CN112635010B - Data storage and sharing method under edge computing based on double block chains - Google Patents
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Abstract
The invention discloses a data storage and sharing method under edge calculation based on a double-block chain. Comprising the following steps: the medical equipment generates original data of physiological health information of a user; generating a complete signature of the original data; extracting hot spot data and non-hot spot data in the original data; generating an extraction signature for extracting the sub-message; respectively constructing hot spot data and non-hot spot data to respectively generate key share sets; submitting the key share sets of the hot spot data and the non-hot spot data to an edge node; reconstructing the key share sets of the hot spot data and the non-hot spot data to obtain a key set; the edge node submits the key share of the hot spot data to a hot spot data chain, and submits the reconstructed key share set to a storage chain; and uploading the non-hot data to the cloud end in a backup uploading mode. The method has high safety, good tamper resistance, and the construction and reconstruction of the key share improve the fault tolerance.
Description
Technical Field
The invention belongs to the field of information security, and particularly relates to a data storage and sharing method under edge computing based on a double-block chain.
Background
With the rapid development of the internet of things and 5G network architecture, wireless human body sensor networks have been widely used for measuring physiological parameters of people, and can provide abundant services for end users. The medical equipment of the Internet of things can process physiological parameters of users, and achieve the effects of disease monitoring, prevention and treatment. Conventional data storage schemes typically employ a centralized storage architecture that typically uses a private database to store data, with patient medical data being distributed among different hospitals. Because of poor interoperability between individual storage systems and lack of uniform data management, users cannot easily access past data even though they belong to them. Information islanding problems caused by centralized medical architectures prevent sharing of data. In addition, in the traditional cloud computing model, a user sends data to a cloud for storage and processing, a large amount of network bandwidth and computing resources are consumed, a brand new thought is provided for solving the problem by the occurrence of edge computing, hot spot data can be stored in an edge node, when the user requests data sharing, the hot spot data can be immediately returned to the user, and non-hot spot data can be stored in the cloud. The problem of insufficient storage space is solved to a great extent by combining cloud storage with edge computing, but huge data amount security of a third party which is stored in a semi-trust state cannot be guaranteed, and the problem of falsified and leaked data is easy to occur. It is therefore necessary to provide a blockchain-based electronic medical record storage and sharing scheme.
Kanji et al propose a data sharing scheme in blockchain-based mobile edge computation [ J.Kang et al, "Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks," in IEEE Internet of Things Journal, vol.6, no.3, pp.4660-4670, june 2019 ], which considers timeliness of data processing, and sensors first submit to edge nodes for processing after receiving the data, while storing valuable data to cloud servers in order to mitigate storage costs of the edge nodes. Although the timeliness of data processing is guaranteed, there is still a problem that the semi-trusted edge node is likely to reveal private data of users when processing the data. Zhang Lihua, et al, propose a safe storage and sharing scheme for medical records based on a double-blockchain [ Zhang Lihua, blue-vant, jiang Pan climbing, preferably vacation ]. A safe storage and sharing scheme for medical records based on a double-blockchain [ J ]. Computer engineering and science, 2019,41 (09): 1581-1587 ]. In the article, the patient encrypts the electronic medical record of the patient by using a private key and stores the encrypted electronic medical record in a third party hosting service, and no reliable third party exists in the real world, which brings about the risk of data leakage.
Disclosure of Invention
The present invention aims to solve the above problems, and provides a method for storing and sharing data under edge computing based on dual-block chain.
The technical scheme for realizing the purpose of the invention is as follows: a data storage and sharing method under edge computing based on double block chains. The method comprises the following steps:
step 1, generating original data;
step 2, generating a complete signature of the complete original data;
step 3, extracting hot spot data A and non-hot spot data B in the original data, and generating an extraction signature of the corresponding extraction sub-message;
step 4, constructing the hot spot data A to generate b key share sets setA;
step 5, constructing the non-hot spot data B to generate B key share sets SetB;
step 6, submitting the key shares SetA and SetB of the hot spot data a and the non-hot spot data B to the edge node;
step 8, the edge node submits the key share SetA of the hotspot data a to a hotspot data chain;
step 9, submitting the reconstructed key share set SetC to a storage chain;
step 10, hot spot data A is stored in an edge node, and non-hot spot data B is uploaded to a cloud end in a backup uploading mode;
compared with the prior art, the invention has the remarkable advantages that: 1) The theoretical model of the invention starts from the data itself, divides the data into hot spot data and non-hot spot data, adopts two different storage modes, and ensures the sharing high efficiency; 2) The invention adopts a mode of constructing key shares for hot spot data and non-hot spot data to realize safe storage of the data, thereby improving the fault tolerance of the data storage; 3) For shared data, considering storage limitation, we design the blockchain external storage to reduce the data written into the blockchain, thereby eliminating throughput bottlenecks, and realizing tamper resistance of the data by utilizing the blockchain storage key share.
The invention is described in further detail below with reference to the accompanying drawings.
Drawings
FIG. 1 is a general architecture diagram of a data storage and sharing method under dual-blockchain based edge computation of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, in conjunction with fig. 1, a dual-blockchain based edge computing data storage and sharing method is provided, the method comprising the steps of:
step 1, generating original data;
step 2, generating a complete signature of the complete original data;
step 3, extracting hot spot data A and non-hot spot data B in the original data, and generating an extraction signature of the corresponding extraction sub-message;
step 4, constructing the hot spot data A to generate b key share sets setA;
step 5, constructing the non-hot spot data B to generate B key share sets SetB;
step 6, submitting the key shares SetA and SetB of the hot spot data a and the non-hot spot data B to the edge node;
step 8, the edge node submits the key share SetA of the hotspot data a to a hotspot data chain;
step 9, submitting the reconstructed key share set SetC to a storage chain;
step 10, hot spot data A is stored in an edge node, and non-hot spot data B is uploaded to a cloud end in a backup uploading mode;
further, in one embodiment, the generating of the raw data in step 1 specifically includes:
step 1-1, submitting human health data collected by the Internet of things equipment to medical equipment by a user, and analyzing and processing the data by the medical equipment to generate complete original data;
further, in one embodiment, the generating the complete signature of the complete original data in step 2 specifically includes:
step 2-1, randomly selecting two unequal prime numbers p and q;
step 2-2, calculating n=p×q, and setting an euler function Φ (n) = (p-1) (q-1);
step 2-3, randomly selecting an integer e which is compatible with phi (n) in the interval [1, phi (n) ];
step 2-4, finding an integer d such that d satisfies (e×d) mod Φ (n) =1;
step 2-5, obtaining public-private key pairs PK= { n, e }. SK= { n, d } of medical equipment of the Internet of things according to the calculation, wherein the public-private key pairs PK= { n, e }. SK= { n, d } are used for signing;
step 2-6, generating a random number r with a fixed length for each electronic medical record sub-message i ；
Step 2-7, each electronic medical record sub-message and the corresponding random number r i After being connected together, the hash value H is calculated i ；
Step 2-8, connecting all hash values together to generate H, and connecting all random numbers together to generate R;
step 2-9, generating signature delta for H using signature private key H Finally, a complete signature delta of the key share is generated full ＝{δ H ,R}；
Further, in one embodiment, the extracting the hot spot data a and the non-hot spot data B in the original data in the step 3 generates an extraction signature of the corresponding extraction sub-message, which specifically includes:
step 3-1, selecting the sub-messages which the user wants to share according to his own will, defining each sub-message which the user wants to share as a hot spot data sub-message or a non-hot spot data sub-message, and then dividing the original data into two parts: hotspot data a and non-hotspot data B;
step 3-2, H unext A hash value representing an unextracted non-hotspot message, the initial value being set to 0; r is R ext A hash value representing the extracted hot spot message, the initial value being set to 0;
step 3-3, extracting the random number r corresponding to each sub-message from the complete signature i ；
Step 3-4, scanning each sub-message, if the sub-message is the sub-message which does not need to be extracted, calculating the hash value of the sub-message after the random number is connected, and connecting all calculated hash values in sequence to obtain H unext ；
Step 3-5, if the hot spot data sub-message to be extracted is the hot spot data sub-message, connecting the random numbers corresponding to the hot spot data sub-message in sequence to obtain R ext ；
Step 3-6, by the above calculation, an extracted signature δ for each sub-message can be generated ext ＝{δ full ,H unext ,R ext }；
Further, in one embodiment, the constructing the hotspot data a in step 4 generates b key share sets SetA, which specifically includes:
step 4-1, randomly selecting k-1 number a 1 ,…,a k-1 Set a 0 The hot spot data A generated in the step 3 is obtained;
step 4-2, creating a polynomial function f (x) =a 0 +a 1 x+a 2 x 2 +…+a k-1 x k-1 ；
Step 4-3, randomly selecting b numbers: x is x 1 ,x 2 ,…x b Substituting them into a polynomial function to obtain f (x 1 ),…,f(x b ) Obtaining b key shares m 1 ＝(x 1 ,f(x 1 )),…,m b ＝(x b ,f(x b ) A key share set seta= { A1, A2, …, ab }, a1=m1..the term, ab=mb, corresponding to the hotspot data is defined.
Further, in one embodiment, the constructing the non-hotspot data B in step 5 generates B key share sets SetB, which specifically includes:
step 5-1, thenSub-randomly selecting k-1 number a 1 ,…,a k-1 Resetting a 0 For generating step 5-1 in step 3, randomly selecting k-1 number c 1 ,…,c k-1 Resetting c 0 The non-hot spot data B generated in the step 3;
step 5-2, creating a polynomial function y (x) =c 0 +c 1 x+c 2 x 2 +…+c k-1 x k-1 ；
Step 5-3, randomly selecting b numbers: k (k) 1 ,k 2 ,…k b Substituting them into a polynomial function to obtain y (k 1 ),…,y(k b ) B key shares o can be obtained 1 ＝(k 1 ,y(k 1 )),…,o b ＝(k b ,y(k b ) A key share set setb= { B1, B2, …, bb }, b1=o1,...
Further, in one embodiment, the key share sets SetA and SetB of the hotspot data a and the non-hotspot data B described in step 6 are submitted to the edge node;
further, in one embodiment, the reconstructing, by the edge node, the key shares SetA and SetB of the hot spot data a and the non-hot spot data B to obtain the key set SetC specifically includes:
step 7-1, the edge node generates setc= { a1+b1, …, an+bn } through the reconstruction of the key share;
further, in one embodiment, the edge node in step 8 submits the key share SetA of the hotspot data a to the hotspot data chain, so as to achieve consensus and then uplink;
further, in one embodiment, the edge node in step 9 submits the reconstructed key share set SetC to the storage chain to achieve consensus back-chaining;
further, in one embodiment, the hotspot data a in step 10 is stored in an edge node, and the non-hotspot data B is uploaded to the cloud end in a backup uploading manner;
examples
As a specific example, the present invention is further illustrated. The invention relates to a data storage and sharing method under edge computing based on double block chains, which comprises the following steps:
(1) The user submits the data collected by the Internet of things equipment to the medical equipment, the medical equipment analyzes and processes the data to generate complete original data, and the complete original data is assumed to comprise 9 parts: personal information, symptoms, diagnostic results, treatment methods, prescriptions, physical examination reports, medical history, sleep time, heart beat. We define the raw data as M full ＝{m 1 ,m 2 ,m 3 ,m 4 ,m 5 ,m 6 ,m 7 ,m 8 ,m 9 }。
(2) The key generation mechanism randomly selects two unequal prime numbers h and q, calculates n=h×q, and sets euler function Φ (n) = (h-1) (q-1). The key generation mechanism randomly selects an integer that is prime to phi (n) and finds a d that satisfies (e x d) mod phi (n) =1 in the interval [1, phi (n) ]. And obtaining a public-private key pair PK= { n, e }. SK= { n, d } of the medical equipment of the Internet of things according to the calculation.
(3) The key generation facility first generates a complete signature for the complete electronic medical record using a content extraction signature algorithm. Sub-message m in each original data i Generating a random number r of fixed length i Each sub-message m i And corresponding random number r i After being connected together, the hash value H is calculated i The method comprises the steps of carrying out a first treatment on the surface of the Concatenating all hash values together produces h=h 1 ||H 2 ||H 3 ||H 4 ||…||H b The method comprises the steps of carrying out a first treatment on the surface of the Connecting all random numbers together produces r=r 1 ||r 2 ||r 3 ||r 4 ||…||r b Generating a signature delta for H using a signature private key sk= { n, d } H =h≡mod n, resulting in the complete signature delta of the key share full ＝{δ H ,R}。
(4) The authenticity of the complete signature is first verified. For each sub-message m i The hash value H (m i ||r i ) Wherein i is [1, b ]]. Judging whether the calculated hash value is equal or notHash values obtained in the decrypted message. For signature delta H Using public key pk= { n, e } verification of internet of things medical device, delta is calculated H And if the calculation result is equal to H, delta H Is a valid signature for H.
(5) For each sub-message m i A verifiable extracted signature is generated. H unext A hash value representing an unextracted non-hotspot message, the initial value being set to 0; r is R ext A hash value representing the extracted hot spot message, the initial value being set to 0; extracting each sub-message m from the complete signature i Corresponding random number r i The method comprises the steps of carrying out a first treatment on the surface of the Scanning each sub-message m i If the sub-message m i For sub-message not needing to be extracted, calculate the sub-message m i Connecting random number r i The subsequent hash value H i ＝H(m i ||r i ) Will not extract sub-message m i Hash value H of (a) i Splicing to generate H unext ＝H unext ||H i If it is the extracted hot spot data sub-message m i The sub-message m i Corresponding random number r i Extracting and sequentially connecting to obtain R ext ＝R ext ||r i . Through the above calculation, an extracted signature δ for each sub-message can be generated ext ＝{δ full ,H unext ,R ext }。
(6) Verifying the correctness of the extracted signature, checking the mark of the message block in the signature document, and calculating H if the mark is not hidden i ＝H(m i ||r i ) Wherein the random number r i From R ext Extracting. Extracting H directly in signature file if message is hidden i . H of data block to be extracted i H with unextracted message blocks i And (5) concatenating the sub-messages in the original document in order to obtain H. Decrypting the pk= { n, e } pair extracted signature using the public key of the internet of things medical device, and (H, δ) pair of message signatures H ) Calculate delta H And a. E mod n, if the result is equal to H, if the verification passes, otherwise the document or signature is tampered with.
(7) The user divides the original data into hot spot dataNon-hotspot data and a corresponding extracted signature is generated, next, the user takes k-1 random numbers a 1 ,…,a k-1 . Let a 0 =hotspot data a, a polynomial is constructed as follows: f (x) =a 0 +a 1 x+a 2 x 2 +…+a k-1 x k-1 . Take b number x 1 ,…,x b Respectively substituting the polynomials to obtain f (x) 1 ),…,f(x b ). The user can obtain b key shares of m 1 ＝(x 1 ,f(x 1 )),…,m b ＝(x b ,f(x b ) A key share set seta= { A1, A2, …, ab } corresponding to the hotspot data is defined.
(8) The user takes b numbers again: k (k) 1 ,k 2 ,…k b . Let o 0 Non-hotspot data B, a polynomial is constructed as follows: y (x) =c 0 +c 1 x+c 2 x 2 +…+c k-1 x k-1 The method comprises the steps of carrying out a first treatment on the surface of the . Take b number x 1 ,…,x b Respectively substituting the polynomials to obtain y (k) 1 ),…,y(k b ). The user can obtain b key shares of o 1 ＝(k 1 ,y(k 1 )),…,o b ＝(k b ,y(k b ) Defining a key share set setb= { o corresponding to the hot spot data 1 ,…,o b }；。
(8) And submitting the constructed key share sets setA and setB to An edge node by a user for reconstruction, and generating setC= { A1+B1, …, an+Bn } by the edge node through the reconstruction of the key shares.
(9) The edge node submits the key share SetA of the hot spot data a to the hot spot data chain, and the hot spot data chain is uplink after consensus is achieved. The edge node submits the reconstructed key share set SetC to a storage chain to achieve consensus and then uplink; the hot spot data A is stored in the edge node, and the non-hot spot data B is uploaded to the cloud end in a backup uploading mode;
from the above embodiments, it can be known that the theoretical model of the present invention separates data into hot spot data and non-hot spot data from the data itself, and adopts two different storage modes, thereby ensuring sharing efficiency; the invention adopts a mode of constructing key shares for hot spot data and non-hot spot data to realize safe storage of the data, thereby improving the fault tolerance of the data storage; for shared data, considering storage limitation, we design the blockchain external storage to reduce the data written into the blockchain, thereby eliminating throughput bottlenecks, and realizing tamper resistance of the data by utilizing the blockchain storage key share.
Claims (4)
1. The data storage and sharing method based on the edge calculation of the double block chains is characterized by comprising the following steps:
step 1, generating original data;
step 2, generating a complete signature of complete original data, which specifically comprises the following steps:
step 2-1, randomly selecting two unequal prime numbers p and q;
step 2-2, calculating n=p×q, and setting an euler function Φ (n) = (p-1) (q-1);
step 2-3, randomly selecting an integer e which is compatible with phi (n) in the interval [1, phi (n) ];
step 2-4, finding an integer d such that d satisfies (e×d) mod Φ (n) =1;
step 2-5, obtaining public-private key pairs PK= { n, e } of the medical equipment of the Internet of things according to the calculation, wherein SK= { n, d } is used for signing;
step 2-6, generating a random number r of fixed length for each sub-message in the original data i ；
Step 2-7, the sub-message and the corresponding random number r in each original data are processed i After being connected together, the hash value H is calculated i ；
Step 2-8, connecting all hash values together to generate H, and connecting all random numbers together to generate R;
step 2-9, generating signature delta for H using signature private key H Generating a complete signature delta of the original data full ＝{δ H ，R}；
Step 3, extracting hot spot data and non-hot spot data in the original data to generate an extraction signature of the corresponding extraction sub-message, wherein the method specifically comprises the following steps:
step 3-1, selecting the sub-messages to be shared according to wish, defining each sub-message to be shared as a hot spot data sub-message or a non-hot spot data sub-message, and then dividing the original data into two parts: hotspot data a and non-hotspot data B;
step 3-2, H unext A hash value representing an unextracted non-hotspot message, the initial value being set to 0; r is R ext A hash value representing the extracted hot spot message, the initial value being set to 0;
step 3-3, extracting the random number r corresponding to each sub-message from the complete signature i ；
Step 3-4, scanning each sub-message, if the sub-message is the sub-message which does not need to be extracted, calculating the hash value of the sub-message after the random number is connected, and connecting all calculated hash values in sequence to obtain H unext ；
Step 3-5, if the hot spot data sub-message to be extracted is the hot spot data sub-message, connecting the random numbers corresponding to the hot spot data sub-message in sequence to obtain R ext ；
Step 3-6, generating an extracted signature delta of each sub-message by the above calculation ext ＝{δ full ,H unext ,R ext }；
Verifying the correctness of the extracted signature, checking the mark of the message block in the signature document, and calculating H if the mark is not hidden i ＝H(m i ||r i ) Wherein m is i For sub-messages in the original data, a random number r i From R ext Extracting H directly in signature file if message is hidden i H of the data block to be extracted i H with unextracted message blocks i The sub-messages are serially connected in the order of the original document to obtain H, the public key of the medical equipment of the Internet of things is used for carrying out decryption operation on the extracted signature of PK= { n, e }, and the message signature pair (H, delta) is used for carrying out decryption operation on the extracted signature of PK= { n, e } H ) Calculate delta H The a e mod n, if the result is equal to H, if the verification passes, otherwise the document or signature is tampered with;
step 4, constructing the hot spot data to generate b key share sets setA;
step 5, constructing the non-hot spot data to generate b key share sets SetB;
step 6, submitting key shares SetA and SetB of the hot spot data and the non-hot spot data to the edge node;
step 7, the edge node rebuilds key shares SetA and SetB of the hot spot data and the non-hot spot data to obtain a key set SetC;
step 8, the edge node submits the key share SetA of the hotspot data to a hotspot data chain;
step 9, submitting the reconstructed key share set SetC to a storage chain;
and step 10, storing the hot spot data in an edge node, and uploading the non-hot spot data B to the cloud end in a backup uploading mode.
2. The method for storing and sharing data under edge computing based on dual-blockchain as in claim 1, wherein the constructing the hot spot data a in step 4 generates b key share sets SetA, specifically comprising:
step 4-1, randomly selecting k-1 number a 1 ,…，a k-1 Set a 0 The hot spot data generated in the step 3 are obtained;
step 4-2, creating a polynomial function f (x) =a 0 +a 1 x+a 2 x 2 +…+a k-1 x k-1 ；
Step 4-3, randomly selecting b numbers: x is x 1 ，x 2 ，...x b Substituting them into a polynomial function to obtain f (x 1 )，…，f(x b ) Obtaining b key shares m 1 ＝(x 1 ，f(x 1 ))，…，m b ＝(x b ,f(x b ) A key share set seta= { A1, A2, …, ab }, a1=m1, …, ab=mb) corresponding to the hotspot data is defined.
3. The method for storing and sharing data under edge computing based on dual blockchain as in claim 1, wherein the constructing non-hot data in step 5 generates b key share sets SetB, specifically comprising:
step 5-1, randomly selecting k-1 number c 1 ，..，c k-1 Resetting c 0 The non-hot spot data B generated in the step 3;
step 5-2, creating a polynomial function y (x) =c 0 +c 1 x+c 2 x 2 +…+c k-1 x k-1 ；
Step 5-3, randomly selecting b numbers: k (k) 1 ，k 2 ，...k b Substituting them into a polynomial function to obtain y (k 1 )，…，y(k b ) B key shares o can be obtained 1 ＝(k 1 ，y(k 1 ))，…，o b ＝(k b ，y(k b ) Defining a key share set setb= { B1, B2, … corresponding to the non-hotspot data B; bb }, b1=o1,...
4. The method for storing and sharing data under edge computing based on dual blockchain as in claim 1, wherein the edge node in step 7 reconstructs key shares of hot spot data and non-hot spot data, specifically comprising:
the edge node generates setc= { a1+b1,..an+bn }, through the reconstruction of the key shares.
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