CN114793228A - Data source screening method and system for preventing merchant from doing malicious activities based on zero knowledge proof - Google Patents
Data source screening method and system for preventing merchant from doing malicious activities based on zero knowledge proof Download PDFInfo
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Abstract
The invention provides a data source screening method and a system for preventing a merchant from doing malice based on zero knowledge proof, which comprises the following steps: step 1: generating a random value on a privacy computing platform, and sending the random value to a merchant server in an HTTPS mode; step 2: in a merchant server, generating a corresponding calculation result value and a zero knowledge proof according to the random value; and 3, step 3: sending the calculation result value, the zero knowledge proof and the process data to a privacy calculation platform; and 4, step 4: and verifying the calculation result value and the zero knowledge proof in the privacy calculation platform, and if the verification result does not meet the preset requirement, performing corresponding punishment on the merchant. Through the verification of the invention, the platform can verify the merchants performing the privacy calculation according to the public keys provided by the merchants, so as to improve the fraud cost of the merchants, improve the management capability of the platform and the quality of data provided by the merchants, and ensure that each merchant is truer and more credible in the aspect of privacy data calculation.
Description
Technical Field
The invention relates to the technical field of data source screening, in particular to a data source screening method and a data source screening system for preventing a merchant from doing disgust based on zero knowledge proof.
Background
sacre belongs to a privacy computing platform, and provides privacy data interactive computing under a safe and credible environment by two or more parties on the premise that data of each merchant cannot go out of the local area. The platform can carry out point accumulation excitation on the calculation among the merchants, and the point accumulation can improve the searching priority of the merchants on the platform and can occupy better resources of the platform.
Some merchants can conduct disguised calculation on the platform and obtain the credit reward of the platform on the premise of invalid calculation so as to improve self ranking.
Patent document CN114172655A (application number: CN202111310563.7) discloses a secure multiparty computation data system, method, device and data processing terminal, the secure multiparty computation data method includes: constructing a data model with standard acquisition and broadcast verification; a data pool is provided for verifying and storing the broadcasted data model; a hardware adapter is provided for converting data in the data pool into data suitable for secure multiparty computation; a novel evaluation and excitation mechanism built on a block chain is provided. However, the patent cannot effectively prevent the situation that the merchant performs MPC privacy calculation to acquire the platform points.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a data source screening method and system for preventing merchants from doing malicious activities based on zero knowledge proof.
The data source screening method for preventing the merchant from doing the malicious activities based on the zero-knowledge proof provided by the invention comprises the following steps:
step 1: generating a random value on a privacy computing platform, and sending the random value to a merchant server in an HTTPS mode;
and 2, step: in a merchant server, generating a corresponding calculation result value and a zero knowledge proof according to the random value;
and step 3: sending the calculation result value, the zero knowledge proof and the process data to a privacy calculation platform;
and 4, step 4: and verifying the calculation result value and the zero knowledge proof in the privacy calculation platform, and performing corresponding punishment on the merchant if the verification result does not meet the preset requirement.
Preferably, the random value is compressed into a 64-bit 16-ary number by a keccak256 hash algorithm, and the probability of hash collision is 0.
Preferably, the generation of the calculation result value includes:
will randomly value n 1 And taking the self public key pk as data input, and obtaining a hash value by using a hash function keccak256k1 algorithm for the data input: hash ═ keccak256k1 (n) 1 Pk), the resulting data is computed recursively until the output is less than the p-value in the elliptic curve algorithm secp256pk1 and is a certain point g on the curve 1 ＝(x，y)；
G is prepared from 1 Performing curve operation on the point and the private key sk to obtain a Y value, wherein the expression is as follows:
Y＝g 1 sk
the hash value is obtained as the calculation result value using the keccak256k1 algorithm for the Y value.
Preferably, the calculation of the zero knowledge proof comprises:
generating a random number: n is a radical of an alkyl radical 2 ＝nounce，n 2 ∈z q ，z q Representing a set of rational numbers;
u is calculated from the determined and publicly known parameter g in the secp256pk1 algorithm, and the expression is:
u＝g n2
according to g 1 Point and random value n 2 To calculate v, the expression is:
computing a zero knowledge proof, the expression of which is:
c＝Hash(g 1 ,pk,Y,u,v)mod p
proof＝n 2 -c*sk
wherein: mod represents the modulus; y, p, u, v, c are calculated intermediate values.
Preferably, the public keys pk and Y of the merchant are verified, whether pk and Y are points on the secp256k1 curve is checked, and then whether the calculated results of c, pk, proof and g points are consistent with the u value is mathematically verified, and the derivation process is as follows:
if the verification is passed, the merchant is indicated to be performing effective MPC privacy calculation.
The data source screening system for preventing the merchant from doing the malicious activities based on the zero-knowledge proof provided by the invention comprises:
module M1: generating a random value on a privacy computing platform, and sending the random value to a merchant server in an HTTPS mode;
module M2: in a merchant server, generating a corresponding calculation result value and a zero knowledge proof according to the random value;
module M3: sending the calculation result value, the zero knowledge proof and the process data to a privacy calculation platform;
module M4: and verifying the calculation result value and the zero knowledge proof in the privacy calculation platform, and if the verification result does not meet the preset requirement, performing corresponding punishment on the merchant.
Preferably, the random value is compressed into a 64-bit 16-ary number by a keccak256 hash algorithm, and the probability of hash collision is 0.
Preferably, the generation of the calculation result value includes:
will randomly value n 1 And taking the self public key pk as data input, and obtaining a hash value of the data input by using a hash function keccak256k1 algorithm: hash ═ keccak256k1 (n) 1 Pk), the resulting data is computed recursively until the output is less than the p value in the elliptic curve algorithm secp256pk1 and is at some point g on the curve 1 ＝(x，y)；
G is prepared from 1 Performing curve operation on the point and the private key sk to obtain a Y value, wherein the expression is as follows:
Y＝g 1 sk
the hash value is obtained as a calculation result value using the keccak256k1 algorithm for the Y value.
Preferably, the calculation of the zero knowledge proof comprises:
generating a random number: n is a radical of an alkyl radical 2 ＝nounce，n 2 ∈z q ，z q Representing a set of rational numbers;
u is calculated according to a determined and publicly known parameter g in the secp256pk1 algorithm, and the expression is as follows:
u＝g n2
according to g 1 Point and random value n 2 To calculate v, the expression is:
computing a zero knowledge proof, the expression of which is:
c＝Hash(g 1 ,pk,Y,u,v)mod p
proof＝n 2 -c*sk
wherein: mod represents the modulus; y, p, u, v, c are calculated intermediate values.
Preferably, the public keys pk and Y of the merchant are verified, whether pk and Y are points on the secp256k1 curve is checked, and then whether the calculated results of c, pk, proof and g points are consistent with the u value is mathematically verified, and the derivation process is as follows:
if the verification is passed, the merchant is indicated to be performing effective MPC privacy calculation.
Compared with the prior art, the invention has the following beneficial effects:
through the verification of the invention, the platform can verify the merchants performing privacy calculation according to the public keys provided by the merchants so as to improve the cost of the merchants, improve the management capability of the platform and the quality of data provided by the merchants, effectively inhibit the merchants from performing MPC privacy calculation to acquire platform points, and ensure that each merchant is truer and more credible in the aspect of privacy data calculation.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of data source addition computation.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will aid those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any manner. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the invention.
The embodiment is as follows:
after a data provider registers on a platform, a recoco client needs to be downloaded, the recoco client runs on a server where a client is located, after a certain deposit is submitted for an account number, when two parties or multiple parties carry out MPC operation, the recoco platform can carry out state inspection on the server clients of the parties at variable time, whether the two parties meet the platform rule or not is checked, and if a cheating behavior is found, the account number is frozen and the deposit provided is punished.
The system mainly comprises three systems of merchant registration, deposit service and platform system verification.
Merchant registration: the merchant registers as MPC computing merchant on the platform, adds corresponding label and description to display the data service which can be provided, and the merchant must provide its public key data information during registration.
Deposit service: if the registered merchant needs to provide data calculation, a corresponding deposit is provided to prevent the merchant from providing unreliable or fake data. In the case of the calculation process, which can prove that the merchant provides the untrusted data, the account number of the merchant is frozen, and the deposit is deducted to make up for the loss of the merchant in the calculation process.
Platform system verification: the platform verifies the servers computed by the merchant to ensure that spurious computations are not provided.
The verification process mainly comprises the following steps:
(1) a random value null is randomly generated by the sacre platform and is sent to the merchant server in an HTTPS mode, n 1 ＝nounce；
n 1 Represents a random value generated by the sacre platform server, and the result is a 64-bit 16-ary number generated by the keccak256 hashing algorithm of arbitrary values.
The Keccak256 algorithm can compress an input of arbitrary length into a 64-bit 16-ary number, and the probability of a hash collision is almost 0.
(2) And the merchant receives the random value to generate a corresponding calculation result value output and a proof of zero knowledge generation.
(a) Generating output procedure
N is to be 1 And a self public key (pk) as data input, and taking Hash by using a keccak256k1 method for the input.
Hash＝keccak256k1(n 1 Pk), recursively calculating the resulting data until the output is less than the p value in the secp256pk1 algorithm and is a certain point (x, y) on the curve;
g 1 ＝(x，y)
g is prepared from 1 Performing curve operation on the point and the private key (sk) to obtain a Y value;
Y＝g 1 sk
the hash is taken for Y using keccak256k1 as the output result, output.
(b) Generation of proof
Random secure generation of a random number: n is 2 ＝nounce，n 2 ∈z q ，z q Representing a set of rational numbers;
from the parameter g (g is determined and publicly known) in the secp256pk1 algorithm, u is calculated by the expression:
u＝g n2
according to g 1 Point and random value n 2 To calculate v, the expression is:
calculating proof, the expression is:
c＝Hash(g 1 ,pk,Y,u,v)mod p
proof＝n 2 -c*sk
mod represents the modulus; p represents a value generated when output is generated; sk represents a merchant private key; pk represents the public key of the merchant; g is the parameter g (determined and disclosed) in the secp256pk1 algorithm; c. u is the calculated median value.
(3) The merchant sends output and Y, c, u and proof to the sacre platform;
(4) and the platform verifies the result and proof to see whether the result is correct or not, and correspondingly penalizes the result which is incorrect for multiple times.
Verifying a public key and a Y point of a merchant, and checking whether pk and Y are points on a secp256k1 curve;
then, whether the calculation results of the points c, pk, proof and g are consistent with the u value or not is verified mathematically;
and (3) derivation process:
if the verification is passed, the merchant is indicated to be performing effective MPC privacy calculation.
The invention provides a data source screening system for preventing a merchant from doing malice based on zero knowledge proof, which comprises: module M1: generating a random value on a privacy computing platform, and sending the random value to a merchant server in an HTTPS mode; module M2: in a merchant server, generating a corresponding calculation result value and a zero knowledge proof according to the random value; module M3: sending the calculation result value, the zero knowledge proof and the process data to a privacy calculation platform; module M4: and verifying the calculation result value and the zero knowledge proof in the privacy calculation platform, and if the verification result does not meet the preset requirement, performing corresponding punishment on the merchant.
The random value is compressed into a 64-bit 16-ary number by a keccak256 hash algorithm, and the probability of hash collision is 0. The generation of the calculation result value includes: will randomly value n 1 And its own public key pk as data input, using a hash function kec on the data inputcak256k1 algorithm obtains hash values: hash ═ keccak256k1 (n) 1 Pk), the resulting data is computed recursively until the output is less than the p-value in the elliptic curve algorithm secp256pk1 and is a certain point g on the curve 1 (x, y); g is prepared from 1 Performing curve operation on the point and the private key sk to obtain a Y value, wherein the expression is as follows: g represents Y 1 sk (ii) a The hash value is obtained as the calculation result value using the keccak256k1 algorithm for the Y value.
The calculation of the zero knowledge proof includes: generating a random number: n is a radical of an alkyl radical 2 ＝nounce，n 2 ∈z q ，z q Representing a set of rational numbers; u is calculated from the determined and publicly known parameter g in the secp256pk1 algorithm, and the expression is: u-g n2 (ii) a According to g 1 Point and random value n 2 To calculate v, the expression is:
computing a zero knowledge proof, the expression of which is: hash (g) 1 ,pk,Y,u,v)mod p，proof＝n 2 -c sk; wherein: mod represents modulo; y, p, u, v, c are calculated intermediate values.
Firstly, verifying public keys pk and Y points of a merchant, verifying whether pk and Y are points on a secp256k1 curve, and then mathematically verifying whether the calculation results of c, pk, proof and g points are consistent with u values, wherein the derivation process comprises the following steps:
if the verification is passed, the merchant is indicated to be performing effective MPC privacy calculation.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the apparatus, and the modules thereof provided by the present invention may be considered as a hardware component, and the modules included in the system, the apparatus, and the modules for implementing various programs may also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A data source screening method for preventing a merchant from doing malicious activities based on zero-knowledge proof is characterized by comprising the following steps:
step 1: generating a random value on a privacy computing platform, and sending the random value to a merchant server in an HTTPS mode;
step 2: in a merchant server, generating a corresponding calculation result value and a zero knowledge proof according to the random value;
and 3, step 3: sending the calculation result value, the zero knowledge proof and the process data to a privacy calculation platform;
and 4, step 4: and verifying the calculation result value and the zero knowledge proof in the privacy calculation platform, and performing corresponding punishment on the merchant if the verification result does not meet the preset requirement.
2. The zero-knowledge-based data source screening method for proving prevention of merchant repugnance as claimed in claim 1, wherein the random value is compressed into a 64-bit 16-ary number by a keccak256 hash algorithm, and the probability of hash collision is 0.
3. The zero-knowledge proof merchant aversion-based data source screening method as claimed in claim 1, wherein the generating of the calculation result value comprises:
will randomly value n 1 And the self public key pk is used as data input, and a hash function keccak is used for the data inputThe 256k1 algorithm obtains the hash value: hash 256k1 (n) 1 Pk), the resulting data is computed recursively until the output is less than the p-value in the elliptic curve algorithm secp256pk1 and is a certain point g on the curve 1 ＝(x，y)；
G is prepared from 1 Performing curve operation on the point and the private key sk to obtain a Y value, wherein the expression is as follows:
Y＝g 1 sk
the hash value is obtained as a calculation result value using the keccak256k1 algorithm for the Y value.
4. The method of claim 3, wherein the computing of the zero knowledge proof comprises:
generating a random number: n is 2 ＝nounce，n 2 ∈z q ，z q Representing a set of rational numbers;
u is calculated from the determined and publicly known parameter g in the secp256pk1 algorithm, and the expression is:
u＝g n2
according to g 1 Point and random value n 2 To calculate v, the expression is:
computing a zero knowledge proof, the expression of which is:
c＝Hash(g 1 ,pk,Y,u,v)mod p
proof＝n 2 -c*sk
wherein: mod represents the modulus; y, p, u, v, c are calculated intermediate values.
5. The method for screening data sources based on zero-knowledge proof to prevent the merchant from doing malicious activities according to claim 4, wherein the public keys pk and Y of the merchant are verified, it is checked whether pk and Y are points on the secp256k1 curve, and then it is verified mathematically whether the calculated results of c, pk, proof, g points are consistent with the u value, the derivation process is:
if the verification is passed, the merchant is indicated to be performing effective MPC privacy calculation.
6. A data source screening system for preventing merchant fraud based on zero knowledge identification, comprising:
module M1: generating a random value on a privacy computing platform, and sending the random value to a merchant server in an HTTPS mode;
module M2: in a merchant server, generating a corresponding calculation result value and a zero knowledge proof according to the random value;
module M3: sending the calculation result value, the zero knowledge proof and the process data to a privacy calculation platform;
module M4: and verifying the calculation result value and the zero knowledge proof in the privacy calculation platform, and if the verification result does not meet the preset requirement, performing corresponding punishment on the merchant.
7. The zero-knowledge-based data source screening system for proving prevention of merchant repugnance as claimed in claim 6, wherein the random value is compressed into a 64-bit 16-ary number by a keccak256 hash algorithm, and the probability of hash collision is 0.
8. The zero-knowledge proof merchant aversion-based data source screening system as recited in claim 7, wherein the generating of the calculated result value comprises:
will randomly value n 1 And taking the self public key pk as data input, and obtaining a hash value by using a hash function keccak256k1 algorithm for the data input: hash ═ keccak256k1 (n) 1 Pk), the resulting data is computed recursively until the output is less than the p-value in the elliptic curve algorithm secp256pk1 and is a certain point g on the curve 1 ＝(x，y)；
G is prepared from 1 Performing curve operation on the point and the private key sk to obtain a Y value, wherein the expression is as follows:
Y＝g 1 sk
the hash value is obtained as the calculation result value using the keccak256k1 algorithm for the Y value.
9. The zero-knowledge proof merchant aversion-based data source screening system as recited in claim 8, wherein the zero-knowledge proof calculation comprises:
generating a random number: n is 2 ＝nounce，n 2 ∈z q ，z q Representing a set of rational numbers;
u is calculated according to a determined and publicly known parameter g in the secp256pk1 algorithm, and the expression is as follows:
u＝g n2
according to g 1 Point and random value n 2 To calculate v, the expression is:
computing a zero knowledge proof, the expression of which is:
c＝Hash(g 1 ,pk,Y,u,v)mod p
proof＝n 2 -c*sk
wherein: mod represents the modulus; y, p, u, v, c are calculated intermediate values.
10. The system of claim 9, wherein the public keys pk and Y of the merchant are verified, and it is checked whether pk and Y are points on the secp256k1 curve, and then it is mathematically verified whether the calculated results of c, pk, proof and g are consistent with the u value, and the derivation process is:
if the verification is passed, the merchant is indicated to be performing effective MPC privacy calculation.
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| CN114070561A (en) * | 2022-01-17 | 2022-02-18 | 工业信息安全(四川)创新中心有限公司 | Zero-knowledge proof method and system based on SM2 algorithm |
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