CN113315631B - Data processing method and device and data processing device - Google Patents
Data processing method and device and data processing device Download PDFInfo
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Abstract
The embodiment of the invention provides a data processing method and device and a device for data processing. In the method, secret sharing factors of target polynomial coefficients in a target polynomial set are obtained, wherein the target polynomial set is generated according to a target relation required to be met by preprocessed data to be generated, the target relations are met among the target polynomials in the target polynomial set, and the target relation is a preset relation corresponding to the data type of the preprocessed data. And constructing a sub polynomial corresponding to the target polynomial according to the secret sharing factor of the target polynomial coefficient. And calculating the output value of the sub-polynomial according to the sub-polynomial and the local input value to obtain the secret sharing factor of the preprocessed data. Therefore, the generation of the preprocessed data can be safely and conveniently realized, and the generation efficiency of the preprocessed data can be improved to a certain extent.
Description
Technical Field
The present invention relates to the field of network technologies, and in particular, to a data processing method and apparatus, and an apparatus for data processing.
Background
At present, preprocessing data required by an online stage is often generated through the offline stage, so that the complexity of online processing is reduced, and the online stage is optimized. In the multi-party security computation, the computation process in the online stage is often combined with the pre-processed data generated in advance in the offline stage to implement online computation, so that the computation efficiency is improved, and the processing complexity is reduced.
In the online calculation process, a large amount of various preprocessing data needs to be consumed, and how to generate the preprocessing data efficiently becomes a problem which needs to be solved urgently.
Disclosure of Invention
The embodiment of the invention provides a data processing method and device and a device for data processing, which aim to improve the generation efficiency of preprocessed data.
In order to solve the above problem, an embodiment of the present invention discloses a data processing method, which is applied to a computing node in a multi-party secure computing system, where the multi-party secure computing system includes multiple computing nodes, and the multiple computing nodes are used to cooperatively complete a multi-party secure computing task, and the method includes:
acquiring a secret sharing factor of a target polynomial coefficient in a target polynomial set, wherein the target polynomial set is generated according to a target relationship required to be met by preprocessed data to be generated, the target relationship is met among target polynomials in the target polynomial set, and the target relationship is a preset relationship corresponding to the data type of the preprocessed data;
constructing a sub polynomial corresponding to the target polynomial according to the secret sharing factor of the target polynomial coefficient;
and calculating the output value of the sub polynomial according to the sub polynomial and the local input value to obtain the secret sharing factor of the preprocessed data.
In another aspect, an embodiment of the present invention discloses a data processing apparatus, where the apparatus is applied to a computing node in a multi-party secure computing system, where the multi-party secure computing system includes multiple computing nodes, and the multiple computing nodes are used to cooperatively complete a multi-party secure computing task, and the apparatus includes:
the secret sharing method comprises the following steps that an obtaining module is used for obtaining a secret sharing factor of a target polynomial coefficient in a target polynomial set, wherein the target polynomial set is generated according to a target relation required to be met by preprocessed data to be generated, the target relation is met among target polynomials in the target polynomial set, and the target relation is a preset relation corresponding to the data type of the preprocessed data;
the construction module is used for constructing a sub polynomial corresponding to the target polynomial according to the secret sharing factor of the target polynomial coefficient;
and the calculating module is used for calculating the output value of the sub-polynomial according to the sub-polynomial and the local input value to obtain the secret sharing factor of the preprocessed data.
In yet another aspect, an embodiment of the present invention discloses an apparatus for data processing, which is applied to a computing node in a multi-party secure computing system, where the multi-party secure computing system includes a plurality of computing nodes, and the plurality of computing nodes are configured to cooperate to complete a multi-party secure computing task, the apparatus includes a memory, and one or more programs, where the one or more programs are stored in the memory, and configured to be executed by one or more processors, and the one or more programs include instructions for:
acquiring a secret sharing factor of a target polynomial coefficient in a target polynomial set, wherein the target polynomial set is generated according to a target relationship required to be met by preprocessed data to be generated, the target relationship is met among target polynomials in the target polynomial set, and the target relationship is a preset relationship corresponding to the data type of the preprocessed data;
constructing a sub polynomial corresponding to the target polynomial according to the secret sharing factor of the target polynomial coefficient;
and calculating the output value of the sub polynomial according to the sub polynomial and the local input value to obtain the secret sharing factor of the preprocessed data.
In yet another aspect, an embodiment of the invention discloses a machine-readable medium having stored thereon instructions, which, when executed by one or more processors, cause an apparatus to perform a data processing method as described in one or more of the preceding.
The embodiment of the invention has the following advantages:
in the data processing method of the embodiment of the invention, a secret sharing factor of target polynomial coefficients in a target polynomial set is obtained, wherein the target polynomial set is generated according to a target relationship required to be met by preprocessed data to be generated, the target polynomials in the target polynomial set meet the target relationship, and the target relationship is a preset relationship corresponding to the data type of the preprocessed data. And constructing a sub polynomial corresponding to the target polynomial according to the secret sharing factor of the target polynomial coefficient. And calculating the output value of the sub-polynomial according to the sub-polynomial and the local input value to obtain the secret sharing factor of the preprocessed data. Therefore, the secret sharing factor of the preprocessed data can be generated only by constructing the sub-polynomial according to the secret sharing factor of the acquired target polynomial coefficient and calculating the output value of the sub-polynomial, and the generation of the preprocessed data can be safely and conveniently realized, so that the generation efficiency of the preprocessed data can be improved to a certain extent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a flow chart of the steps of one data processing method embodiment of the present invention;
FIG. 2 is a schematic process diagram provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of another exemplary process provided by embodiments of the present invention;
FIG. 4 is a block diagram of an embodiment of a data processing apparatus of the present invention;
FIG. 5 is a block diagram of an apparatus 800 for data processing of the present invention; and
fig. 6 is a schematic diagram of a server in some embodiments of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Method embodiment
Referring to fig. 1, a flowchart illustrating steps of an embodiment of a data processing method according to the present invention is shown, applied to a computing node in a multi-party secure computing system, where the multi-party secure computing system includes a plurality of computing nodes, and the computing nodes are configured to cooperate to complete a multi-party secure computing task, and the method includes the following steps:
And 102, constructing a sub polynomial corresponding to the target polynomial according to the secret sharing factor of the target polynomial coefficient.
And 103, calculating an output value of the sub-polynomial according to the sub-polynomial and the local input value to obtain a secret sharing factor of the preprocessed data.
In the embodiment of the present invention, the computing nodes in the multi-party secure computing system may include a terminal, a server, and other devices with computing capabilities. The target polynomial coefficients may be coefficients contained in the target polynomial. In an alternative embodiment, for a target polynomial coefficient, a compute node may obtain a secret sharing factor in the target polynomial coefficient. Correspondingly, a sub-polynomial constructed by the computing nodes based on the secret sharing factor of the target polynomial coefficient can represent a part of the target polynomial, and the sub-polynomial constructed by all the computing nodes can recover the target polynomial.
It can be understood that the preset relationships corresponding to different data types may be different, and the preset relationships corresponding to the respective data types may be preset. By way of example, the data types may include a ternary type (beaver), an exponential type, a natural logarithmic type,and so on. Accordingly, in the case that the data type is a triplet type, the preset relationship may be: and a, b and c, wherein a, b and c form preprocessed data in a triad type. The triple-tuple type preprocessed data can be applied to the multiplication processing of data in the multi-party security calculation process. In the case that the data type is exponential, the preset relationship may be: λ, λ-1，…，λ-q. In the case that the data type is a natural logarithm type, the preset relationship may be: λ, e-iλ，…，e-iλq. The specific value of q may be set according to actual requirements, which is not limited in the embodiment of the present invention, and the exponent/natural logarithm preprocessed data may be applied to multi-form calculation and natural exponent power calculation. Furthermore, by setting the preset relationship between the target polynomials in the target polynomial set which corresponds to the preprocessed data to be generated at present, the preprocessed data generated based on the target polynomial coefficients can be ensured to a certain extent, the target relationship required to be met is met, and the accuracy of the preprocessed data is ensured.
Further, the preprocessed data may be used to protect the raw data, i.e., the raw data may be computationally processed, e.g., multiplied, added, etc., using the preprocessed data. After the original data is protected, the original data can be further subjected to subsequent calculation processing. The raw data may include information related to the user in the network platform, such as age information, height information, income information, loan information, historical access information, and the like, which is not limited in this embodiment of the present invention. Generating the preprocessed data in the form of the secret sharing factor based on the plurality of computing nodes corresponds to storing the preprocessed data in the plurality of computing nodes in a distributed manner. In this way, it is possible to ensure the security of the generated preprocessed data while achieving the generation of the preprocessed data.
In the data processing method of the embodiment of the invention, a secret sharing factor of target polynomial coefficients in a target polynomial set is obtained, wherein the target polynomial set is generated according to a target relationship required to be met by preprocessed data to be generated, the target polynomials in the target polynomial set meet the target relationship, and the target relationship is a preset relationship corresponding to the data type of the preprocessed data. And constructing a sub polynomial corresponding to the target polynomial according to the secret sharing factor of the target polynomial coefficient. And calculating the output value of the sub-polynomial according to the sub-polynomial and the local input value to obtain the secret sharing factor of the preprocessed data. Therefore, the secret sharing factor of the preprocessed data can be generated only by constructing the sub-polynomial according to the secret sharing factor of the acquired target polynomial coefficient and calculating the output value of the sub-polynomial, and the generation of the preprocessed data can be safely and conveniently realized, so that the generation efficiency of the preprocessed data can be improved to a certain extent.
Optionally, in an implementation manner of the embodiment of the present invention, the operation of obtaining the secret sharing factor of the target polynomial coefficient in the target polynomial set may be implemented by the following steps:
step S21, obtaining, from the trusted device, a secret sharing factor of the target polynomial coefficient in the set of target polynomials generated and shared in secret by the trusted device.
In embodiments of the present invention, the trusted device may be a third party device independent of the multi-party secure computing system. Accordingly, in this implementation, the operation of generating the target polynomial set according to the target relationship that needs to be satisfied by the pre-processing data to be generated may be performed by the trusted device, and further, after the target polynomial set is generated, the trusted device may perform secret sharing on each target polynomial in the target polynomial set, that is, extract the target polynomial coefficient of each target polynomial in the target polynomial set, and share the secret to each computing node. For example, the secret sharing may be to divide the target polynomial coefficient into n secret sharing factors and then send the secret sharing factors to each computing node. Where n may be the number of compute nodes. Accordingly, each computing node can obtain the secret sharing factor of the target polynomial coefficient by receiving the secret sharing content of the trusted device, namely, the secret sharing factor of the target polynomial coefficient.
In this way, since the computing nodes in the multi-party secure computing system do not need to execute generation operation by themselves, and only need to directly acquire the secret sharing factor of the target polynomial coefficient shared by the trusted device in secret, the efficiency of acquiring the secret sharing factor of the target polynomial coefficient can be improved to a certain extent.
Or, in another implementation manner of the embodiment of the present invention, the operation of obtaining the secret sharing factor of the target polynomial coefficients in the target polynomial set may also be implemented by the following steps:
step S31, negotiating with other computing nodes in the multiple computing nodes that cooperate to complete a multi-party secure computing task to generate a secret sharing factor of the target polynomial coefficient in the target polynomial set.
The operation of generating the secret sharing factor of the target polynomial coefficients in the target polynomial set may be a multi-party secure computation task. In specific implementation, the secret sharing factor of the target polynomial coefficient can be generated according to a secret mode based on a plurality of computing nodes. That is, a plurality of computing nodes in the multi-party secure computing system cooperate with each other by multiple parties to complete an operation of generating a target polynomial set according to a target relationship that needs to be satisfied by preprocessed data to be generated, and then cooperate with each other to complete an operation of sharing a target polynomial coefficient of each target polynomial secretly to each computing node, for example, each computing node may share secretly some held target polynomial coefficients and send an obtained secret sharing factor to each computing node, so that each computing node obtains the secret sharing factor of the target polynomial coefficient. The multi-party collaboration may refer to computation or processing based on a security computation scheme such as multi-party security computation or homomorphic encryption. Further, since a single participant in the multi-party collaboration does not obtain the full amount of the target polynomial, the security of the target polynomial can be ensured to a certain extent.
In this way, without setting additional trusted devices, the secret sharing factor of the target polynomial coefficient can be obtained only by performing negotiation generation through a plurality of computing nodes included in the multi-party secure computing system. Therefore, the hardware implementation cost can be reduced while ensuring the safety to a certain extent.
It should be noted that, in the embodiment of the present invention, the processes of generating the secret sharing factor of the target polynomial set and the target polynomial coefficient may both be implemented in a manner based on ciphertext calculation, that is, the intermediate generation process may be in a form of ciphertext. Further, the plaintext of the secret sharing factor may be decrypted at each computing node (i.e., participant) after the target polynomial coefficients are generated.
Optionally, the operation of generating the target polynomial set according to the target relationship that needs to be satisfied by the preprocessed data to be generated may be implemented by the following steps:
step S41, generating points of a target polynomial according to the target relation; each point comprises a first coordinate value and a second coordinate value, the second coordinate values of the points of different target polynomials conform to the target relationship, and the first coordinate values of the points of different target polynomials conform to a preset specified relationship.
The point in the embodiment of the present invention may be a coordinate point, the first coordinate value may be an abscissa value of the point, and the second coordinate value may be an ordinate value of the point. The designated relationship may be set according to actual requirements, for example, the designated relationship may be that the numerical values are the same, the designated values are sequentially added between the numerical values, and the like. Specifically, when the points are generated, the abscissa values of the same group of points of each target polynomial may satisfy a predetermined relationship, and the same group of points may satisfy a target relationship between the ordinate values. Alternatively, the abscissa values of all the points may be set to satisfy a prescribed relationship, and for example, the coordinate values of the respective points may be the same. Further, since the ordinate values may characterize the output values of the polynomials, by ensuring that the second coordinate values of the points of different target polynomials conform to the target relationship, the output values of the target polynomials subsequently constructed based on these points may conform to the target relationship.
In specific implementation, a point on one target polynomial may be generated first, and then a point on the next target polynomial may be generated according to the abscissa value, the ordinate value, the specified relationship, and the target relationship of the point, and by analogy, a point on each target polynomial may be generated, thereby obtaining a group of points. The specific value of the number p of points generated for one target polynomial may be set according to actual requirements, which is not limited in the embodiment of the present invention.
Taking the example that the operation of generating the target polynomial set is completed by cooperation among a plurality of computing nodes of the multi-party secure computing task, the data type of the preprocessed data is a triplet type, and the designated relationship is the same as a numerical value, a random number can be generated by cooperation of a plurality of parties to serve as an abscissa H _ a1 and an ordinate V _ a1 of a point on the polynomial a, so as to obtain the point (H _ a1, V _ a 1). Then, the parties cooperatively generate random numbers as an abscissa H _ a2 and an ordinate V _ a2 of a point on the polynomial B to obtain points (H _ a2, V _ a2), where H _ a1 is H _ a 2. Finally, the parties cooperatively generate random numbers as an abscissa H _ A3 and an ordinate V _ A3 of a point on the polynomial C, resulting in points (H _ A3, V _ A3). H _ a1 ═ H _ a2 ═ H _ A3, and V _ C1 ═ V _ a1 ═ V _ B1.
Step S42, constructing the target polynomial according to all points of the target polynomial; in the case where the input value is a first coordinate value of the point, the output value of the target polynomial is a second coordinate value of the point; the generated plurality of target polynomials constitutes the set of target polynomials.
In particular, for any target polynomial, a linear fit may be performed based on all points generated for the target polynomial to construct a target polynomial. Or, lagrangian interpolation calculation can be performed according to the points generated by using to construct a target polynomial, so as to obtain a target polynomial coefficient. The above processing procedure is implemented in a ciphertext form, or may be implemented in a plaintext form. Accordingly, the coefficients of the target polynomial in the plaintext or ciphertext state can be obtained.
Finally, all of the generated target polynomials may constitute a set of target polynomials. By way of example, assume that the generated target polynomial includes: a (x), B (x), C (x), wherein 0 ═ A (x) B (x)C (x). Then A (x), B (x), and C (x) may comprise a set of target polynomials. Assuming that the generated target polynomial includes: f1(x) x, f2(x) x-1，…,fp(x)＝x-qThen: f1(x) x, f2(x) x-1，…,fp(x)＝x-qA set of target polynomials may be composed. Assuming that the generated target polynomial includes: f1(x) x, f2(x) e-ix，…，fp(x)＝e-ixqThen f1(x) is x, f2(x) is e-ix，…，fp(x)＝e-ixqA set of target polynomials may be composed.
In the embodiment of the invention, according to the target relationship, the points of the target polynomial are generated, wherein the points comprise a first coordinate value and a second coordinate value, the second coordinate values of the points of different target polynomials conform to the target relationship, and the first coordinate values of the points of different target polynomials conform to the preset specified relationship. And constructing the target polynomial according to all the points of the target polynomial. When the input value is the first coordinate value of the point, the output value of the target polynomial is the second coordinate value of the point; the generated plurality of target polynomials constitutes a set of target polynomials. In this way, the target polynomial can be constructed based on the points generated by generating the points of the target polynomial which accord with the target relationship, and the generation efficiency of the target polynomial can be ensured to a certain extent, so that the overall generation efficiency of the preprocessed data is improved.
Optionally, in the case that the data type of the preprocessed data is a triple with verification, the embodiment of the present invention may further include the following steps:
and step S51, updating the generated target polynomial set according to the held verification key factor and the supplementary variable based on the multi-party calculation protocol to obtain the target polynomial corresponding to the triple with verification.
In specific implementation, after a target polynomial is generated according to a target relationship that needs to be satisfied by the preprocessed data to be generated, the data type of the preprocessed data is further detected: and the triad type is whether the triad type is verified or not. If yes, the currently generated target polynomial can be further updated to generate a target polynomial corresponding to the verification triplet.
Further, each computing node may hold a verification key factor and a supplementary variable δ that are generated in advance to constitute the verification key α. The supplementary variable δ may be pre-stored in a clear text, and the verification key α may be pre-stored in a secret sharing manner. The specific computing nodes can generate the verification key alpha according to the held verification key factor based on the multi-party computing protocol. For example, the sum of the authentication key factors is computed by multiparty security as the authentication key α. And then, combining the verification key alpha and the supplementary variable delta with the current target polynomial according to a preset form to obtain a target polynomial corresponding to the triple with verification, wherein the target polynomial carries the verification key and the supplementary variable. For example, assume that the target polynomial is: a (x), b (x), c (x), 0 ═ a (x) b (x) -c (x). Then the target polynomial corresponding to the triple with verification may be: mac (a) ═ α (a) (x) + δ, mac (b) ═ α (b) (x) + δ, and mac (c) ═ α (b (x) + δ). Accordingly, for the preprocessed data a, b, c output by the target polynomial of the triplet type, the preprocessed data output by the target polynomial of the triplet type with verification may be γ (x) ═ α (x + δ), where x may be a, b, c. For example, Mac (a) _ v, Mac (b) _ v, Mac (c) _ v, Mac (a) _ v ═ α (a + δ); mac (b) _ v ═ α (b + δ); mac (c) _ v ═ α (c + δ). By generating the preprocessed data with verification in the off-line stage, the correctness of the data a, b and c in the calculation process can be ensured based on the carried verification information in the subsequent on-line stage.
In the embodiment of the invention, the generated target polynomial set is updated according to the held verification key factor and the supplementary variable based on the multi-party computing protocol to obtain the target polynomial corresponding to the triple with verification, so that the triple-type preprocessing data with verification can be finally generated. The diversity of the generated triplet type preprocessed data can be improved compared to the manner in which only triplet type preprocessed data are generated.
Optionally, in an implementation manner, the local input values of the plurality of computing nodes may satisfy the above-mentioned specified relationship, so as to ensure that output values of each final target polynomial output based on the local input values meet a target relationship, and further ensure accuracy of the finally generated preprocessed data. The local input value of each computing node can be pre-synchronized to the computing node, or further generated by each computing node according to pre-synchronized information. For example, where the specified relationships are numerically equal, the local input value may be a pseudo-random number (rx) generated based on a random seed (RND seed). The random seeds owned by the plurality of computing nodes are the same, and accordingly, the generated pseudo random numbers are also the same. Specifically, each calculation may read a preset random seed and then generate a pseudo-random number based on the random seed as a local input value. In this way, the local input values used by the various compute nodes are the same, i.e., conform to the specified relationship. Therefore, the final output values of all target polynomials output based on the local input values can be in accordance with the target relationship, and the accuracy of the finally generated preprocessed data can be further ensured. Meanwhile, compared with a mode of setting a fixed value as a local input value, in the formula embodiment, a pseudo random number generated based on a random seed is used as the local input value, and the randomness of the local input value used each time can be improved under the condition that the local input values used by each computing node are the same, so that the data security is improved.
Wherein the output value of the target polynomial output based on the local input value may be the sum of the output values of the sub-polynomials of the target polynomial output based on the local input value. The random seed held by each computing node may be pre-shared by the parties or generated by one party and then transmitted to each other in a designated manner. For example, they are transmitted to each other by means of a transport system of the secure transport layer protocol (TLS).
Further, the random seed in the embodiment of the present invention may be held only by the computing node, so that, when the target polynomial is constructed by the trusted device, the trusted device is prevented from acquiring the preprocessed data, and thus, data security is ensured.
Optionally, the data type of the preprocessed data may be obtained by parsing a type configuration file. In specific implementation, a preset type configuration file can be read first, and then the type configuration file is analyzed to determine the data type indicated by the type configuration file, so as to obtain the data type of the preprocessed data. The specific file type of the type configuration file and the data type indicated by the type configuration file can be preset by a user according to actual requirements, so that the flexibility of user setting can be improved, and preprocessing data of various data types can be generated according to requirements. For example, when the preprocessed data of the triplet type needs to be generated, the data type indicated by the type profile may be set as the triplet type, and when the preprocessed data of the natural logarithm type needs to be generated, the data type indicated by the type profile may be set as the natural logarithm type. In the embodiment of the invention, the data type of the preprocessed data can be conveniently determined by analyzing the type configuration file, so that the overall processing efficiency can be improved to a certain extent.
Optionally, the operation of constructing a sub-polynomial corresponding to the target polynomial according to the secret sharing factor of the target polynomial coefficient may specifically include:
and step S61, determining the distribution form of the secret sharing factor of the target polynomial coefficient.
Step S62, based on the distribution form, adding the secret sharing factor of the target polynomial coefficient to a corresponding position to obtain a sub-polynomial corresponding to the target polynomial.
The distribution form may represent a position of a target polynomial coefficient corresponding to the secret sharing factor in the target polynomial. Further, according to the distribution form, the secret sharing factors of each target polynomial coefficient are sequentially added to corresponding positions in the original polynomial, and then the sub-polynomial corresponding to the target polynomial is obtained. The corresponding position of the secret sharing factor may be a position of a target polynomial coefficient corresponding to the secret sharing factor in the target polynomial. The original polynomial may be pre-synchronized to each computing node, and the original polynomial may be a polynomial including only arguments, wherein the form and distribution of the arguments are the same as those of the target polynomial.
For example, assume that the multi-party secure computing system includes compute node 1, compute node 2, …, and compute node n. Target polynomial a (x) ap xp+……+a2*x2+a1*x1. The target polynomial coefficients of the target polynomial a (x) may then include a1, a2, …, ap. The secret sharing factors of the target polynomial coefficient a1 acquired by the computing node 1, the computing nodes 2, … and the computing node n are a11, a12, … and a1 n. The secret sharing factors of the target polynomial coefficient a2 acquired by the computing node 1, the computing nodes 2, … and the computing node n are a21, a22, … and a2 n. Secret sharing factors of the target polynomial coefficient ap respectively acquired by the computing node 1, the computing nodes 2, … and the computing node n are ap1, ap2, … and apn. Accordingly, for the computation node 1, a sub-polynomial corresponding to the target polynomial a (x) may be constructed as: a1(x) ═ ap1 xp+……+a21*x2+a11*x1. For the computation node 2, a sub-polynomial corresponding to the target polynomial a (x) may be constructed as: a2(x) ═ ap2 xp+……+a22*x2+a12*x1…, for the computation node n, the corresponding sub-polynomial of the target polynomial a (x) can be constructed as: an (x) apn xp+……+a2n*x2+a1n*x1。
Further, the number of the respective target polynomial corresponding sub-polynomials may be the same as the number of the computation nodes. For example, for a (x), b (x), c (x), and a corresponding sub-polynomial of the target polynomial set, a (x) may include: a1(x), a2(x) …, an (x); the target polynomial b (x) corresponding sub-polynomials may include: b1(x), B2(x) …, bn (x); the target polynomial c (x) the corresponding sub-polynomial may include: c1(x), C2(x) …, cp (x). For f1(x), f2(x) …, fp (x) in the above set of target polynomials, the target polynomial f1(x) corresponding sub-polynomials may comprise: f11(x), f12(x), …, f1n (x); the target polynomial f2(x) corresponding sub-polynomial may include: f21(x), f22(x), …, f2n (x); further, the target polynomial fp (x) corresponding sub-polynomial may include: fp1(x), fp2(x), …, fpn (x).
In the embodiment of the invention, the distribution form of the secret sharing factor of the target polynomial coefficient is determined, and the secret sharing factor of the target polynomial coefficient is added to the corresponding position based on the distribution form to obtain the sub-polynomial corresponding to the target polynomial. Therefore, the finally constructed sub-polynomials and the target polynomial have the same distribution form to a certain extent, and the target polynomial can be formed when the sub-polynomials are held by the plurality of computing nodes, so that the plurality of computing nodes can obtain the secret sharing factor of the preprocessed data based on the output values of the sub-polynomials, and the preprocessed data can be effectively represented.
Optionally, in an implementation, the operation of determining the distribution form of the secret sharing factor of the target polynomial coefficient may include:
step S71, determining a distribution form of the secret sharing factors of the target polynomial coefficients according to the order of obtaining the secret sharing factors of the target polynomial coefficients.
In the embodiment of the present invention, when obtaining the secret sharing factor of each target polynomial coefficient, the computing node may sequentially obtain the secret sharing factors according to the order of the target polynomial coefficients in the target polynomial. Correspondingly, the corresponding positions of the secret sharing factors can be determined according to the sequence of obtaining the secret sharing factors of the target polynomial coefficients, and then the distribution form is obtained. For example, it is assumed that the order of the secret sharing factors of the target polynomial coefficients a1, a2, … ap received by the computing node 1 is: a11, a21, … ap 1. That is, a11 is received first, then a21 is received, and finally ap1 is received. That is, in secret sharing, the secret sharing factor a11 of the target polynomial coefficient a1, the secret sharing factors a21 and … of the target polynomial coefficient a2, and the secret sharing factor ap1 of the target polynomial coefficient ap may be transmitted. The corresponding positions may be determined for the secret sharing factors in the order in which they were received, i.e., the order in which they were transmitted. For example, the corresponding location determined for the first received a11 may be the location of a1, and the corresponding location determined for the last received ap1 may be the location of an ap.
Of course, in a specific implementation, the distribution form may also be determined in other manners, for example, when the secret sharing factor of the target polynomial coefficient is obtained, further obtaining location indication information for indicating a location corresponding to the secret sharing factor of the target polynomial coefficient, then determining, according to the location indication information, a location corresponding to the target polynomial coefficient corresponding to each secret sharing factor in the target polynomial, and further determining the distribution form, which is not limited in the embodiment of the present invention.
In the embodiment of the invention, the distribution form of the secret sharing factors of the target polynomial coefficients is determined immediately according to the sequence of obtaining the secret sharing factors of the target polynomial coefficients, so that the determination efficiency can be ensured to a certain extent.
Further, assuming that the finally generated triplet type preprocessed data is a, b, c, a, b, c may be held by each computing node in a secret sharing manner. For example, the computing node 1 may hold a secret sharing factor of the preprocessed data: a1, b1, c1, compute node 2 may hold a secret sharing factor of preprocessed data: a2, b2, c2, …, compute node n may hold a secret sharing factor of preprocessed data: an, bn, cn. Accordingly, the secret sharing factor of the preprocessed data held by each computing node may satisfy the relationship: a-a 1+ a2+ … + an, b-b 1+ b2+ … + bn, c-c 1+ c2+ … + cn, and a-b-c. Accordingly, the computing nodes in the multi-party secure computing system can collaboratively complete online computing based on the secret sharing factor of the pre-processed data which are respectively held.
Further, fig. 2 is a schematic processing procedure diagram provided in the embodiment of the present invention, and as shown in fig. 2, a target polynomial may be first: respectively generating points of a target polynomial by using the polynomial A, the polynomial B and the polynomial C, and then carrying out Lagrangian difference evaluation on the points to construct target polynomials A (x), B (x) and C (x). And then, carrying out secret sharing on each target polynomial coefficient so as to share the secret sharing factor of the target polynomial coefficient to each computing node.
Fig. 3 is another schematic processing procedure diagram provided in an embodiment of the present invention, and as shown in fig. 3, each computing node may generate a pseudo random number based on a random seed, and input the pseudo random number into a sub polynomial corresponding to each target polynomial, so as to obtain each preprocessed data: a, b, c secret sharing factor. For example, the computing node 1 may obtain the secret sharing factor a1 of the preprocessed data a, the secret sharing factor b1 of the preprocessed data b, and the secret sharing factors c1 and … of the preprocessed data c, and the computing node n may obtain the secret sharing factor an of the preprocessed data a, the secret sharing factor bn of the preprocessed data b, and the secret sharing factor cn of the preprocessed data c.
In the embodiment of the invention, only a target polynomial set is needed to be constructed, a sub-polynomial is constructed by secret sharing of the target polynomial coefficient and a secret sharing factor of the target polynomial coefficient based on secret sharing, and the generation of the preprocessed data can be realized based on the sub-polynomial. Because the process of generating the preprocessed data only involves the implementation of simpler operation and does not involve the operation with high complexity, the processing performance can be optimized, and the preprocessed data meeting the requirements under each data type can be simply, conveniently and efficiently generated in a large amount while the safety is ensured.
Device embodiment
Referring to fig. 4, a block diagram of an embodiment of a data processing apparatus according to the present invention is shown, which is applied to a computing node in a multi-party secure computing system, where the multi-party secure computing system includes a plurality of computing nodes configured to cooperate to perform a multi-party secure computing task, and the apparatus includes:
an obtaining module 201, configured to obtain a secret sharing factor of a target polynomial coefficient in a target polynomial set, where the target polynomial set is generated according to a target relationship that needs to be satisfied by preprocessed data to be generated, the target polynomial in the target polynomial set satisfies the target relationship, and the target relationship is a preset relationship corresponding to a data type of the preprocessed data;
a constructing module 202, configured to construct a sub-polynomial corresponding to the target polynomial according to the secret sharing factor of the target polynomial coefficient;
and the calculating module 203 is configured to calculate an output value of the sub-polynomial according to the sub-polynomial and the local input value, so as to obtain a secret sharing factor of the preprocessed data.
The optional obtaining module 201 is specifically configured to:
acquiring a secret sharing factor of a target polynomial coefficient in a target polynomial set generated and shared secretly by the trusted equipment from the trusted equipment; or the like, or, alternatively,
and negotiating with other computing nodes in a plurality of computing nodes which cooperate to complete a multi-party safe computing task to generate a secret sharing factor of the target polynomial coefficient in the target polynomial set.
Optionally, the obtaining module 201 is further specifically configured to:
generating points of a target polynomial according to the target relation; each point comprises a first coordinate value and a second coordinate value, the second coordinate values of the points of different target polynomials conform to the target relationship, and the first coordinate values of the points of different target polynomials conform to a preset specified relationship;
constructing the target polynomial according to all points of the target polynomial; in the case where the input value is a first coordinate value of the point, the output value of the target polynomial is a second coordinate value of the point;
the generated plurality of target polynomials constitutes the set of target polynomials.
Optionally, the designated relationships are the same in value; the local input value is a pseudo random number generated based on a random seed, wherein the random seeds owned by the plurality of compute nodes are the same, and the generated pseudo random numbers are the same.
Optionally, in a case that the data type of the preprocessed data is a triplet with verification, the apparatus further includes:
and the updating module is used for updating the generated target polynomial set according to the held verification key factor and the supplementary variable based on the multi-party computing protocol to obtain the target polynomial corresponding to the triple with verification.
Optionally, the data type of the preprocessed data is obtained by parsing a type configuration file.
Optionally, the building module 202 is specifically configured to:
determining a distribution form of a secret sharing factor of the target polynomial coefficient;
and based on the distribution form, adding the secret sharing factor of the target polynomial coefficient to a corresponding position to obtain a sub-polynomial corresponding to the target polynomial.
Optionally, the building module 202 is further specifically configured to:
and determining the distribution form of the secret sharing factors of the target polynomial coefficients according to the sequence of obtaining the secret sharing factors of the target polynomial coefficients.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
An embodiment of the present invention provides an apparatus for data processing, applied to a computing node in a multi-party secure computing system, where the multi-party secure computing system includes multiple computing nodes, the multiple computing nodes are used for collaboratively performing a multi-party secure computing task, and the multi-party secure computing system includes a memory and one or more programs, where the one or more programs are stored in the memory, and configured to be executed by one or more processors, and the one or more programs include instructions for: acquiring a secret sharing factor of a target polynomial coefficient in a target polynomial set, wherein the target polynomial set is generated according to a target relationship required to be met by preprocessed data to be generated, the target relationship is met among target polynomials in the target polynomial set, and the target relationship is a preset relationship corresponding to the data type of the preprocessed data; constructing a sub polynomial corresponding to the target polynomial according to the secret sharing factor of the target polynomial coefficient; and calculating the output value of the sub polynomial according to the sub polynomial and the local input value to obtain the secret sharing factor of the preprocessed data.
Fig. 5 is a block diagram illustrating an apparatus 800 for data processing in accordance with an example embodiment. For example, the apparatus 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 5, the apparatus 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.
The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 808 includes a screen that provides an output interface between the device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 800 is in an operational mode, such as a call mode, a recording mode, and a voice information processing mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed status of the device 800, the relative positioning of components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in the position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration/deceleration of the device 800, and a change in the temperature of the device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communications between the apparatus 800 and other devices in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on radio frequency information processing (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 804 comprising instructions, executable by the processor 820 of the device 800 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Fig. 6 is a schematic diagram of a server in some embodiments of the invention. The server 1900 may vary widely by configuration or performance and may include one or more Central Processing Units (CPUs) 1922 (e.g., one or more processors) and memory 1932, one or more storage media 1930 (e.g., one or more mass storage devices) storing applications 1942 or data 1944. Memory 1932 and storage medium 1930 can be, among other things, transient or persistent storage. The program stored in the storage medium 1930 may include one or more modules (not shown), each of which may include a series of instructions operating on a server. Still further, a central processor 1922 may be provided in communication with the storage medium 1930 to execute a series of instruction operations in the storage medium 1930 on the server 1900.
The server 1900 may also include one or more power supplies 1926, one or more wired or wireless network interfaces 1950, one or more input-output interfaces 1958, one or more keyboards 1956, and/or one or more operating systems 1941, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.
A non-transitory computer-readable storage medium in which instructions, when executed by a processor of an apparatus (server or terminal), enable the apparatus to perform the data processing method shown in fig. 1.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
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.
The data processing method, the data processing apparatus and the apparatus for data processing provided by the present invention are described in detail above, and specific examples are applied herein to illustrate the principles and embodiments of the present invention, and the description of the above embodiments is only used to help understand the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (16)
1. A data processing method applied to a computing node in a multi-party secure computing system, the multi-party secure computing system comprising a plurality of computing nodes, the plurality of computing nodes being configured to cooperate to perform a multi-party secure computing task, the method comprising:
acquiring a secret sharing factor of a target polynomial coefficient in a target polynomial set, wherein the target polynomial set is generated according to a target relationship required to be met by preprocessed data to be generated, the target relationship is met among target polynomials in the target polynomial set, and the target relationship is a preset relationship corresponding to the data type of the preprocessed data;
determining a distribution form of a secret sharing factor of the target polynomial coefficient;
based on the distribution form, adding the secret sharing factor of the target polynomial coefficient to a corresponding position to obtain a sub-polynomial corresponding to the target polynomial; the corresponding position of the secret sharing factor is the position of a target polynomial coefficient corresponding to the secret sharing factor in the target polynomial;
and calculating the output value of the sub polynomial according to the sub polynomial and the local input value to obtain the secret sharing factor of the preprocessed data.
2. The method of claim 1, wherein obtaining the secret sharing factor of the target polynomial coefficient in the set of target polynomials comprises:
acquiring a secret sharing factor of a target polynomial coefficient in a target polynomial set generated and shared secretly by the trusted equipment from the trusted equipment; or the like, or, alternatively,
and negotiating with other computing nodes in a plurality of computing nodes which cooperate to complete a multi-party safe computing task to generate a secret sharing factor of the target polynomial coefficient in the target polynomial set.
3. The method of claim 1, wherein generating the set of target polynomials according to a target relationship that needs to be satisfied by the pre-processed data to be generated comprises:
generating points of a target polynomial according to the target relation; each point comprises a first coordinate value and a second coordinate value, the second coordinate values of the points of different target polynomials conform to the target relationship, and the first coordinate values of the points of different target polynomials conform to a preset specified relationship;
constructing the target polynomial according to all points of the target polynomial; in the case where the input value is a first coordinate value of the point, the output value of the target polynomial is a second coordinate value of the point;
the generated plurality of target polynomials constitutes the set of target polynomials.
4. The method of claim 3, wherein the specified relationships are numerically the same; the local input value is a pseudo random number generated based on a random seed, wherein the random seeds owned by the plurality of compute nodes are the same, and the generated pseudo random numbers are the same.
5. The method of claim 1, wherein in the case that the data type of the preprocessed data is a triplet with validation, the method further comprises:
and updating the generated target polynomial set according to the held verification key factor and the supplementary variable based on the multi-party computing protocol to obtain the target polynomial corresponding to the triple with verification.
6. The method according to any one of claims 1 to 5, wherein the data type of the preprocessed data is obtained by parsing a type profile.
7. The method according to any one of claims 1 to 5, wherein the determining the distribution form of the secret sharing factor of the target polynomial coefficient comprises:
and determining the distribution form of the secret sharing factors of the target polynomial coefficients according to the sequence of obtaining the secret sharing factors of the target polynomial coefficients.
8. A data processing apparatus for use in a computing node in a multi-party secure computing system, the multi-party secure computing system including a plurality of computing nodes configured to cooperate to perform a multi-party secure computing task, the apparatus comprising:
the secret sharing method comprises the following steps that an obtaining module is used for obtaining a secret sharing factor of a target polynomial coefficient in a target polynomial set, wherein the target polynomial set is generated according to a target relation required to be met by preprocessed data to be generated, the target relation is met among target polynomials in the target polynomial set, and the target relation is a preset relation corresponding to the data type of the preprocessed data;
the construction module is used for determining the distribution form of the secret sharing factor of the target polynomial coefficient; based on the distribution form, adding the secret sharing factor of the target polynomial coefficient to a corresponding position to obtain a sub-polynomial corresponding to the target polynomial; the corresponding position of the secret sharing factor is the position of a target polynomial coefficient corresponding to the secret sharing factor in the target polynomial;
and the calculating module is used for calculating the output value of the sub-polynomial according to the sub-polynomial and the local input value to obtain the secret sharing factor of the preprocessed data.
9. The apparatus of claim 8, wherein the obtaining module is specifically configured to:
acquiring a secret sharing factor of a target polynomial coefficient in a target polynomial set generated and shared secretly by the trusted equipment from the trusted equipment; or the like, or, alternatively,
and negotiating with other computing nodes in a plurality of computing nodes which cooperate to complete a multi-party safe computing task to generate a secret sharing factor of the target polynomial coefficient in the target polynomial set.
10. The apparatus of claim 8, wherein the obtaining module is further specifically configured to:
generating points of a target polynomial according to the target relation; each point comprises a first coordinate value and a second coordinate value, the second coordinate values of the points of different target polynomials conform to the target relationship, and the first coordinate values of the points of different target polynomials conform to a preset specified relationship;
constructing the target polynomial according to all points of the target polynomial; in the case where the input value is a first coordinate value of the point, the output value of the target polynomial is a second coordinate value of the point;
the generated plurality of target polynomials constitutes the set of target polynomials.
11. The apparatus of claim 10, wherein the specified relationships are numerically the same; the local input value is a pseudo random number generated based on a random seed, wherein the random seeds owned by the plurality of compute nodes are the same, and the generated pseudo random numbers are the same.
12. The apparatus of claim 8, wherein in the case that the data type of the preprocessed data is a triplet with verification, the apparatus further comprises:
and the updating module is used for updating the generated target polynomial set according to the held verification key factor and the supplementary variable based on the multi-party computing protocol to obtain the target polynomial corresponding to the triple with verification.
13. The apparatus of any of claims 8 to 12, wherein the data type of the preprocessed data is obtained by parsing a type profile.
14. The apparatus according to any one of claims 8 to 12, wherein the building module is further configured to:
and determining the distribution form of the secret sharing factors of the target polynomial coefficients according to the sequence of obtaining the secret sharing factors of the target polynomial coefficients.
15. An apparatus for data processing, the apparatus being applied to a computing node in a multi-party secure computing system, the multi-party secure computing system comprising a plurality of computing nodes configured to cooperate to perform a multi-party secure computing task, the apparatus comprising a memory, and one or more processors, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors to execute instructions included in the one or more programs for:
acquiring a secret sharing factor of a target polynomial coefficient in a target polynomial set, wherein the target polynomial set is generated according to a target relationship required to be met by preprocessed data to be generated, the target relationship is met among target polynomials in the target polynomial set, and the target relationship is a preset relationship corresponding to the data type of the preprocessed data;
determining a distribution form of a secret sharing factor of the target polynomial coefficient;
based on the distribution form, adding the secret sharing factor of the target polynomial coefficient to a corresponding position to obtain a sub-polynomial corresponding to the target polynomial; the corresponding position of the secret sharing factor is the position of a target polynomial coefficient corresponding to the secret sharing factor in the target polynomial;
and calculating the output value of the sub polynomial according to the sub polynomial and the local input value to obtain the secret sharing factor of the preprocessed data.
16. A machine-readable medium having stored thereon instructions, which when executed by one or more processors, cause an apparatus to perform the data processing method of any of claims 1 to 7.
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