A method for constructing multi-party computed data commodities to realize the commercial value of data

Abstract

The present invention belongs to the field of data computing, and specifically relates to a method for constructing a multi-party computing data commodity to realize the commercial value of data. Step S1: Distinguish the participating parties into data holders and platform parties; Step S2: The data holders participate in the calculation, without leaking the data content and unable to obtain the true mean value, and obtain benefits after sales; Step S3: The platform party holds the random number R and participates in the calculation, and all participating parties obtain the multi-party mean value M according to the description of the mean algorithm; Step S4: The true mean value can be obtained by removing R through (M * the number of participating parties - R) / (the number of participating parties - 1) and sold as a data commodity; Step S5: Since the data holders do not obtain the true mean value, they still need to pay for the mean value; Step S6: Conduct revenue sharing. The present invention can obtain the data mean value while protecting the data privacy of each data holder, and protects the commercial value of the data commodity.

Description

Technical Field

[0001] The present invention belongs to the field of data computing, and specifically is a multi-party computing data commodity construction method for realizing the commercial value of data. Background Art

[0002] With the rapid development of mobile Internet in recent years, financial enterprises have also had many new changes and demands in their professional services to customers. For some key data of industries, industry associations generally publish yearbooks on an annual basis. The lag of yearbook data is at least one year. The method proposed in this document can greatly improve the timeliness of key data release and can be applied to corporate business activities and the business of corporate stakeholders more quickly.

[0003] Every enterprise continuously generates data in its business and production activities. The key indicators in these data are regarded as important information of the enterprise. Enterprises, regulators, and commercial banks attach great importance to these operating indicators. The reality is that every enterprise does not want its indicators to be known to the outside world, but at the same time hopes to obtain the average indicators of the industry as a reference for operation. For commercial banks, enterprises with loan demands can obtain operating indicators under authorization in the form of agreements, but commercial banks need industry averages to judge the operating conditions of enterprises. For example, the occupancy rate of hotel enterprises, the raw material prices of steel enterprises, the empty load rate and turnover rate of transportation enterprises are all key indicators of enterprises, but the recent situation of the industry is difficult to obtain. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-party computing data commodity construction method for realizing the commercial value of data. The present invention provides a data calculation method, which can obtain the data mean while protecting the data privacy of each data holder under the participation of multiple data holders and a platform party. The market party obtains the mean and sells it, thereby realizing the commercial value of data and promoting the healthy operation of data production.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for constructing multi-party computing data commodities to realize the commercial value of data, the steps are as follows:

[0006] Step S1, dividing the participants into data holders and platform parties;

[0007] Step S2: Data holders participate in the calculation, which will not leak data content and cannot obtain the true mean value. They will obtain profits after the sale. The platform party participates in the calculation, which can obtain the true mean value and is responsible for data sales and profit sharing.

[0008] Step S3: The platform holds a random number R and participates in the calculation with the data holder, and obtains the multi-party mean M according to the mean algorithm description;

[0009] In step S4, the platform party can obtain the true mean by removing R from (M * number of participants - R) / (number of participants - 1) and sell it as a data product to the outside world.

[0010] In step S5, since the data holder does not obtain the true mean, it still needs to pay for the mean.

[0011] In step S6, revenue sharing is carried out.

[0012] Preferably, there is exactly one platform party in step S2, which is convenient for data integration.

[0013] Preferably, the specific steps of the mean algorithm in step S3 are as follows:

[0014] In step S11, for n participants, the value of each participant is denoted as M1, M2,..., Mn.

[0015] In step S12, each participant randomly decomposes the value into the sum of n numbers.

[0016] In step S13, each participant sorts the decomposed values and all participants, and sends the data components according to the sorting.

[0017] In step S14, each participant verifies the signature of the n - 1 encrypted contents received and decrypts them.

[0018] In step S15, each participant adds these n - 1 values to y, encrypts and signs the result, and sends it to n - 1 other participants.

[0019] In step S16, each participant verifies the signature, decrypts and adds the n - 1 results received, adds them to the value sent by itself in step S15, and then divides by n to obtain the mean.

[0020] Preferably, the specific steps of the participant sorting in step S13 are as follows:

[0021] S111, encrypt the i-th value and send it to the i-th participant.

[0022] S112, use the public key of the i-th participant for encryption.

[0023] S113, sign the encrypted result with its own private key.

[0024] S114, denote the decomposed value pointed to itself as y and participate in subsequent calculations.

[0025] Preferably, the number of the data holders in step S2 should be greater than 2, otherwise the values of other data holders will be known. In specific applications, if the participants accept that another party knows the value of their enterprise, the calculation can be carried out when the number of data holders is equal to 2.

[0026] Preferably, the protection intensity of data privacy is such that the actual value of a certain holder can only be obtained through collusion among other data holders.

[0027] Preferably, the calculation of the average commodity is divided into four rounds. In the first round, it is decomposed and sent. All participating parties decompose the value and send the decomposed value to other participating parties, while keeping one copy for themselves. In the second round, it is added after decryption during transmission and the added result is sent to other participating parties. In the third round, the result received and the value sent in the previous round by oneself are added, and then the average value is calculated. In the fourth round, the platform party calculates the true average value of the commodity.

[0028] Preferably, the content of the steps from the perspective of the data holder is six rounds. In the first round, the self-owned value is input. In the second round, the self-owned value is decomposed and sent to other participating parties, while keeping one copy. In the third round, the decomposed value is received. In the fourth round, the received value is added to the locally reserved number and this value is sent to the remaining participating parties. In the fifth round, the added value received from the remaining participating parties is added to the value sent in the previous round by oneself. In the sixth round, the average value is calculated.

[0029] Preferably, two average values are obtained through four rounds of calculation, namely the average value including the platform and the average value without the platform, and the results of the two average values are different.

[0030] Preferably, the purpose of encryption mentioned in steps S14, S15, S16, S111, and S112 is to prevent possible tampering during transmission. Removing the encryption link does not affect the calculation result of the average value and will not cause data leakage of the data holder. The purpose of signature and signature verification mentioned in steps S14, S15, S16, and S113 is to confirm the digital identity of the calculation participating parties, and retaining the calculation process information can restore the data for accountability afterwards. Removing the signature verification link does not affect the calculation result of the average value and will not cause data leakage of the data holder.

[0031] In summary, compared with the prior art, the method of the present invention can obtain the data average value while protecting the data privacy of each data holder in the case of multiple data holders and one platform party participating. At the same time, the participating parties other than the platform party cannot obtain the true average value, protecting the commercial value of the data commodity. The platform party obtains the average value for sales, thus realizing the realization of the commercial value of the data and promoting the healthy operation of data production. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0033] Figure 1 is the overall algorithm flowchart of the present invention;

[0034] Figure 2 Flow chart of the calculation method for the platform side of the present invention;

[0035] Figure 3 Flow chart of the calculation method for the average value of the participating parties of the present invention;

[0036] Figure 4 Flow chart of the sorting method for the participating parties of the present invention;

[0037] Figure 5 Data graph for calculating the average value from the overall perspective of the present invention;

[0038] Figure 6 Data graph for calculation from the perspective of Holder A of the present invention. Detailed implementation manners

[0039] The following will describe in detail the implementation manners of the present application in conjunction with the accompanying drawings and embodiments, so as to fully understand how the present application uses technical means to solve technical problems and achieve the implementation process of technical effects and implement accordingly.

[0040] Please refer to Figure 1-6 , the present invention provides a method for constructing a multi-party calculation data product to realize the commercial value of data, constructs assumed holders A, B, and C, and refers to Figure 5 , the calculation table illustrates the calculation process of the average value from two perspectives: the overall perspective and the calculation process from the perspective of A. Since each participating party has obtained the calculated average value, it has damaged the basis of commercial applications - all the largest potential users have obtained the values, and each party can output this value, resulting in the inability to continue cooperation in the end. Therefore, the algorithm is modified to distinguish the participating roles and assume different responsibilities. The participating parties are divided into data holders and the platform side. The data holders participate in the calculation and obtain the data sales revenue. The platform side participates in the calculation, obtains the true average value, is responsible for data sales and profit sharing. The platform side holds the random number R to participate in the calculation. The data holders obtain the multi-party average value M according to the description of the average value algorithm. The purpose of encryption is to prevent possible tampering during the transmission process. Removing the encryption link does not affect the calculation result of the average value and will not cause data leakage of the data holders. The purpose of signature and signature verification is to confirm the digital identities of the calculation participating parties. Retaining the calculation process information can restore the data for accountability afterwards. Removing the signature verification link does not affect the calculation result of the average value and will not cause data leakage of the data holders.

[0041] Refer to Figure 2, the data holder calculates the mean. There are n participating parties, and the values of each participating party are denoted as M1, M2,..., Mn. Each participating party randomly decomposes its value into the sum of n numbers. Each participating party sorts the decomposed values and all participating parties, and encrypts the i-th value and sends it to the i-th participating party. The encryption uses the public key of the i-th participating party, and the signature is made using its own private key for the encrypted result. The decomposed value pointing to itself is denoted as y. Each participating party verifies the signature of the n - 1 encrypted contents received, decrypts them, then adds these n - 1 values to y, encrypts and signs the result and sends it to the n - 1 other participating parties. Each participating party decrypts and adds the n - 1 results received, then adds the result to the value sent in the previous step, and divides by n to obtain the mean.

[0042] The platform holder participates in the calculation with the random number R. Refer to Figure 5 , the platform calculates the mean in four rounds. The first round is decomposition and sending; the second round is adding after transmission and decryption; the third round is adding the values received and the value sent in the previous round by itself, and calculating the mean; the fourth round is the platform calculating the mean of the goods.

[0043] Refer to Figure 6 , the content of the steps from the perspective of holder A is six rounds. The first round is inputting its own value; the second round is decomposing its own value; the third round is receiving the decomposed values; the fourth round is adding the received values to the locally reserved number and sending this value to the other participating parties; the fifth round is receiving the added values from the other participating parties; the sixth round is calculating the mean. The data holder obtains the multi-party mean M according to the algorithm described above. This value cannot be used because R is mixed in. Since the platform holder knows R, it can remove R by (M * number of participating parties - R) / (number of participating parties - 1) to obtain the true mean and sell it as a data product to the outside world. Since the data holder does not obtain the true mean, it still needs to pay for the mean, which guarantees the commercial value of the mean. At the same time, through revenue sharing, the economic interests of the data holder are guaranteed.

[0044] The number of data holders should be greater than 2, otherwise, the values of other data holders will be known after purchasing the mean. In specific applications, if the participating parties accept that another party knows the values of their enterprises, the algorithm can still be used when the number of data holders is equal to 2. The protection intensity of this algorithm for data privacy is that the actual value of a certain holder can only be obtained through collusion among other data holders.

[0045] As used in the specification and claims, certain terms are used to refer to specific components. Those skilled in the art should understand that hardware manufacturers may use different terms to refer to the same component. The specification and claims do not use the difference in names as a way to distinguish components, but rather use the difference in the functions of components as the criterion for distinction. As used throughout the specification and claims, "comprising" is an open-ended term and should be interpreted as "including but not limited to". "Substantially" means within an acceptable error range. Those skilled in the art can solve the technical problem within a certain error range and basically achieve the technical effect.

[0046] It should be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a commodity or system including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such commodity or system. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the commodity or system including the said element.

[0047] The above description shows and describes several preferred embodiments of the present invention. However, as mentioned above, it should be understood that the present invention is not limited to the form disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be changed within the scope of the inventive concept described herein through the above teachings or the technology or knowledge in the relevant field. And any changes and modifications made by those skilled in the art without departing from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A method for constructing a multi-party computing data commodity to realize the commercial value of data, characterized in that, The steps are as follows: Step S1: Distinguish the participants into data holders and platform parties; Step S2: The data holders participate in the calculation, without leaking the data content and unable to obtain the true mean value, and obtain benefits after the sale; The platform parties participate in the calculation, can obtain the true mean value, and are responsible for data sales and profit sharing; Step S3: The platform party holds the random number R and jointly participates in the calculation with the data holders, and obtains the multi-party mean value M according to the description of the mean algorithm; Step S4: The platform party can remove R through (M * number of participants - R) / (number of participants - 1) to obtain the true mean value, and sell it as a data product to the outside world; Step S5: Since the data holders do not obtain the true mean value, they still need to pay for the mean value; Step S6: Conduct income profit sharing; The specific steps of the mean algorithm in Step S3 are: Step S11: For n participants, the value of each participant is denoted as M1, M2,..., Mn; Step S12: Each participant randomly decomposes the held value Mi into the sum of n numbers; Step S13: Each participant sorts the decomposed values and all participants, and sends the data components according to the sorting; Step S14: Each participant verifies the signature of the n - 1 encrypted contents received and then decrypts them; Step S15: Each participant adds the n - 1 values to y, encrypts and signs the result, and sends it to the other n - 1 participants, where y is the decomposed data of the participant pointing to itself; Step S16: Each participant verifies the signature, decrypts and adds the n - 1 results received, adds them to the value sent by itself in Step S15, and then divides by n to obtain the mean value.

2. The method for constructing a multi-party computing data commodity to realize the commercial value of data according to claim 1, characterized in that: There is exactly one platform party in Step S2.

3. The method for constructing a multi-party computing data commodity to realize the commercial value of data according to claim 1, characterized in that: The specific steps of sending the data components after sorting in Step S13 are: S111: Encrypt the i-th value and send it to the i-th participant; S112: Use the public key of the i-th participant for encryption; S113: Sign the encrypted result with one's own private key; S114: Denote the decomposed value pointing to oneself as y and participate in the subsequent calculation.

4. The method for constructing a multi-party computing data commodity to realize the commercial value of data according to claim 1, characterized in that: The number of the data holders in Step S2 should be greater than 2, otherwise the values of other data holders will be known; In specific applications, if the participants accept that another party knows the value of their enterprise, the calculation can be carried out when the number of data holders is equal to 2.

5. The method for constructing a multi-party computing data commodity to realize the commercial value of data according to claim 1, characterized in that: The protection intensity of data privacy is that only when other data holders collude can they obtain the actual value of a certain holder.

6. The method for constructing a multi-party computing data commodity to realize the commercial value of data according to claim 1, characterized in that: The calculation of the mean value product is divided into four rounds. In the first round, it is decomposition and sending. All participants decompose the values and send the decomposed values to other participants, and keep one copy for themselves; In the second round, it is adding after transmission and decryption, and sending the added result to other participants; In the third round, it is adding the received results and calculating the mean value; In the fourth round, the platform party calculates the true mean value of the product.

7. The method for constructing a multi-party computing data commodity to realize the commercial value of data according to claim 6, characterized in that: The content of the steps from the perspective of the data holders is six rounds. In the first round, input one's own value; In the second round, decompose one's own value and send it to other participants, and keep one copy; In the third round, receive the decomposed values; In the fourth round, add the received values to the locally reserved number and send this value to the remaining participants; In the fifth round, receive the added values of the remaining participants; In the sixth round, calculate the mean value.

8. The method for constructing a multi-party computing data commodity to realize the commercial value of data according to claim 6, characterized in that: Among the four rounds of calculations, two means are obtained, namely the mean with the platform and the mean without the platform, and the results of the two means are different.

9. The method for constructing a multi-party computing data commodity to realize the commercial value of data according to claim 3, characterized in that:The encryption mentioned in steps S14, S15, S16, S111, and S112 aims to prevent possible tampering during the transmission process. Removing the encryption link does not affect the calculation result of the mean and will not cause data leakage of the data holder; the signing and signature verification mentioned in steps S14, S15, S16, and S113 aim to confirm the digital identities of the calculation participants, and retaining the calculation process information can be used for accountability afterwards; removing the signature verification link does not affect the calculation result of the mean and will not cause data leakage of the data holder.

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