CN113328864A - Data transmission method and system based on function encryption, block chain and machine learning - Google Patents
Data transmission method and system based on function encryption, block chain and machine learning Download PDFInfo
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Abstract
The utility model provides a data transmission method and system based on function encryption, block chaining and machine learning, wherein an edge server performs function encryption on edge data to obtain edge encrypted data, and sends the edge encrypted data to a cloud storage server; the edge server constructs data transaction information corresponding to the edge encrypted data and sends the data transaction information to the block chain server; the edge server sends the constructed and trained token generation model to a central server, and the central server generates a token by using the token generation model; the token generation model is a machine learning model; the central server acquires and verifies the data transaction information from the blockchain server, and decrypts the edge encrypted data acquired from the cloud storage server by using the token in response to the fact that the data transaction information is verified to obtain data in a preset range in the edge data. By combining the related operation of data encryption with the transaction in the blockchain, the reliability of the edge data is guaranteed by utilizing the characteristic that the blockchain cannot be tampered.
Description
Technical Field
The present disclosure relates to the field of data transmission technologies, and in particular, to a data transmission method and system based on function encryption, block chaining, and machine learning.
Background
In a data management system of 'center server-edge server', when a center server needs to acquire edge data in an edge server, the edge server encrypts the edge data by using a private key to obtain ciphertext information of the edge data and sends the ciphertext information to the center server, and the center server decrypts the ciphertext information by using a public key to obtain plaintext information of the edge data for reference and use.
However, in the existing data transmission method, a potentially malicious edge server may destroy the reliability of the edge data, for example, destroy other edge servers, eavesdrop on a key, upload fake data information, and the like, and the reliability of the edge data cannot be guaranteed.
Disclosure of Invention
In view of the above, the present disclosure provides a data transmission method and system based on function encryption, block chain and machine learning.
In view of the above, the present disclosure provides a data transmission method based on function encryption, blockchain and machine learning, where the method is implemented by an edge server, a central server, a blockchain server and a cloud storage server, and the method includes:
the edge server performs function encryption on edge data to obtain edge encrypted data, and sends the edge encrypted data to the cloud storage server;
the edge server constructs data transaction information corresponding to the edge encrypted data and sends the data transaction information to the block chain server;
the edge server sends the constructed and trained token generation model to the central server, and the central server generates a token by using the token generation model; wherein the token generation model is a machine learning model;
and the central server acquires and verifies the data transaction information from the block chain server, and decrypts the edge encrypted data acquired from the cloud storage server by using the token in response to the fact that the data transaction information is verified to obtain data in a preset range in the edge data.
Based on the same inventive concept, the present disclosure provides a data transmission system based on function encryption, blockchain and machine learning, comprising an edge server, a central server, a blockchain server and a cloud storage server, wherein the system is used for implementing the method as described above.
As can be seen from the above description, the present disclosure provides a data transmission method and system based on function encryption, block chain and machine learning, including: the edge server performs function encryption on the edge data to obtain edge encrypted data, and sends the edge encrypted data to the cloud storage server; the edge server constructs data transaction information corresponding to the edge encrypted data and sends the data transaction information to the block chain server; the edge server sends the constructed and trained token generation model to a central server, and the central server generates a token by using the token generation model; the token generation model is a machine learning model; the central server acquires and verifies the data transaction information from the blockchain server, and decrypts the edge encrypted data acquired from the cloud storage server by using the token in response to the fact that the data transaction information is verified to obtain data in a preset range in the edge data. By combining the related operation of data encryption with the transaction in the blockchain, the reliability of the edge data is guaranteed by utilizing the characteristic that the blockchain cannot be tampered.
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In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings needed to be used in the description of the embodiments or related technologies are briefly introduced below, and it is obvious that the drawings in the following description are only embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of an architecture of a center-edge data management system in the related art;
fig. 2 is a schematic flowchart of a data transmission method based on function encryption, block chain and machine learning according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a data transaction information generation algorithm provided by an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a data transaction information verification algorithm provided by an embodiment of the present disclosure;
fig. 5 is an architecture diagram of a data transmission system based on function encryption, block chain and machine learning according to an embodiment of the present disclosure;
fig. 6 is a more specific architecture diagram of a data transmission system based on function encryption, block chain and machine learning according to an embodiment of the present disclosure;
fig. 7 is a schematic diagram illustrating a result of a first simulation experiment provided by the embodiment of the present disclosure;
fig. 8 is a diagram illustrating a result of a second simulation experiment provided by the embodiment of the present disclosure;
fig. 9 is a more specific hardware structure diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It is to be noted that technical terms or scientific terms used in the embodiments of the present disclosure should have a general meaning as understood by those having ordinary skill in the art to which the present disclosure belongs, unless otherwise defined. The use of "first," "second," and similar terms in the embodiments of the disclosure is not intended to indicate any order, quantity, or importance, but rather to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
Referring to fig. 1, an architecture diagram of a center-edge data management system in the related art is shown; in a center-edge (center server-edge server) data management system, first, an edge server registers with a center server, and the center server performs authentication authorization on the edge server, thereby establishing a mutual trust relationship between the center server and the edge server, including agreement and key sending. When the central server applies for obtaining the edge data in the edge server, the edge server encrypts the edge data by using a private key to obtain ciphertext information of the edge data and sends the ciphertext information to the central server, and the central server decrypts the ciphertext information by using a public key to obtain plaintext information of the edge data so as to look up and use the edge data.
However, in the above-mentioned data transmission method, a potentially malicious edge server may destroy the reliability of the edge data, such as violating a signed protocol, destroying other edge servers, eavesdropping a key, uploading counterfeit data information, and the like, but the central server cannot effectively determine the potentially malicious edge server, and thus, the reliability of the edge data cannot be guaranteed.
In view of the above, the present disclosure provides a data transmission method and system based on function encryption, block chain and machine learning.
Referring to fig. 2, it is a schematic flowchart of a data transmission method based on function encryption, block chain and machine learning according to an embodiment of the present disclosure. The method is realized by an edge server, a central server, a block chain server and a cloud storage server. The number of the block chain servers and the cloud storage servers is not limited.
When the data transmission method based on function encryption, blockchain and machine learning provided by the embodiment of the present disclosure is implemented for the first time, it is first required to construct a secret key between the central server and the edge server (i.e. to generate a master public key and a master private key), and it is required that the edge server and the central server register in the blockchain server, and the blockchain server is directed to the public key and the private key generated by the edge server (for convenience of differentiation, this disclosure is referred to as an edge server public key and an edge server private key).
Specifically, the method further comprises: and the central server generates a main public key and a main private key and sends the main public key to the edge server.
In some embodiments, the central server includes an authorization server and a service server.
The authorization server obtains a security parameter lambda. The security parameter is a parameter input when a certain required initial value is generated in the cryptographic algorithm, and is a rule which is computationally infeasible for data such as a key in the algorithm. Generally, the larger the security parameter, the higher the security of the cryptographic algorithm. In practice, a relatively large safety constant (e.g., 128) is typically selected directly. Generation of asymmetric bilinear pairwise group system PG = (p, G)1,G2,GT,e,g1,g2) Wherein p is a prime number, which is automatically generated according to the security parameters; g1,G2,GTAre all p-order groups; g1,g2Are respectively group G1And G2A generator of (2); e is a bilinear map, and e = G1×G2 → GT 。
The authorization server randomly selects two vectors s, t belongs to Zp nCalculate g1 s、g2 t(ii) a Specifically, for any s e {1,2, T }, gsRepresenting generators of different groups, if an integer x ∈ ZpBy [ x ]]s = gs x∈GsIndicates that if it is a vector x ∈ Zp nAlso using [ x ]]s = gs x∈GsRepresents; whereby said master public key and said master private key are calculated, respectively, as Tmpk=（PG,[s]1,[t]2) And Tmsk= (s, t) represents; wherein, gs x =（gs x1… gs xn）T，ZpRefers to the integer set {0,1,2, …, p-1}, Zp nIs a set of n-dimensional vectors with field p.
The authorization server sends the master public key TmpkAnd sending the data to the edge server.
The method further comprises the following steps: the edge server and the central server register in the blockchain server, and the blockchain server generates an edge server public key and an edge server private key for the edge server, and sends the edge server public key to the central server and the edge server private key to the edge server.
The central server and the edge server maintain a global ledger together. The user who successfully registers can broadcast the transaction to the blockchain network and read the transaction in the blockchain at any time. The blockchain generates some necessary parameters for the registered user, such as public and private keys (edge server public and edge server private), where the public key is a public parameter and the private key is kept and private by the user himself. The edge server public key and the edge server private key are respectively KN PAnd KN SAnd (4) showing.
Referring to fig. 1, the method includes:
s110, the edge server performs function encryption on the edge data to obtain edge encrypted data, and sends the edge encrypted data to the cloud storage server.
The method specifically comprises the following steps:
and for the edge data, the edge server randomly selects an integer and a reversible matrix, calculates to obtain two column vectors, and further calculates to obtain the edge encryption data according to the two column vectors.
Specifically, x = {1, x for the edge data1,…xnThe edge server randomly selects an integerr∈ZpAnd an invertible matrix A ∈ GL2Two column vectors a are calculatediAnd biWherein Z ispRepresenting a set of integers, GL2Representing a set of 2 x 2 invertible matrices,
，
and i belongs to n, calculating to obtain the edge encryption data as follows:
ct=（[r]1,{[ai]1,[bi]2} i∈[n]）。
in some cases, the central server does not necessarily know all the information of the edge data in the edge server itself, it only needs to know the partial content of the edge data or specific information based on the edge data, and it is not willing for the central server to know the content in other edge data except the necessary data. However, in the related art "central server-edge server" data management system, the key encryption has the property of "all-or-nothing", that is, in the related art, the central server can only know all the edge data, or cannot know the edge data at all. Obviously, in the related art, the central server also performs learning on unnecessary edge data, and the data privacy of the edge server cannot be guaranteed.
The disclosure adopts the function encryption technology when the edge server encrypts the edge data, and simultaneously, the function f can be used for creating the decryption private key skf. The private key sk is given to the ciphertext C after encryption of the plaintext mfThe owner of (a) can only obtain f (m) through decryption, but not m itself, that is, the decryptor can only obtain a specific function of the plaintext, but not the complete plaintext information. By using the characteristic of function encryption, the central server can only obtain the specific information of the edge data of the edge server, so that the edge server can upload the encrypted data without reservation and can also reserve the encrypted dataThe certificate center server cannot acquire the complete information of the data. The data privacy of the edge server can be guaranteed.
S120, the edge server constructs data transaction information corresponding to the edge encrypted data and sends the data transaction information to the block chain server.
Wherein the edge server constructs data transaction information corresponding to the edge encrypted data, including:
the edge server calls relevant information corresponding to the edge encrypted data and the edge server private key generated by the blockchain server aiming at the edge server, and generates the data transaction information by using the relevant information and the edge server private key; the related information comprises the identification of the edge server, the identification of the edge encrypted data and the time for uploading the edge encrypted data to the cloud storage server.
Referring to fig. 3, a schematic diagram of a data transaction information generation algorithm provided in an embodiment of the present disclosure is shown.
The method specifically comprises the following steps: calculating the related information TinfoHtx = H (T) hash value ofinfo) Using said edge server private key KA SGenerating a digital signature Sign (htx) KA SIf the data transaction information is T, the data transaction information is obtaineddata = {Tinfo，htx，Sign（htx）KA S}。
Wherein after the sending the data transaction information to the blockchain server, further comprising:
the blockchain server broadcasts the data transaction information to a blockchain network of the blockchain server;
the blockchain server verifies the data transaction information with other nodes in the blockchain network and adds the data transaction information to the blockchain network in response to determining that the data transaction information is verified.
The method specifically comprises the following steps: acquiring an edge server public key generated by the block chain server for the edge server; and acquiring the relevant information, the digital signature and the hash value of the relevant information from the data transaction information, decrypting the digital signature by using the edge server public key to generate a hash value for verification, and responding to the fact that the hash value of the relevant information is equal to the hash value for verification, so that the data transaction information passes verification.
Referring to fig. 4, a schematic diagram of a data transaction information verification algorithm provided in an embodiment of the present disclosure is shown.
In some embodiments, an edge server public key K generated by the blockchain server for the edge server is obtainedA P(ii) a Transacting information T from said datadataIn order to obtain the related information TinfoThe digital signature Sign (htx) KA SAnd the related information TinfoUsing said edge server public key K, htxA PDecrypting the digital signature Sign (htx) KA SGenerating a verification hash value htx' in response to determining the related information TinfoThe hash value htx of (a) and the verification hash value htx' are equal, the data transaction information is verified.
The blockchain is a decentralized distributed ledger in nature, and the mechanism of the chain structure ensures that transactions on the blockchain have the characteristics of being not falsified and not reversible. In addition, the decentralized characteristic of the block chain can also avoid network paralysis caused by single point of failure to the maximum extent. The data reliability is improved by using the cryptographic technology and the chain structure of the block chain.
S130, the edge server sends the constructed and trained token generation model to the central server, and the central server generates a token by using the token generation model; wherein the token generation model is a machine learning model.
Wherein, include:
and the edge server sends the token generation model to the authorization server, and the authorization server generates a token by using the token generation model and the main private key and sends the token to the service server.
Specifically, the token generation model is expressed as:
f i (x)=(Px)T D i (Px)，∀i∈[l ]，
wherein i ∈ 2 [, [ solution ]l ]Different labels representing data x, e.g. in handwritten digit recognition, the number displayed on the picture and the font of the number being different labels, i.e. different functions of the data x obtained, the matrix P ∈ Zp n×d、Di∈Zp d ×d。
And the service server applies for the token from the authorization server. And the authorization server verifies the identity of the service server through the registration information at the authorization server when the service server is created, and after the verification is passed, the authorization server generates the token and sends the token to the service server.
Generation of model f using master private key msk and tokeniThe authorization server generates a token skf i：
skf i=( fi[s,t ]2, fi ),
Wherein,f i (s,t)=(Ps)T D i (Pt)。
then, the authorization server sends the token skf i=( fi[s,t ]2, fi ) And sending the information to the service server.
S140, the central server acquires and verifies the data transaction information from the block chain server, and in response to determining that the data transaction information is verified, the edge encrypted data acquired from the cloud storage server is decrypted by using the token to obtain data in a preset range in the edge data.
Wherein, include:
and the central server decrypts the edge encrypted data by using the main public key and the token to obtain the data in the preset range in the edge data.
The business server reads the block chain account book and acquires data transaction information TdataAnd verifying whether the transaction is tampered, thereby verifying the authenticity of the data, the verification process comprising: obtaining an edge server public key K generated by the block chain server for the edge serverA P(ii) a Transacting information T from said datadataIn order to obtain the related information TinfoThe digital signature Sign (htx) KA SAnd the related information TinfoUsing said edge server public key K, htxA PDecrypting the digital signature Sign (htx) KA SGenerating a verification hash value htx' in response to determining the related information TinfoThe hash value htx of (a) and the verification hash value htx' are equal, the data transaction information is verified. And after the verification is passed, the service server executes decryption operation.
Given the master public key mpk and the encrypted ciphertext of the edge server:
ct=（[r]1,{[ai]1,[bi]2} i∈[n]) And the token sk obtained in the token generation processf i=( fi[s,t ]2, fi ) And the service server executes decryption operation.
Wherein, the decryption process is as follows:
for, i ∈ 2d ]Calculating
、
(ii) a Wherein A is-1An inverse matrix, P, representing the matrix AiRepresenting the ith row of the matrix P, s and t represent random vectors generated in the initialization process, and r represents random integers generated in the initialization process;
for i [ epsilon ], [d ]Perform bilinear mapping operationMaking
；
Wherein, because of the matrix DiIs a matrix of D x D, so D represents the matrix DiThe number of rows of (c); i.e. according to matrix DiSince the number of rows (d) is calculated, d bilinear mappings are performed when bilinear mapping is performed.
For all i ∈ valuesl ]First of all, a linear operation is performedf i (s,t)=(Ps)T D i (Pt) Then proceed bilinear mapping operation
. And calculate
. The results of the above two operations are multiplied to cancel, and then the result is obtained
I.e. gfi(x) TIs marked as outi；
Wherein i ∈ 2 [, [ solution ]l ]Different labels representing edge data x; dijRepresentation matrix DiRow and column i;
finally, the service server obtains the log by using a discrete logarithm function log
As the data of the preset range in the edge data.
It can be seen that the central server can only know the specific information of the edge data in the edge server, but cannot know the whole plaintext of the edge data.
As can be seen from the above description, the present disclosure provides a data transmission method and system based on function encryption, block chain and machine learning, including: the edge server performs function encryption on the edge data to obtain edge encrypted data, and sends the edge encrypted data to the cloud storage server; the edge server constructs data transaction information corresponding to the edge encrypted data and sends the data transaction information to the block chain server; the edge server sends the constructed and trained token generation model to a central server, and the central server generates a token by using the token generation model; the token generation model is a machine learning model; the central server acquires and verifies the data transaction information from the blockchain server, and decrypts the edge encrypted data acquired from the cloud storage server by using the token in response to the fact that the data transaction information is verified to obtain data in a preset range in the edge data. By combining the related operation of data encryption with the transaction in the blockchain, the reliability of the edge data is guaranteed by utilizing the characteristic that the blockchain cannot be tampered.
Meanwhile, the central server can only acquire the specific information of the edge data in the edge server, but cannot acquire all plaintexts of the edge data, and the data privacy of the edge server can be guaranteed.
It should be noted that the method of the embodiments of the present disclosure may be executed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the devices may interact with each other to complete the method.
It should be noted that the above describes some embodiments of the disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Referring to fig. 5, it is a schematic structural diagram of a data transmission system based on function encryption, block chain and machine learning according to an embodiment of the present disclosure. A data transmission system based on function encryption, block chain and machine learning comprises an edge server, a central server, a block chain server and a cloud storage server, and is used for realizing the method.
The central server is used for managing the edge servers and can request data from the edge servers. The central server comprises an authorization server and a service server. The authorization server is used for authenticating and authorizing the service server and the edge server, and the service server and the edge server can perform service processing only after authentication and authorization. The business server is used for acquiring data from the edge server, analyzing and processing the data, and providing data support for the central server.
The edge server is used for processing local services, and the local services can be issued or authorized by the central server. The edge server possesses local data, and for the convenience of distinguishing, the local data managed and controlled by the edge server is called edge data in the disclosure. The edge server can provide the edge data to the central server at regular time or according to the requirement of the central server.
The blockchain server is used to provide a trusted environment. The blockchain network has the characteristics of decentralization and independent verification, so that the reliability and the accountability are ensured. When any node uploads data, corresponding transaction is broadcasted to the blockchain network, once the information is recorded on the blockchain, the information cannot be tampered, and malicious behaviors can be resisted.
The cloud storage server is used for providing a data storage platform. The edge server can upload the encrypted edge data to the cloud storage server, and then the central server can freely download the encrypted data. The cloud storage server may relieve the pressure of local storage, which the cloud server itself does not need and which is not allowed to perform any decryption computations.
The edge server, the central server, the blockchain server and the cloud storage server together form an overall architecture for implementing the data transmission method based on function encryption, blockchain and machine learning provided by the embodiment of the disclosure.
Referring to fig. 6, a more specific architecture diagram of a data transmission system based on function encryption, block chain and machine learning according to an embodiment of the present disclosure is shown.
The system adopts a three-layer structure comprising an application layer, a block chain layer and a storage layer, wherein the edge server and the center server are arranged on the application layer, the block chain server is arranged on the block chain layer, and the cloud storage server is arranged on the storage layer.
In some embodiments, the edge servers, the central server, the blockchain servers, and the cloud storage servers are arranged in a three-layer architecture when the method is implemented, where the edge servers and the central server are arranged in an application layer, the blockchain servers are arranged in a blockchain layer, and the cloud storage servers are arranged in a storage layer. The application layer provides an entrance for encryption learning and data analysis for the edge server and the central server, the block chain layer provides an interface for initiating transaction and reading transaction for the application layer, and the storage layer is mainly used for storing data of the application layer.
An application layer: operations such as authorization authentication, encryption and decryption, data training and learning, which are performed by the central server and the edge server, are all in the application layer. The edge server uploads the encrypted data by interacting with the storage layer, and simultaneously interacts with the blockchain layer to record the related data and the behavior of storing the data. The service server verifies the reliability of the data by interacting with the storage layer to download the encrypted data and simultaneously interacting with the block chain layer.
Block chain layer: the block chain layer provides reliability guarantee for data transmission. After the edge server broadcasts the blockchain transaction corresponding to the data to the network, peer nodes in the network verify and record the transaction. The transaction types in the network mainly include data transactions and reward transactions. Furthermore, nodes in the blockchain are not allowed to learn sensitive information of data in the application layer.
The block chain layer also comprises miners and consensus mechanisms. Miners are the basis for blockchain operations. Without miners, the block chain cannot run. The miners are not producers of blockchain information, but are producers of blocks. The block chain is an industry which takes blocks as commodities and packs user information into blocks. The user needs to write information into the block and also needs to use the information in the block. And miners in the blockchain provide this service. They write information into the blockchain, so they are sellers of the blockchain market, while the users are buyers and the goods transacted are blockchain. The service of the transaction is to package the user information into blocks. The mechanism of how miners go to produce blocks and how they gain revenue by providing blocks is a consensus mechanism.
A storage layer: the storage tier merely plays the role of a storage platform, and is introduced to save the cost of local storage and blockchain storage. Here, a cloud server is used to store cryptographic data and some of the computations related to cryptographic data searches.
In some embodiments, the present disclosure also defines a threat model.
A potentially malicious user may act against the security requirements of the system in order to maximize his profit, and therefore have a security threat.
Assuming that there is an adversary, his goal is to obtain as much secret information as possible, thereby seeking more interest. If the adversary is honest but curious, he must trust the mechanisms provided by the present disclosure and follow the protocol specifications in the mechanisms provided by the present disclosure. The mechanism provided by the present disclosure allows the adversary to access system common parameters and data streams transmitted in common channels and associated cryptographic algorithms. When an adversary turns into a malicious adversary, in addition to the above capabilities, he can also destroy any number of edge servers, eavesdrop on key information, upload fake data to deceive the central server to achieve his conspiracy. On this basis, the information transfer is to operate in a secure and trusted channel.
The threat model also makes the following assumptions: by default, the authorization server is fully trusted and any adversary cannot destroy the trustworthiness of the authorization server. To do this, the rights definition may be made in the authorization server. Assuming that all authorizations are performed secretly in a secure and efficient manner, the information of the service server is not revealed to any adversary. There is no linking action between the service servers, so that an unauthorized service server cannot acquire data information.
To verify the feasibility of the present disclosure, the present disclosure performs deployment and simulation tests. The operating system used in the experiment was Linux (5.4.0-62-genetic) Ubuntu 18.04.5 installed on VMware Workstation Pro, the Memory was 4G, and the equipment model was Intel (R) core (TM) i7-9700 CPU @3.00GHz 32G Memory. The present disclosure aims to achieve a reliable and conditional privacy preserving reinforcement learning, whereby the testing includes at least data processing and blockchain interaction.
The experiment utilized the Charm framework, PBC library and OpenSSL (version 1.1), where the elliptic curve used was MNT159 with an embeddability of 6, with 80 bits of security. This can be used to implement all encryption algorithms in this disclosure. A blockchain network of five nodes is also deployed from a simplified toolbox in Python language and PyCharm IDE (2020 version) code with about 1500 lines. The blockchain consensus algorithm is PoW, and the parameters of the created blocks are shown in table 1 below. In addition, a neural network is trained on TensorFlow to realize a machine learning algorithm, and the data set used for training is an MNIST handwritten number set, wherein the training data set comprises 60,000 samples, and the testing data set comprises 10,000 samples. Each picture in the data set consists of 28 x 28 pixels, each pixel being represented by a gray scale value in the range of 0-255.
TABLE 1 parameter settings for creative tiles
| Parameter(s) | Trading | Hash | Previous block hash | Serial number | |
| Value of | GENESIS | a5fb4682b627e36c36a55ad6b4c8606465e9723fd48254b92c542e2773c3b05e | null | 1 |
The neural network model used in the experiment was a 2-layer fully-connected network, with the goal of classifying the input as one of 10 numbers in the range of 0-9, setting the output layer size to 10, and the number of samples (batch size) selected for one training was 100.
Refer to fig. 7, which is a diagram illustrating a result of a first simulation experiment provided by an embodiment of the present disclosure. A in fig. 7 shows the accuracy of 10 epochs, and as can be seen from a in fig. 7, the training can achieve 97% accuracy. The time cost required for encryption and decryption is tested, and since the FE needs to perform time-consuming discrete logarithm and pairing operation during decryption, considering the utilization of space transformation time, some discrete logarithms are solved in advance and stored in the database. Here, a PostgreSQL database is used, which is an open source object relational database system with reliability and better performance. B in fig. 7 and c in fig. 7 show the average time cost consumed by the decryption/encryption time as the number of pictures increases, and in the experimental process, the program will automatically select one of the pictures to be encrypted and then decrypted, each encryption/decryption operation is independent, and ten averaging operations are carried out.
The interaction performance of the blockchain is also tested, in the blockchain network, the consensus algorithm PoW ensures that only nodes solving the difficult problem can obtain the accounting right, the nodes successfully obtaining the accounting right are responsible for recording transaction information, and the transactions are packaged and synchronized to the whole network.
Refer to fig. 8, which is a diagram illustrating a result of a second simulation experiment provided by the embodiment of the present disclosure. A in fig. 8 shows a variation of the transaction generation time as the data amount increases, and it can be seen that the time taken for transaction generation increases linearly as the data size increases. B in fig. 8 shows the time spent for transaction confirmation as the transaction volume increases within the block, and it can be seen that the time spent for transaction confirmation is little and no significant change. C in fig. 8 shows the CPU occupancy over time, and it can be seen that the CPU consumption increases over time, but eventually still falls within an acceptable range. D in fig. 8 shows the response time variation with increasing concurrency number, and it can be seen that the time cost increases with increasing transaction number, but the overall consumption time is shorter and the efficiency is higher.
The experiments show that the method has higher precision and reasonable throughput on the basic premise of ensuring privacy and reliability.
Based on the same inventive concept, corresponding to any of the above embodiments, the present disclosure further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the data transmission method based on function encryption, block chaining, and machine learning according to any of the above embodiments is implemented.
Fig. 9 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
The electronic device of the foregoing embodiment is used to implement the corresponding data transmission method based on function encryption, block chain and machine learning in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to any of the above-described embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the data transmission method based on function encryption, block chaining, and machine learning according to any of the above embodiments.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the data transmission method based on function encryption, block chain and machine learning according to any of the above embodiments, and have the beneficial effects of corresponding method embodiments, which are not described herein again.
It should be noted that the embodiments of the present disclosure can be further described in the following ways:
a data transmission method based on function encryption, blockchain and machine learning, the method is realized by an edge server, a center server, a blockchain server and a cloud storage server, and the method comprises the following steps:
the edge server performs function encryption on edge data to obtain edge encrypted data, and sends the edge encrypted data to the cloud storage server;
the edge server constructs data transaction information corresponding to the edge encrypted data and sends the data transaction information to the block chain server;
the edge server sends the constructed and trained token generation model to the central server, and the central server generates a token by using the token generation model; wherein the token generation model is a machine learning model;
and the central server acquires and verifies the data transaction information from the block chain server, and decrypts the edge encrypted data acquired from the cloud storage server by using the token in response to the fact that the data transaction information is verified to obtain data in a preset range in the edge data.
Optionally, the method further includes: and the central server generates a main public key and a main private key and sends the main public key to the edge server.
Optionally, the method further includes: the edge server and the central server register in the blockchain server, and the blockchain server generates an edge server public key and an edge server private key for the edge server, and sends the edge server public key to the central server and the edge server private key to the edge server.
Optionally, the performing, by the edge server, function encryption on the edge data to obtain edge encrypted data, and sending the edge encrypted data to the cloud storage server includes:
and for the edge data, the edge server randomly selects an integer and a reversible matrix, calculates to obtain two column vectors, and further calculates to obtain the edge encryption data according to the two column vectors.
Optionally, the constructing, by the edge server, data transaction information corresponding to the edge encrypted data includes:
the edge server calls relevant information corresponding to the edge encrypted data and the edge server private key generated by the blockchain server aiming at the edge server, and generates the data transaction information by using the relevant information and the edge server private key; the related information comprises the identification of the edge server, the identification of the edge encrypted data and the time for uploading the edge encrypted data to the cloud storage server.
Optionally, after the edge server sends the data transaction information to the blockchain server, the method further includes:
the blockchain server broadcasts the data transaction information to a blockchain network of the blockchain server;
the blockchain server verifies the data transaction information with other nodes in the blockchain network and adds the data transaction information to the blockchain network in response to determining that the data transaction information is verified.
Optionally, the central server includes an authorization server and a service server; the edge server sends the constructed and trained token generation model to the central server, and the central server generates a token by using the token generation model, including:
and the edge server sends the token generation model to the authorization server, and the authorization server generates a token by using the token generation model and the main private key and sends the token to the service server.
Optionally, the obtaining, by the central server, the data transaction information from the blockchain server and verifying the data transaction information, and in response to determining that the data transaction information is verified, decrypting, by using the token, the edge encrypted data to obtain data in a preset range in the edge data includes:
and the central server decrypts the edge encrypted data by using the main public key and the token to obtain the data in the preset range in the edge data.
A data transmission system based on function encryption, block chain and machine learning comprises an edge server, a central server, a block chain server and a cloud storage server, and is used for realizing the method.
Optionally, the system adopts a three-layer architecture including an application layer, a blockchain layer and a storage layer, the edge server and the central server are disposed in the application layer, the blockchain server is disposed in the blockchain layer, and the cloud storage server is disposed in the storage layer.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the present disclosure, also technical features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures for simplicity of illustration and discussion, and so as not to obscure the embodiments of the disclosure. Furthermore, devices may be shown in block diagram form in order to avoid obscuring embodiments of the present disclosure, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the present disclosure are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that the embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The disclosed embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalents, improvements, and the like that may be made within the spirit and principles of the embodiments of the disclosure are intended to be included within the scope of the disclosure.
Claims (10)
1. A data transmission method based on function encryption, blockchain and machine learning, the method is realized by an edge server, a center server, a blockchain server and a cloud storage server, and the method comprises the following steps:
the edge server performs function encryption on edge data to obtain edge encrypted data, and sends the edge encrypted data to the cloud storage server;
the edge server constructs data transaction information corresponding to the edge encrypted data and sends the data transaction information to the block chain server;
the edge server sends the constructed and trained token generation model to the central server, and the central server generates a token by using the token generation model; wherein the token generation model is a machine learning model;
and the central server acquires and verifies the data transaction information from the block chain server, and decrypts the edge encrypted data acquired from the cloud storage server by using the token in response to the fact that the data transaction information is verified to obtain data in a preset range in the edge data.
2. The method of claim 1, further comprising: and the central server generates a main public key and a main private key and sends the main public key to the edge server.
3. The method of claim 1, further comprising: the edge server and the central server register in the blockchain server, and the blockchain server generates an edge server public key and an edge server private key for the edge server, and sends the edge server public key to the central server and the edge server private key to the edge server.
4. The method according to claim 1, wherein the edge server performs function encryption on edge data to obtain edge encrypted data, and sends the edge encrypted data to the cloud storage server, and the method comprises:
and for the edge data, the edge server randomly selects an integer and a reversible matrix, calculates to obtain two column vectors, and further calculates to obtain the edge encryption data according to the two column vectors.
5. The method of claim 3, wherein the edge server constructs data transaction information corresponding to the edge encrypted data, comprising:
the edge server calls relevant information corresponding to the edge encrypted data and the edge server private key generated by the blockchain server aiming at the edge server, and generates the data transaction information by using the relevant information and the edge server private key; the related information comprises the identification of the edge server, the identification of the edge encrypted data and the time for uploading the edge encrypted data to the cloud storage server.
6. The method of claim 1, wherein after the edge server sends the data transaction information to the blockchain server, further comprising:
the blockchain server broadcasts the data transaction information to a blockchain network of the blockchain server;
the blockchain server verifies the data transaction information with other nodes in the blockchain network and adds the data transaction information to the blockchain network in response to determining that the data transaction information is verified.
7. The method of claim 2, wherein the central server comprises an authorization server and a business server; the edge server sends the constructed and trained token generation model to the central server, and the central server generates a token by using the token generation model, including:
and the edge server sends the token generation model to the authorization server, and the authorization server generates a token by using the token generation model and the main private key and sends the token to the service server.
8. The method of claim 2, wherein the central server obtains and verifies the data transaction information from the blockchain server, and in response to determining that the data transaction information is verified, decrypts the edge encrypted data using the token to obtain a preset range of data in the edge data, including:
and the central server decrypts the edge encrypted data by using the main public key and the token to obtain the data in the preset range in the edge data.
9. A data transmission system based on function encryption, blockchain and machine learning, comprising edge servers, central servers, blockchain servers and cloud storage servers, the system being configured to implement the method according to any one of claims 1 to 8.
10. The system of claim 9, wherein the system employs a three-tier architecture comprising an application tier, a blockchain tier, and a storage tier, the edge servers and the central server being disposed at the application tier, the blockchain servers being disposed at the blockchain tier, and the cloud storage servers being disposed at the storage tier.
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