CN112149165A - Block chain-based social system and method with incentive mechanism and symptom matching function - Google Patents

Block chain-based social system and method with incentive mechanism and symptom matching function Download PDF

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CN112149165A
CN112149165A CN202011013850.7A CN202011013850A CN112149165A CN 112149165 A CN112149165 A CN 112149165A CN 202011013850 A CN202011013850 A CN 202011013850A CN 112149165 A CN112149165 A CN 112149165A
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张爱清
聂雪丽
陈金豆
叶新荣
高雅
胡院院
代雅琳
邓艳
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Anhui Normal University
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Abstract

The invention relates to the technical field of matched safety and privacy protection, and discloses a social system and a social method with incentive mechanism symptom matching based on a block chain, wherein the social system comprises the following steps: the social contact member location end is used for encrypting the keywords of the symptoms by using the attribute key to generate a keyword ciphertext, uploading the keyword ciphertext to the block chain under the condition that the keywords are verified to be real, and pricing disease related data of the social contact member based on an incentive mechanism in a preset intelligent contract; and the requester is located and used for sending a search gate to the block chain to realize matching search of similar symptoms, obtaining pricing of a social member with similar symptoms matched with the symptoms corresponding to the disease-related data expected to be viewed by the requester when the social member with similar symptoms is searched, and viewing the disease-related data of the social member when payment is made for the price corresponding to the pricing. The invention can encourage more users to participate in the system, and simultaneously enable the users to pay and access data fairly.

Description

Block chain-based social system and method with incentive mechanism and symptom matching function
Technical Field
The invention relates to the technical field of matched safety and privacy protection, in particular to a social system and a social method with incentive mechanism symptom matching based on a block chain.
Background
With the rapid development of communication technology, more and more people want to find users with similar disease symptoms on the internet to share their experiences and support each other. For an average person, she/he likes to hand friends with similar topics of interest. For a patient, she/he wants to find a patient with the same symptoms, exchange experiences, broaden the understanding of the symptoms, help the patient to get early diagnosis and better treatment in time, based on which the matching of patient data has been receiving more and more attention from many research institutes in recent years. Since the matched data is personal privacy information, data security and privacy protection are crucial to matching. However, some private information such as disease status, religious preferences, sexual orientation, reputation and welfare may be revealed at any time during the pairing process. There is a need to develop a private matching scheme without revealing personal information.
In order to solve the above problems, the following solutions have been proposed: the system comprises a scheme for protecting user sensitive information based on cloud data sharing, matching, calculating and storing, a novel scheme for cloud-assisted privacy protection contour matching under multiple keys based on proxy re-encryption and a homomorphic encryption algorithm depending on a third party, a scalable friend matching and recommendation scheme which does not reveal personal data of a user to a cloud end, a safe and effective data sharing and contour matching scheme and a scheme for an attribute-based conditional data re-encryption structure. Although these solutions make use of cryptographic algorithms and cloud computing to achieve privacy protection and data security, the cloud is a semi-trusted center, which is honest but curious about data, inevitably with some security risks, and in addition, the above solutions face the risk of a single point of failure. In addition, if the cloud server is attacked, the privacy information of the user can be revealed.
Disclosure of Invention
The invention provides a social system and a method with incentive mechanism symptom matching based on a block chain, which are used for realizing safe symptom matching and privacy protection based on a block chain technology and an intelligent contract, and the incentive mechanism can encourage more users to participate in the system and simultaneously enable the users to pay and access data fairly.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
the invention also provides a social system with incentive mechanism symptom matching based on the blockchain, which comprises the following components: the social contact member location end is used for encrypting the keywords of the symptoms by using the attribute key to generate a keyword ciphertext, uploading the keyword ciphertext to the block chain under the condition that the keywords are verified to be real, and pricing disease-related data of the social contact member based on an incentive mechanism in a preset intelligent contract; and the requester is located and used for sending a search gate to the block chain to realize matching search of similar symptoms, obtaining pricing of a social member with similar symptoms matched with the symptoms corresponding to the disease-related data expected to be viewed by the requester when the social member with similar symptoms is searched, and viewing the disease-related data of the social member when payment is completed and the cost corresponding to the pricing is completed.
In addition, the invention also provides a social method with incentive mechanism symptom matching based on the blockchain, which uses the social system with incentive mechanism symptom matching based on the blockchain, and the social method with incentive mechanism symptom matching based on the blockchain comprises the following steps: step 1, initializing the social system with incentive mechanism symptom matching based on the block chain; step 2, the side where the social member is located generates a keyword ciphertext corresponding to the symptom of the social member; step 3, the end where the requester is located generates a search trapdoor corresponding to data which the requester expects to view; step 4, finding out similar symptoms matched with the symptoms corresponding to the data which the requester desires to view and corresponding social members from the keyword ciphertext by the end where the requester is located through the search trapdoor; step 5, setting pricing based on an incentive mechanism by the located group of the found social members; and step 6, when the end where the requester is located pays the fee corresponding to the pricing, checking the disease related data of the social member.
Preferably, the method of step 1 comprises: step 11, given a security parameter λ, the Attribute Authority (AA) selects a bilinear map
Figure BDA0002698405220000031
G1And G2Is an addition cycle group in which two prime numbers q are the same, where GTIs a multiplicative cyclic group of prime numbers q, P1Is an additive cyclic group G1Is generated from P2Is an additive cyclic group G2A generator of (2); step 12, AA selects a Hash function:
Figure BDA0002698405220000032
and randomly select mu1,μ2,
Figure BDA0002698405220000033
Figure BDA0002698405220000034
Representing a property space in a system, let m be the size of a bit array of a Bloom Filter (BF), and k be the number of hash functions associated with the BF; AA selection
Figure BDA0002698405220000035
Random group element h1,h2,...,hU∈G1And generates k hash functions H1'(),H'2(),...,H'k() Mapping random group elements to [1, m]At a certain position within the range of the mobile terminal,
Figure BDA0002698405220000036
MSK＝(μ1,μ2,θi) (ii) a Wherein, MPK is the master public key, MSK is the master secret key; step 13, the AA generates an attribute key (N, ρ) upon registration of the requester, which is configured to have a Linear Secret Sharing Scheme (LSSS) access structure, where N is an l × N matrix, TrIs a set of different attributes within N,
Figure BDA0002698405220000037
ρ is a function that maps the rows of matrix N to attributes; selecting a random vector
Figure BDA0002698405220000038
Wherein the random vector
Figure BDA0002698405220000039
Will be used to share μ1For 1. ltoreq. i.ltoreq.l, calculate
Figure BDA00026984052200000310
Wherein N isiIs the ith row of N, and continues to select
Figure BDA00026984052200000311
Calculation of Ai＝λP2i+θρ(i)σiP2，Bi＝σiP2，
Figure BDA00026984052200000312
Ei,b＝θbσiP2，ak＝(Ai,Bi,Ei,b) Where ak is the private key.
Preferably, the step 2 includes: step 21, the Social Members (SM Social Members) select random values
Figure BDA00026984052200000313
And calculates a keyword cipher text cw＝{I1,Iz,I2In which I1＝χP1，Iz＝θzχP1，z∈Att，
Figure BDA00026984052200000314
Step 22, if
Figure BDA00026984052200000315
The keyword is verified as being untrue and transmission of the keyword ciphertext c is deniedwOtherwise, the key word is verified as true, and the SM sends a key word ciphertext cwTo the blockchain, storing W by deploying BF in a Verification Smart Contract (VSC), where W is a keyword and W is a preset set of keywords.
Preferably, the step 3 comprises: step 31, obtaining the private key ak of the requester (JR, Joining requests) ═ ai,Bi,Ei,b) (ii) a Step 32, selecting a random vector
Figure BDA0002698405220000041
The vector
Figure BDA0002698405220000042
Will be used to share μ2(ii) a Step 33, calculate
Figure BDA0002698405220000043
Selecting
Figure BDA0002698405220000044
Calculating T1,i＝ηiH1(W)P2+θiσiP2，T2,i＝iBi，T3,i,b＝Ei,bTo obtain search trapdoors Tw＝(T1,i，T2,i，T3,i,b) Wherein, in the step (A),
Figure BDA0002698405220000045
preferably, the step 4 comprises: step 41, determine whether the following equation holds, e (I)1,∑i∈Iπi(T1,i+∑z∈Δ/ρ(i)T3,i,z))＝e(∑z∈ΔIz,∑i∈IπiT2,i)·I2(ii) a Step 42, if yes, the keyword cipher text c is processedw＝{I1,Iz,I2The corresponding symptom is treated as a similar symptom that matches the symptom that the requestor desires to view the data.
Preferably, said step 5 is configured to set pricing based on the optimal pricing mechanism of the Stackelberg game, wherein: requester benefit Ur(xj,pj)＝fjajQ-xjpjWherein x isjThe access volume of the requester of social member j, the access unit price of the social member is pj，fjExpressed as a small amount of charge to be paid as a reward, ajIs the access willingness of the requester; membership benefit Uj(xj,pj)＝sjxjpj-xjcjWherein c isjUnit cost, s, set for social Member jjA reputation score for a social member, the reputation score configured to be dynamically related to feedback of a requester; the maximum value of the benefit of the requester and the social member is configured as a balance point (x)* j,p* j)。
Compared with the prior art, the social system with incentive mechanism symptom matching based on the block chain encrypts the keywords by using the attribute key encryption algorithm, so that the privacy of data is protected from being leaked, the existence of the keywords can be determined by verifying the authenticity of the keywords, and in addition, in order to encourage more users to participate in the system, a fair payment mode is set by the system, so that the matching of safe symptoms is realized, the privacy of the users is also protected, the usability and the fluency of data are improved, and the development trend of data matching under the current value Internet background is met.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of a blockchain-based symptom matching social system with incentive mechanism of the present invention; and
FIG. 2 is a flow chart of a blockchain-based social method with incentive mechanism symptom matching according to the present invention; and
FIG. 3 is a block diagram of the working modules of the block chain-based symptom matching social system with incentive mechanism of the present invention.
Description of reference numerals:
1. social member location end 2 and requester location end
3. Block chain
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
The characteristics of decentralization, transparency, anonymity, autonomy and the like of the block chain 3 are beneficial to realizing safe symptom matching, and meanwhile, the validity of the key words is verified by utilizing an intelligent contract. The incentive mechanism encourages more users to actively participate in the system, and the fairness is also embodied. At present, a cloud data sharing system in the prior art has a risk of privacy leakage single-point failure.
Fig. 1 and 3 illustrate a block chain 3-based social system with incentive mechanism symptom matching according to the present invention, and as shown in fig. 1 and 3, the block chain 3-based social system with incentive mechanism symptom matching includes: the social contact member location end 1 is used for encrypting keywords of symptoms by using an attribute key to generate a keyword ciphertext, uploading the keyword ciphertext to a block chain 3 under the condition that the keywords of the symptoms are verified to be real, pricing disease-related data of the social contact member based on an incentive mechanism in a preset intelligent contract, and is a client terminal which is connected with an attribute center and the block chain 3, is used for receiving initialization parameters of the attribute center and is used for subsequent steps of encryption, uploading and pricing; and the requester-located end 2 is used for sending a search gate to the block chain 3 to realize matching search of similar symptoms, acquiring pricing of a social member with similar symptoms matched with the symptoms corresponding to the disease-related data expected to be viewed by the requester when the social member with similar symptoms is searched, and viewing the disease-related data of the social member when payment is completed and the price corresponds to the pricing.
In addition, fig. 2 is a block chain 3-based social method with incentive mechanism symptom matching according to the present invention, and as shown in fig. 2, the present invention further provides a block chain 3-based social method with incentive mechanism symptom matching according to the present invention, where the block chain 3-based social system with incentive mechanism symptom matching is used, the block chain 3-based social method with incentive mechanism symptom matching includes:
step 1, initializing the block chain 3-based social system with incentive mechanism symptom matching.
In practice, the initialization process is to write parameters, reduce the attribute authority, set up functions, set up public keys, and register keys. Specifically, the initialization process includes:
step 11, given a security parameter λ, the Attribute Authority (AA) selects a bilinear map
Figure BDA0002698405220000061
G1And G2Is an addition cycle group in which two prime numbers q are the same, where GTIs a multiplicative cyclic group of prime numbers q, P1Is an additive cyclic group G1Is generated from P2Is an additive cyclic group G2A generator of (2);
step 12, AA selects a Hash function:
Figure BDA0002698405220000062
and randomly select mu1,μ2,
Figure BDA0002698405220000063
Figure BDA0002698405220000071
Figure BDA0002698405220000072
Representing a property space in a system, let m be the size of a bit array of a Bloom Filter (BF), and k be the number of hash functions associated with the BF; AA selection
Figure BDA0002698405220000073
Random group element h1,h2,...,hU∈G1And generates k hash functions H1'(),H'2(),...,H'k() Mapping random group elements to [1, m]At a certain position within the range of the mobile terminal,
Figure BDA0002698405220000074
MSK＝(μ1,μ2,θi)；
wherein, MPK is the master public key, MSK is the master secret key;
at the registration of the requester, the AA generates an attribute key (N, p) configured to have a Linear Secret Sharing Scheme (LSSS) access structure, step 13, wherein,
n is a matrix of l × N, TrIs a set of different attributes within N,
Figure BDA0002698405220000075
the function p maps the rows of the matrix N to attributes, selecting a random vector
Figure BDA0002698405220000076
Wherein the random vector
Figure BDA0002698405220000077
Will be used to share μ1For 1. ltoreq. i.ltoreq.l, calculate
Figure BDA0002698405220000078
Wherein N isiIs the ith row of N, and continues to select
Figure BDA0002698405220000079
Calculation of Ai＝λP2i+θρ(i)σiP2，Bi＝σiP2，
Figure BDA00026984052200000710
Ei,b＝θbσiP2，ak＝(Ai,Bi,Ei,b) Where ak is the private key.
Step 2, the terminal 1 where the social member is located generates a keyword ciphertext corresponding to the symptom of the social member; the keyword ciphertext generated by the social member comprises I1＝χP1，Iz＝θzχP1，z∈Att，
Figure BDA00026984052200000711
Wherein the Att is a set of attributes.
The method comprises the following specific steps:
step 21, SM selection is followedMachine value
Figure BDA00026984052200000712
Key word ciphertext I of calculation symptom1＝χP1,Iz＝θzχP1,z∈Att,
Figure BDA00026984052200000713
Step 22, SM sends cwTo blockchain 3, share their medical experience symptoms and signs. To verify that the security index is a W selected from a predefined set of keys, the W is stored by deploying the BF in a smart contract (VSC). If it is not
Figure BDA0002698405220000081
Then 0 is returned, otherwise it returns the ciphertext c of 1, SMwIs sent to the blockchain 3, where W is a keyword and W is a preset set of keywords.
And 3, the end 2 where the requester is located generates a search trapdoor corresponding to the data which the requester expects to view.
Specifically, the steps are as follows:
step 31, obtaining the private key ak of the requester (JR, Joining requests) ═ ai,Bi,Ei,b)；
Step 32, selecting a random vector
Figure BDA0002698405220000082
The vector
Figure BDA0002698405220000083
Will be used to share μ2；
Step 33, calculate
Figure BDA0002698405220000084
Selecting
Figure BDA0002698405220000085
Calculating T1,i＝ηiH1(W)P2+θiσiP2,T2,i＝iBi，T3,i,b＝Ei,bTo obtain search trapdoors Tw＝(T1,i，T2,i，T3,i,b) Wherein, in the step (A),
Figure BDA0002698405220000086
and 4, finding out similar symptoms matched with the symptoms corresponding to the data which the requester desires to view and corresponding social members of the similar symptoms from the keyword ciphertext by the terminal 2 where the requester is located through the search trapdoor.
Step 41, determining whether the following equation is true,
e(I1,∑i∈Iπi(T1,i+∑z∈Δ/ρ(i)T3,i,z))＝e(∑z∈ΔIz,∑i∈IπiT2,i)·I2；
step 42, if yes, the keyword cipher text c is processedw＝{I1,Iz,I2The corresponding symptom is treated as a similar symptom that matches the symptom that the requestor desires to view the data.
And step 5, setting pricing by the found social member terminal 1 based on an incentive mechanism.
In particular, said step 5 is configured to set pricing based on the optimal pricing mechanism of the Stackelberg game, wherein:
requester benefit Ur(xj,pj)＝fjajQ-xjpjWherein x isjThe access volume of the requester of social member j, the access unit price of the social member is pj，fjExpressed as a small amount of charge to be paid as a reward, ajIs the access willingness of the requester;
membership benefit Uj(xj,pj)＝sjxjpj-xjcjWherein c isjUnit cost, s, set for social Member jjScoring a reputation of a social member, the reputationThe score is configured to be dynamically related to the feedback of the requestor;
the maximum value of the benefit of the requester and the social member is configured as a balance point (x)* j,p* j). At this time, the maximum benefit of the requester is:
Ur(x* j,p* j)＝fjajQ-x* jp* j；
the maximum benefit of the member is:
Uj(x* j,p* j)＝sjx* jp* j-x* jcj；
the incentive mechanism promotes active participation of social members in experience sharing. If the balance between the requestor and the social members is broken, some contradictions and conflicts in interests may arise.
And 6, when the terminal 2 where the requester is located pays the fee corresponding to the pricing, checking disease related data of the social member, wherein the disease related data comprise medical experience symptoms and physical signs and are mainly used for communication among patients.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. A blockchain-based social system having incentive scheme symptom matching, the blockchain-based social system having incentive scheme symptom matching comprising:
the social contact member location end is used for encrypting the keywords of the symptoms by using the attribute key to generate a keyword ciphertext, uploading the keyword ciphertext to the block chain under the condition that the keywords are verified to be real, and pricing disease-related data of the social contact member based on an incentive mechanism in a preset intelligent contract; and
the requester is located at the end and used for sending a search gate to the block chain to realize matching search of similar symptoms, obtaining pricing of a social member with similar symptoms matched with the symptoms corresponding to the disease-related data expected to be viewed by the requester when the social member with similar symptoms is searched, and viewing the disease-related data of the social member when payment is completed and the price is corresponding to the pricing.
2. A block chain-based social method with incentive mechanism symptom matching using the block chain-based social system with incentive mechanism symptom matching according to claim 1, wherein the block chain-based social method with incentive mechanism symptom matching comprises:
step 1, initializing the social system with incentive mechanism symptom matching based on the block chain;
step 2, the side where the social member is located generates a keyword ciphertext corresponding to the symptom of the social member;
step 3, the end where the requester is located generates a search trapdoor corresponding to data which the requester expects to view;
step 4, finding out similar symptoms matched with the symptoms corresponding to the data which the requester desires to view and corresponding social members from the keyword ciphertext by the end where the requester is located through the search trapdoor;
step 5, setting pricing based on an incentive mechanism by the located group of the found social members; and
and 6, checking the disease related data of the social member when the end where the requester is located pays the fee corresponding to the pricing.
3. The block chain based symptom matching social method with incentive mechanism according to claim 2, wherein the method of step 1 comprises:
step 11, given a security parameter λ, the Attribute Authority (AA) selects a bilinear map
Figure FDA0002698405210000021
G1And G2Is an addition cycle group in which two prime numbers q are the same, where GTIs a multiplicative cyclic group of prime numbers q, P1Is an additive cyclic group G1Is generated from P2Is an additive cyclic group G2A generator of (2);
step 12, AA selects a Hash function:
Figure FDA0002698405210000022
and randomly select
Figure FDA0002698405210000023
Figure FDA0002698405210000024
Figure FDA0002698405210000025
Representing a property space in a system, let m be the size of a bit array of a Bloom Filter (BF), and k be the number of hash functions associated with the BF; AA selection
Figure FDA0002698405210000026
Random group element h1,h2,...,hU∈G1And generates k hash functions H'1(),H'2(),...,H'k() Mapping random group elements to [1, m]At a certain position within the range of the mobile terminal,
Figure FDA0002698405210000027
MSK＝(μ1,μ2,θi)；
wherein, MPK is the master public key, MSK is the master secret key;
at the registration of the requester, the AA generates an attribute key (N, p) configured to have a Linear Secret Sharing Scheme (LSSS) access structure, step 13, wherein,
n is a matrix of l × N, TrIs a set of different attributes within N,
Figure FDA0002698405210000028
ρ is a function that maps the rows of matrix N to attributes;
selecting a random vector
Figure FDA0002698405210000029
Wherein the random vector
Figure FDA00026984052100000210
Will be used to share μ1For 1. ltoreq. i.ltoreq.l, calculate
Figure FDA00026984052100000211
Wherein N isiIs the ith row of N, and continues to select
Figure FDA00026984052100000212
Calculation of Ai＝λP2i+θρ(i)σiP2，Bi＝σiP2，
Figure FDA00026984052100000213
Ei,b＝θbσiP2，ak＝(Ai,Bi,Ei,b) Where ak is the private key.
4. The block chain based symptom matching social method with incentive mechanism according to claim 3, wherein the step 2 comprises:
step 21, the Social Members (SM Social Members) select random values
Figure FDA0002698405210000031
And calculates a keyword cipher text cw＝{I1,Iz,I2In which I1＝χP1，Iz＝θzχP1，z∈Att，
Figure FDA0002698405210000032
Step 22, if
Figure FDA0002698405210000033
The keyword is verified as being untrue and transmission of the keyword ciphertext c is deniedwOtherwise, the key word is verified as true, and the SM sends a key word ciphertext cwAnd storing W by deploying BF in a smart contract (VSC) to the blockchain, wherein the W is a keyword and the W is a preset keyword set.
5. The block chain based symptom matching social method with incentive mechanism according to claim 4, wherein the step 3 comprises:
step 31, obtaining the private key ak of the requester (JR, Joining requests) ═ ai,Bi,Ei,b)；
Step 32, selecting a random vector
Figure FDA0002698405210000034
The vector
Figure FDA0002698405210000035
Will be used to share μ2；
Step 33, calculate
Figure FDA0002698405210000036
Selecting
Figure FDA0002698405210000037
Calculating T1,i＝ηiH1(W)P2+θiσiP2，T2,i＝iBi，T3,i,b＝Ei,bTo obtain search trapdoors Tw＝(T1,i，T2,i，T3,i,b) Wherein, in the step (A),
Figure FDA0002698405210000038
6. the block chain based symptom matching social method with incentive mechanism according to claim 5, wherein the step 4 comprises:
step 41, determining whether the following equation is true,
e(I1,∑i∈Iπi(T1,i+∑z∈Δ/ρ(i)T3,i,z))＝e(∑z∈ΔIz,∑i∈IπiT2,i)·I2；
step 42, if yes, the keyword cipher text c is processedw＝{I1,Iz,I2The corresponding symptom is treated as a similar symptom that matches the symptom that the requestor desires to view the data.
7. The blockchain-based social method having incentive scheme symptom matching according to claim 6, wherein the step 5 is configured to set pricing based on an optimal pricing scheme of a Stackelberg game, wherein:
requester benefit Ur(xj,pj)＝fjajQ-xjpjWherein x isjThe access volume of the requester of social member j, the access unit price of the social member is pj，fjExpressed as a small amount of charge to be paid as a reward, ajIs the access willingness of the requester;
membership benefit Uj(xj,pj)＝sjxjpj-xjcjWherein c isjUnit cost, s, set for social Member jjReputation for social membersA score configured to dynamically correlate to feedback of a requester;
the maximum value of the benefit of the requester and the social member is configured as a balance point (x)* j,p* j)。
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