CN114003923A - Multi-party calculation data commodity construction method for realizing data commercial value - Google Patents
Multi-party calculation data commodity construction method for realizing data commercial value Download PDFInfo
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- CN114003923A CN114003923A CN202111133946.1A CN202111133946A CN114003923A CN 114003923 A CN114003923 A CN 114003923A CN 202111133946 A CN202111133946 A CN 202111133946A CN 114003923 A CN114003923 A CN 114003923A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
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Abstract
The invention belongs to the field of data calculation, in particular to a multi-party calculation data commodity construction method for realizing data commercial value, which comprises the following steps that S1, participants are divided into a data holder and a platform party; step S2, the data holder participates in calculation, the real mean value can not be obtained without leaking the data content, and the income is obtained after sale; step S3, the platform side holds the random number R to participate in the calculation, and all the participants obtain a multi-party mean value M according to the description of the mean value algorithm; step S4, removing R by (M × number of participants-R)/(number of participants-1) to obtain a true average value, which is sold as a data commodity; step S5, the data holder still needs to pay for the mean value because the real mean value is not obtained; step S6 is performed to divide the income. The invention can obtain the data mean value under the condition of protecting the data privacy of each data holder, thereby protecting the commercial value of the data commodity.
Description
Technical Field
The invention belongs to the field of data calculation, and particularly relates to a multi-party calculation data commodity construction method for realizing data commercial value.
Background
With the rapid development of the mobile internet in recent years, financial enterprises have many new changes and demands on professional services for customers, and for key data of some industries, yearbooks are generally published by industry associations according to annual statistics. Hysteresis of yearbook data for at least one year. The method provided by the document can greatly improve the timeliness of the key data release, and can be more quickly applied to business operation activities of enterprises and business of enterprise-related parties.
Every enterprise continuously generates data in the operation and production activities, key indexes in the data are regarded as important information of the enterprise, and the enterprise, a supervisor and a commercial bank pay attention to the operation indexes. The reality of the situation is that each enterprise does not want the own index to be known outside, and meanwhile, the enterprise also wants to obtain the average index of the industry as the operation reference. For the commercial bank, the enterprises with loan complaints can obtain the operation indexes under the authorization condition in the form of agreement, but the commercial bank needs the industry mean value to judge the operation condition of the enterprises. For example, the check-in rate of hotel enterprises, the raw material price of steel enterprises, the idle rate of transportation enterprises, the leaving rate and the like are all key indexes of the enterprises, but the recent conditions of the industry are difficult to obtain.
Disclosure of Invention
The invention aims to provide a multi-party calculation data commodity construction method for realizing data commercial value, and provides a data calculation method which can obtain a data mean value under the condition of protecting the data privacy of each data holder under the condition that a plurality of data holders and a platform party participate. The market party obtains the mean value for sale, thereby realizing the commercial value realization of the data and promoting the benign operation of the data production.
In order to achieve the purpose, the invention provides the following technical scheme: a multi-party calculation data commodity construction method for realizing data commercial value comprises the following steps:
step S1, dividing the participant into data holder and platform;
step S2, the data holder participates in calculation, does not leak data content and can not obtain a real mean value, obtains income after sale, and the platform part participates in calculation, can obtain a real mean value and is responsible for data sale and data distribution;
step S3, the platform side holds the random number R and participates in the calculation together with the data holder, and a multi-party mean M is obtained according to the description of the mean algorithm;
step S4, the platform side can remove R through (M + number of participants-R)/(number of participants-1) to obtain a real average value, and the real average value is used as a data commodity for sale;
step S5, the data holder still needs to pay for the mean value because the real mean value is not obtained;
step S6, income is divided and moisten;
preferably, there is one and only one platform in step S2, which facilitates the integration of data.
Preferably, the averaging algorithm in step S3 specifically includes the steps of:
step S11, n participants, wherein the number of each participant is recorded as M1, M2.
Step S12, each participant randomly decomposes the numerical value into the sum of n numbers;
step S13, each participant sorts the decomposition values and all participants and sends data components according to the sort;
step S14, each participant verifies and decrypts the received n-1 copies of encrypted content;
step S15, each participant adds the n-1 numerical values and y, encrypts and signs the result and sends the result to n-1 other participants;
and step S16, each participant decrypts and adds the received n-1 result signatures, adds the result signatures with the value sent by the participant in the step S15, and divides the result signatures by n to obtain a mean value.
Preferably, the specific steps of the participant sorting in step S13 are:
s111, encrypting the ith numerical value and sending the ith numerical value to the ith participant;
s112, encrypting the public key of the ith participant;
s113, signing the encrypted result by using a private key of the user;
and S114, recording the decomposition value pointing to the user as y, and participating in subsequent calculation.
Preferably, the number of data holders in step S2 should be greater than 2, otherwise other data holder values will be known. In particular applications, if a participant is aware of the enterprise value acceptance for another party, the calculation may be performed when the number of data holders equals 2.
Preferably, the protection strength of data privacy is such that other data holders collude to obtain the actual value of a certain holder.
Preferably, the calculation of the mean value commodity is divided into four rounds, wherein the first round is decomposition and transmission, all the participants decompose the value and transmit the decomposed value to other participants, and one share is reserved by the participants; the second round is encryption after transmission decryption, and the result of the encryption is sent to other participants; the third round is to add the received result and the value sent by the round on the platform, then the average value is calculated, and the fourth round is to calculate the real average value of the commodity for the platform side. .
Preferably, the data holder's view angle step content is six rounds, wherein the first round is the input own numerical value; the second round is that the own numerical value is decomposed and sent to other participants, and one share is reserved; the third round is to receive the decomposition value; the fourth round is that the received numerical value is added with the local reserved number, and the value is sent to the other participants; the fifth round is that the added value of the other participants is received and added with the numerical value sent by the last round; and the sixth round is to calculate the mean value.
Preferably, the two mean values obtained by four rounds of calculation are the mean value with the platform and the mean value without the platform, and the two mean values have different results.
Preferably, the encryption mentioned in steps S14, S15, S16, S111, S112 is to prevent possible falsification during transmission, and to remove the encryption link without affecting the calculation result of the mean value and causing data leakage of the data holder; the signature and the verification mentioned in the steps S14, S15, S16 and S113 are for confirming the digital identity of the calculation participant, and the preserved calculation process information can restore data responsibility afterwards; the mean value calculation result is not influenced by removing the signature verification link, and the data leakage of a data holder is not caused.
In summary, compared with the prior art, the method of the invention can obtain the data mean value under the condition of protecting the data privacy of each data holder under the condition that a plurality of data holders and one platform party participate, and meanwhile, the participating parties except the platform party cannot obtain the real mean value, thereby protecting the commercial value of the data commodity. The platform side obtains the mean value for sale, thereby realizing the commercial value reappearance of the data and promoting the benign operation of data production.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flowchart of the overall algorithm of the present invention;
FIG. 2 is a flow chart of a platform-side computing method of the present invention;
FIG. 3 is a flow chart of a method for calculating the mean value of the participants according to the present invention;
FIG. 4 is a flow chart of a participant ordering method of the present invention;
FIG. 5 is a graph of overall view mean calculation data in accordance with the present invention;
FIG. 6 is a view of the holder A with the calculated data.
Detailed Description
Embodiments of the present application will be described in detail with reference to the drawings and examples, so that how to implement technical means to solve technical problems and achieve technical effects of the present application can be fully understood and implemented.
Referring to fig. 1-6, the present invention provides a multi-party data commodity construction method for realizing data business value, which constructs a hypothetical holder A, B, C, and referring to fig. 5, a calculation table illustrates the calculation process of the mean value from the two points of view of the whole and a calculation process, respectively, because each participant obtains the calculated mean value, breaking the foundation of business application that all the largest potential users have obtained the value, and each party can output the value, and the final result results lead to the failure of continuous cooperation. The algorithm is modified, participation roles are distinguished, different responsibilities are borne, participants are divided into a data holder and a platform, the data holder participates in calculation and obtains data sales income, the platform participates in calculation and obtains a real mean value, the platform is responsible for data sales and distribution, the platform holds a random number R to participate in calculation, the data holder obtains a multi-party mean value M according to the description of the mean value algorithm, the encryption aims at preventing falsification possibility in the transmission process, the calculation result of the mean value is not influenced in the encryption link is removed, data leakage of the data holder cannot be caused, the signature and signature verification aims at confirming the digital identity of the calculation participants, and data pursuit can be restored after calculation process information is kept. The mean value calculation result is not influenced by removing the signature verification link, and the data leakage of a data holder is not caused.
Referring to fig. 2, a data holder calculates an average value, n participants each having a value M1, M2.. and Mn, each participant randomly decomposes the value into a sum of n numbers, sorts the decomposed value and all participants, encrypts the ith number and sends the ith number to the ith participant, the public key of the ith participant is used for encryption, and the encryption result is signed by using its own private key. And marking the decomposition value pointing to the participant as y, verifying and decrypting n-1 parts of the received encrypted content by each participant, then adding the n-1 values with y, encrypting and signing the result, and then sending the result to n-1 other participants, decrypting and adding the received n-1 results by each participant, adding the result to the value sent in the previous step by each participant, and dividing the result by n to obtain an average value.
The platform side holds a random number R to participate in calculation, and referring to FIG. 5, the average value of the platform calculation is divided into four rounds, wherein the first round is decomposition and transmission; the second round is encryption after transmission decryption; the third round is to add the received values and send out the values on the round, and calculate the mean value, and the fourth round is to calculate the mean value of the commodity for the platform.
Referring to fig. 6, the content of the perspective step of the holder a is six rounds, wherein the first round is the input own numerical value; the second round is the self-owned numerical decomposition; the third round is to receive the decomposition value; the fourth round is that the received numerical value is added with the local reserved number, and the value is sent to the other participants; the fifth round is to receive the sum of the other participants; and the sixth round is to calculate the mean value. The data holder obtains a multiparty mean M according to the algorithm described above, which is unusable due to the inclusion of R. The platform side knows R, and can obtain a real mean value by removing R through (M × number of participants-R)/(number of participants-1) and sell the real mean value as a data commodity. The data holder still needs to pay for the mean value because the real mean value is not obtained, and the commercial value of the mean value is guaranteed. Meanwhile, the economic benefits of data holders are guaranteed through income distribution.
The number of data holders should be greater than 2, otherwise the other data holder values will be known after the mean is purchased. In a specific application, if a participant knows that the enterprise numerical value is accepted by the other party, the algorithm can still be used when the number of data holders is equal to 2, and the protection strength of the algorithm on the data privacy is that other data holders can obtain the actual numerical value of a certain holder by collusion.
As used in the specification and in the claims, certain terms are used to refer to particular components. As one skilled in the art will appreciate, manufacturers may refer to a component by different names. This specification and claims do not intend to distinguish between components that differ in name but not function. In the following description and in the claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. "substantially" means within an acceptable error range, and a person skilled in the art can solve the technical problem within a certain error range to substantially achieve the technical effect.
It is noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a good or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such good or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a commodity or system that includes the element.
The foregoing description shows and describes several preferred embodiments of the invention, but as aforementioned, it is to be understood that the invention is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A multi-party calculation data commodity construction method for realizing data commercial value is characterized by comprising the following steps:
step S1, dividing the participant into data holder and platform;
step S2, the data holder participates in calculation, does not leak data content, cannot obtain a real mean value, and obtains income after sale; the platform side participates in calculation, can obtain a real mean value and is responsible for data sale and data distribution;
step S3, the platform side holds the random number R and participates in the calculation together with the data holder, and a multi-party mean M is obtained according to the description of the mean algorithm;
step S4, the platform side can remove R through (M + number of participants-R)/(number of participants-1) to obtain a real average value, and the real average value is used as a data commodity for sale;
step S5, the data holder still needs to pay for the mean value because the real mean value is not obtained;
step S6 is performed to divide the income.
2. A multi-party computed data commodity construction method for achieving data business value according to claim 1, characterized by: there is one and only one platform in step S2.
3. A multi-party computed data commodity construction method for achieving data business value according to claim 1, characterized by: the average algorithm in step S3 specifically includes the following steps:
step S11, n participants, wherein the number of each participant is recorded as M1, M2.
Step S12, each participant randomly decomposes the held numerical value Mi into the sum of n numerical values;
step S13, each participant sorts the decomposition values and all participants and sends data components according to the sort;
step S14, each participant verifies and decrypts the received n-1 copies of encrypted content;
step S15, each participant adds the n-1 numerical values and y, encrypts and signs the result and sends the result to n-1 other participants;
and step S16, each participant decrypts and adds the received n-1 result signatures, adds the result signatures with the value sent by the participant in the step S15, and divides the result signatures by n to obtain a mean value.
4. A multi-party computed data commodity construction method for achieving data business value according to claim 3, characterized in that: the specific steps of sending the data components after the sequencing in step S13 are:
s111, encrypting the ith numerical value and sending the ith numerical value to the ith participant;
s112, encrypting the public key of the ith participant;
s113, signing the encrypted result by using a private key of the user;
and S114, recording the decomposition value pointing to the user as y, and participating in subsequent calculation.
5. A multi-party computed data commodity construction method for achieving data business value according to claim 1, characterized by: the number of data holders in step S2 should be greater than 2, otherwise other data holder values will be known; in particular applications, if a participant is aware of the enterprise value acceptance for another party, the calculation may be performed when the number of data holders equals 2.
6. A multi-party computed data commodity construction method for achieving data business value according to claim 1, characterized by: the protection strength of data privacy is that other data holders can obtain the actual value of a certain holder in collusion.
7. A multi-party computed data commodity construction method for achieving data business value according to claim 1, characterized by: the calculation of the mean value commodity is divided into four rounds, wherein the first round is decomposition and sending, all participants decompose the value and send the decomposed value to other participants, and one share is reserved by the participants; the second round is encryption after transmission decryption, and the result of the encryption is sent to other participants; the third round is to sum the received results and calculate the mean value, and the fourth round is to calculate the true mean value of the commodity for the platform side.
8. A multi-party computed data commodity construction method for achieving data business value according to claim 7, characterized in that: the data holder visual angle step content is six rounds, wherein the first round is an input own numerical value; the second round is that the own numerical value is decomposed and sent to other participants, and one share is reserved; the third round is to receive the decomposition value; the fourth round is that the received numerical value is added with the local reserved number, and the value is sent to the other participants; the fifth round is to receive the sum of the other participants; and the sixth round is to calculate the mean value.
9. A multi-party computed data commodity construction method for achieving data business value according to claim 7, characterized in that: the two mean values obtained by four-wheel calculation are respectively a platform-containing mean value and a platform-free mean value, and the results of the two mean values are different.
10. The multi-party computing data commodity construction method for realizing data commercial value according to claim 4, characterized in that: the encryption mentioned in steps S14, S15, S16, S111, S112 aims to prevent possible falsification during transmission, and removes the calculation result that the encryption link does not affect the mean value, and does not cause data leakage of the data holder; the signature and the signature verification mentioned in the steps S14, S15, S16 and S113 aim to confirm the digital identity of the calculation participant, and the reserved calculation process information can be responsible for afterward; the mean value calculation result is not influenced by removing the signature verification link, and the data leakage of a data holder is not caused.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115037436A (en) * | 2022-04-29 | 2022-09-09 | 北京龙腾佳讯科技股份公司 | Method and system for secure multiparty calculation of data mean |
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- 2021-09-27 CN CN202111133946.1A patent/CN114003923A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115037436A (en) * | 2022-04-29 | 2022-09-09 | 北京龙腾佳讯科技股份公司 | Method and system for secure multiparty calculation of data mean |
| CN115037436B (en) * | 2022-04-29 | 2023-09-29 | 北京龙腾佳讯科技股份公司 | Method and system for calculating data mean value by using safe multiple parties |
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