belongs to the low-value user group of the rural e-commerce platform.

3. The data protection method based on blockchain and multi-party computation technology according to claim 2 in a rural e-commerce platform, characterized in that: In step 1, the seller's daily / monthly sales volume, rating, return rate, and customer service response data on the rural e-commerce platform are obtained to determine the seller's sales performance and reputation. The merchant's score Df on the rural e-commerce platform is calculated and compared with the merchant's score threshold Ydf on the rural e-commerce platform. When the merchant's score Df is greater than or equal to the merchant's score threshold Ydf on the rural e-commerce platform, it indicates that the merchant is a high-scoring merchant on the rural e-commerce platform; when the merchant's score Df is less than the merchant's score threshold Ydf on the rural e-commerce platform, it indicates that the merchant is a low-scoring merchant on the rural e-commerce platform.

4. The data protection method based on blockchain and multi-party computation technology according to claim 3 in a rural e-commerce platform, characterized in that; In step 1, data on the number of users and merchants online during the operation of the rural e-commerce platform are obtained, time intervals are set, and the number of online users Ut and the number of online merchants St at each time point are calculated; Calculate the standardized values ​​of the number of users and merchants online at each time point, filter out the time points when the number of online users reaches the peak, and calculate the duration of the peak. Calculate the proportion of users and merchants at each level within the peak duration Td, calculate the order data of users at each level within the peak duration Td, construct a conversion rate function by combining the proportion of users and merchants at each level within the peak duration Td and the order data of users at each level, and solve for the time period T with the highest conversion rate during the operation of the rural e-commerce platform. Set the window length Tw, and calculate R(Tw) by sliding it throughout the entire operation of the rural e-commerce platform. Select different data encryption methods according to different conversion rates.

5. The data protection method based on blockchain and multi-party computation technology according to claim 4 in a rural e-commerce platform, characterized in that: In step 2, the entire rural area is evenly divided into multiple small areas; the signal coverage index of the operator's base station in each small area is obtained; and network influencing factor data of user equipment in each small area during the time period T1 are collected. Calculate the network stability index WST for each small area during the T1 time period, and determine the transaction priority XKJ for each small area by combining the conversion rate of each small area during the T1 time period. Order matching calculations and logistics route optimization are performed for each small area based on the transaction priority of each small area.

6. The data protection method based on blockchain and multi-party computation technology according to claim 5 in a rural e-commerce platform, characterized in that: In step 3, the input and output of the matching algorithm are set to generate zero-knowledge proofs; the smart contract is verified on-chain and all transaction conditions are checked.

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