A data security intelligent verification method and system based on batch privacy intersection

Abstract

The present application relates to a kind of data security intelligent verification method, system, electronic equipment and program product based on batch privacy intersection, the method features in it include, the whole amount of data to be compared by participant is uploaded to local;According to sequence, the data field and the verification mode of intersection are selected;The selected data field is spliced, and the spliced field is committed, the commitment value of the spliced field is formed, and the commitment value is specified as new ID column;The new ID column is calculated as the input value of batch privacy intersection calculation;The intersection result is converted into data intelligent verification result, the consistency verification and error check of multiple arbitrary data field are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer technology, and in particular to a data security intelligent verification method and system based on batch privacy intersection. BACKGROUND

[0002] In many scenarios, there is a need for data intelligent verification, that is, to determine whether two sets of data are consistent, whether the difference meets the floating range, etc. The existing technology directly compares data based on plaintext. If the data belongs to two parties, in order to protect the privacy and security of the data of both parties, the hashed data is compared.

[0003] Current privacy computing technology only supports intersection operations on specified ID columns, and cannot perform intersection and matching operations on the entire data, and the intersection range is limited. Data intelligent verification currently only supports two ways of direct comparison of plaintext and comparison after hashing. The direct comparison of plaintext cannot guarantee the security of the data when the data belongs to different institutions. The comparison after hashing can only support the verification of whether the data is consistent, and cannot perform more detailed error verification. Moreover, the hash-based method needs to send the full amount of hashed data to the other party, and there is a risk of leakage of difference set data. SUMMARY

[0004] To solve the problems of the prior art, the present application provides a data security intelligent verification method, device and equipment based on batch privacy intersection, which supports batch and arbitrary field intersection functions based on privacy intersection technology. The fields of data sets are often diversified and complex, and supporting intersection of arbitrary fields means that users can select the fields to be compared according to specific needs, thereby more accurately locating the intersection data. The batch privacy intersection method realizes various and secure data intelligent verification methods by introducing a range proof algorithm, and supports consistency verification and error verification of data without leaving the domain. The range proof algorithm can verify the range of data to ensure the accuracy and consistency of the data. By combining the privacy intersection technology, there is no need to transfer data to other domains or platforms, which helps to protect the privacy and security of the data and avoid the risk of data leakage and misuse. To achieve the above purposes, the technical solution adopted by the present application includes:

[0005] A multi-party real-time reconciliation method implemented based on a message queue, characterized in that it comprises:

[0006] S1. uploading the full amount of data to be compared by the participating parties to the local;

[0007] S2. selecting data fields and verification methods for intersection in sequence;

[0008] S3. Splicing the selected data field, and committing the spliced field to form a commitment value of the spliced field, and assigning the commitment value as a new ID column;

[0009] S4. Calculating the new ID column as an input value of batch privacy intersection calculation;

[0010] S5. Converting the intersection result into a data intelligent verification result.

[0011] Specifically, the present application is a new type of privacy data comparison technical solution. Specifically, the comparison data of the participants is uploaded to the set local database to complete the data comparison with other participants. By selecting specific comparison data fields, multiple arbitrary fields are spliced into a new commitment value according to the commitment algorithm. The local data of other participants is the same. The participants obtain their own new ID column. The new ID column is processed by the traditional privacy data intersection method to obtain the intersection and difference set of the intersection result.

[0012] If the selected verification method is consistency verification, the intersection of the intersection result is the data field that passes the verification, and the difference set is the data field that fails to pass the verification. If the selected verification method is error verification, the data set from the intersection result is distinguished according to the set acceptance range. The intersection is directly used as the data field that passes the verification, and the difference set data needs to be calculated to obtain the difference value data. If the difference value data meets the set acceptance error range, it belongs to the difference set data field that meets the verification requirement. The obtained data field is fed back to the display of the intelligent verification result. Otherwise, the difference value data does not meet the set acceptance error range, and the corresponding difference set data field will be identified as not meeting the verification requirement.

[0013] In an embodiment of the present application, the verification method selected in S2 for intersection includes consistency verification and error verification, wherein the error verification includes setting the difference value acceptance range of the intersection result data of the participants.

[0014] In an embodiment of the present application, S5 converts the intersection result into a data intelligent verification result, including judging whether the intersection result meets the verification requirement of the verification method, and the verification requirement includes meeting the consistency requirement and the error verification requirement.

[0015] Specifically, the consistency requirement is that the obtained commitment value is batched as a new ID column for privacy intersection, and the intersection part of the intersection result directly determines that the corresponding data field is consistent, and the difference set part does not meet the consistency requirement. If error checking is performed, the obtained commitment value is subjected to subtraction operation to obtain the difference value data of the corresponding data, and the difference value data and the set acceptable error range are compared. If it is within the acceptable error range, the data field corresponding to the error range is determined to meet the error checking verification requirement, otherwise, it does not meet the requirement.

[0016] In an embodiment of the present application, the consistency requirement comprises: performing the batched privacy intersection, obtaining the intersection set of the intersection result as data satisfying the consistency requirement, and the difference set of the intersection result as data not satisfying the consistency requirement.

[0017] In an embodiment of the present application, the error checking requirement comprises: performing setting of the difference value acceptable range of the intersection result data of the participant, and range proof checking of the number set of the intersection result. If the range proof can be generated, it is determined that the intersection result data satisfies the error checking requirement, otherwise, it is considered that the number set of the intersection result does not satisfy the error checking requirement.

[0018] Specifically, in the error checking mode, the difference value acceptable range in the error checking mode is set to be adjustable, providing flexible error checking range for data verification of different accuracy requirements. And the intersection result in the error checking mode is the number set of the commitment value subtraction.

[0019] In an embodiment of the present application, the S3 further comprises that the splicing of the selected data field can be replaced by a commitment scheme with homomorphism.

[0020] Specifically, the calculation of the commitment value includes multiple optional ways, including additive homomorphic commitment: it is a commitment scheme that allows addition operation on the commitment value. Specifically, if there are two commitments C1 and C2, which are commitments of x1 and x2 respectively, there is a valid commitment C3, which is a commitment of x1+x2. This property makes additive homomorphic commitment particularly useful in privacy-preserving computation, as it allows participants to perform addition operations on commitment values without exposing the actual numerical values. Multiplicative homomorphic commitment: similar to additive homomorphic commitment, multiplicative homomorphic commitment allows multiplication operation on the commitment value. That is, if there are two commitments C1 and C2, which are commitments of x1 and x2 respectively, there is a valid commitment C3, which is a commitment of x1*x2. Full homomorphic commitment: full homomorphic commitment is a more powerful commitment scheme that allows arbitrary number of addition and multiplication operations on the commitment value, and the result is still a valid commitment. This means that, as long as there is a commitment to a certain value, a commitment to other complex expressions or functions can be obtained through a series of calculations without exposing the original data. Full homomorphic commitment has broad application prospects in building complex privacy-preserving computation protocols.

[0021] In an embodiment of the present application, the S2 selects the data fields and the verification method in sequence for intersection, and performs semantic consistency check on the selected data fields to ensure corresponding processing in the case of inconsistent field names.

[0022] Specifically, since the field names of the participants A and B may be different but have the same meaning, in order to ensure consistent understanding of the same meaning field by the two participants and avoid matching errors, semantic consistency check needs to be performed on the data field selected by the participants.

[0023] The present application also provides a data security intelligent verification system based on batch privacy intersection, characterized in that it comprises,

[0024] A data processing module for uploading full data to be compared by participants to the local and selecting data fields and verification methods in sequence for intersection;

[0025] A homomorphic commitment value calculation module for splicing the selected data fields and committing the spliced fields to form a commitment value of the spliced fields, and designating the commitment value as a new ID column;

[0026] A privacy intersection module for calculating the new ID column as input numerical value for batch privacy intersection calculation;

[0027] A result conversion module for converting the intersection result into a data intelligent verification result.

[0028] The application also provides an electronic device, characterized by comprising a processor and a memory.

[0029] The memory is configured to store a computer program.

[0030] The processor is configured to execute the method according to any one of claims 1 to 7 by calling the computer program.

[0031] The application also relates to a computer program product comprising a computer program and / or instructions, characterized in that the computer program and / or instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

[0032] The application has the beneficial effects that: the batch and arbitrary field intersection function is supported on the basis of the privacy intersection technology; the fields of the data set are diversified and complex; the support of the arbitrary field intersection means that the user can select the fields to be compared according to the specific requirements, so as to more accurately locate the intersection data; the batch privacy intersection method realizes the intelligent verification mode of various and safe data by introducing the range proof algorithm; the consistency verification and error verification of the data are realized under the premise that the data does not go out of the domain; the range proof algorithm can verify the range of the data, and ensures the accuracy and consistency of the data; by combining the privacy intersection technology, the data does not need to be transferred to other domains or platforms, and this characteristic helps to protect the privacy and security of the data, and avoids the risk of data leakage and abuse. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 FIG. 1 is a flowchart of the data security intelligent verification method based on the batch privacy intersection of the application.

[0034] Figure 2 FIG. 2 is a structural diagram of the data security intelligent verification system based on the batch privacy intersection of the application.

[0035] Figure 3 FIG. 3 is an operation diagram of the data security intelligent verification method based on the batch privacy intersection of the application. DETAILED DESCRIPTION

[0036] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application but not all the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0037] It should be noted that similar reference numerals and letters refer to similar items throughout the accompanying drawings, and therefore, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", and the like are only used to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0038] The first aspect of the present application relates to a step flow of a data security intelligent verification method based on batch privacy intersection, as shown in the flow Figure 1 , including:

[0039] S1. uploading the full amount of data to be compared by the participating party to the local;

[0040] S2. selecting data fields and verification methods for intersection in sequence;

[0041] S3. splicing the selected data fields, and committing the spliced fields to form a commitment value of the spliced fields, and designating the commitment value as a new ID column;

[0042] S4. taking the new ID column as an input value for batch privacy intersection calculation;

[0043] S5. converting the intersection result into a data intelligent verification result.

[0044] In the specific embodiment, see Figure 3The shown operation flow diagram starts with participants uploading full data to be compared to a local database, then selecting data fields according to set rules, performing commitment algorithm calculation according to the selected data fields, obtaining commitment values with the same number of bits as new ID columns, and then taking the new ID values as input values for consistency checking or error checking according to the selected verification method; if consistency checking is selected, traditional privacy algorithm technology is used to obtain difference set and intersection set, and if error checking is performed, the commitment values are directly added and subtracted to obtain the number set of the calculation result, and the corresponding data fields that meet the error checking setting requirements are obtained by range proof algorithm.

[0045] If consistency checking is selected, the data of the difference set and the intersection set of the intersection result are directly converted into corresponding data field lists, and the data fields that meet the requirements and the data fields that do not meet the requirements are respectively displayed in the corresponding lists. If error checking is selected, the corresponding data fields of the number set that meet the error checking requirements are converted into corresponding lists for display, and the sizes of the errors are arranged in order in the corresponding table.

[0046] Another aspect of the present application also relates to a system for data security intelligent verification based on batch privacy intersection, which has the structure as shown in the figure. Figure 2 The system comprises:

[0047] A data processing module is configured to upload full data to be compared by participants to a local database and select data fields and verification methods in sequence for intersection;

[0048] In this embodiment, the data fields to be selected for intersection are specified in sequence, and the field names do not need to be the same (the words do not need to be consistent, but the meanings need to be consistent), for example, the name, mobile phone number and home address of participant A, and the name, contact method and contact address of participant B.

[0049] A homomorphic commitment value calculation module is configured to splice the selected data fields and perform commitment on the spliced fields to form commitment values of the spliced fields, and the commitment values are designated as new ID columns;

[0050] Specifically, the selected fields are spliced and committed, for example,

[0051] Commit(name-number-address) =

[0052] And the commitment value is designated as a new ID column.

[0053] A privacy intersection module is configured to calculate the new ID column as an input value for batch privacy intersection calculation;

[0054] Specifically, the optional privacy intersection algorithm has a scheme based on key exchange, as follows:

[0055] The participant Alice selects a random number as a private key. For each data x, Alice first performs a commitment value algorithm operation on it, then encrypts it based on the commitment value using the private key to generate ciphertext, and sends the ciphertext to the participant Bob.

[0056] Bob selects a random number as a private key. For each data y, Bob first performs a commitment value calculation operation on it, then encrypts it based on the commitment value using the private key, and sends the ciphertext to the participant Alice. For the ciphertext received by Alice, Bob uses the private key to encrypt it twice. Alice encrypts the received ciphertext based on the private key twice, compares the two ciphertexts, and if they are consistent, it means that the same data is owned.

[0057] The result conversion module is configured to convert the intersection result into a data intelligent verification result.

[0058] Specifically, if the selection is a consistency verification mode, the difference set and intersection set of the intersection result are directly converted into corresponding data field lists, and the data fields meeting the requirements and the data fields not meeting the requirements are respectively displayed in the corresponding lists. If the selection is an error verification, the corresponding data fields of the data set meeting the error verification requirement are converted into a corresponding list for display, and the sizes of the errors are arranged in order to the corresponding table.

[0059] By using the system, the above-mentioned operation processing method can be executed and the corresponding technical effects can be achieved.

[0060] Embodiments of the application also provide an electronic device for executing the above-mentioned method, as an implementation device of the method, the electronic device at least has a processor and a memory, in particular, the memory stores the data and related computer programs required for executing the method, and the processor calls the data and programs in the memory to execute all steps of the method and obtain the corresponding technical effects.

[0061] Preferably, the electronic device can include a bus architecture, the bus can include any number of interconnected buses and bridges, the bus will include various circuits linked together by one or more processors and memories. The bus can also link various other circuits such as peripheral devices, voltage regulators and power management circuits, which are well known in the art, and therefore, will not be further described herein. The bus interface provides an interface between the bus and the receiver and transmitter. The receiver and transmitter can be the same element, i.e. a transceiver, which provides a unit for communicating with various other systems over a transmission medium. The processor is responsible for managing the bus and general processing, while the memory can be used to store data used by the processor in performing operations.

[0062] Additionally, the electronic device can further include a communication module, an input unit, an audio processor, a display, a power supply, etc. The processor (or controller, operating control) employed therein can include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of various components of the electronic device; the memory can be one or more of a cache, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device, which stores the above-mentioned related data information, and in addition, can store programs for executing related information, and the processor can execute the programs stored in the memory to achieve information storage or processing, etc.; the input unit is used to provide input to the processor, which can be a key or a touch input device, for example; the power supply is used to provide power to the electronic device; the display is used to display display objects such as images and text, which can be an LCD display, for example. The communication module is a transmitter / receiver that transmits and receives signals via an antenna. The communication module (transmitter / receiver) is coupled to the processor to provide input signals and receive output signals, which can be the same as in the case of a conventional mobile communication terminal. Based on different communication technologies, multiple communication modules can be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, etc. The communication module (transmitter / receiver) is also coupled to the speaker and the microphone via the audio processor to provide audio output via the speaker and receive audio input from the microphone, thereby achieving the usual telecommunication functions. The audio processor can include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor is also coupled to the central processor, so that it can be able to record on the local machine through the microphone, and it can be able to play the sound stored on the local machine through the speaker.

[0063] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, apparatus such as a system, or computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0064] The present application is described in reference to the drawings, which are as follows. Figure 1 one or more processes and / or blocks Figure 1 a system with the function specified in the flow(s) and / or block(s) of the flow and / or block diagram.

[0065] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow Figure 1 one or more processes and / or blocks Figure 1 a system with the function specified in the flow(s) and / or block(s) of the flow and / or block diagram.

[0066] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow Figure 1 one or more processes and / or blocks Figure 1 a system with the function specified in the flow(s) and / or block(s) of the flow and / or block diagram.

[0067] The above merely provides the preferred but not limiting embodiments of the present application, and any modification or substitution within the technical scope disclosed by the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A data security intelligent verification method based on batch privacy intersection, characterized in that, include: S1. Upload all the data to be compared by the participants to the local machine; S2. Select the data fields and validation methods for intersection in sequence; S3. Concatenate the selected data fields, commit to the concatenated fields to form a commitment value for the concatenated fields, and designate the commitment value as a new ID column; S4. Use the new ID column as the input value for the batch privacy intersection calculation; S5. Convert the intersection result into a data intelligent verification result; The sequential selection of the intersection verification method in S2 includes consistency verification and error verification, wherein the error verification includes setting the acceptable range of the difference between the commitment values ​​of the participating parties; S5 converts the intersection result into a data intelligent verification result, including determining whether the intersection result meets the verification requirements of the verification method. The verification requirements include meeting consistency requirements and error verification requirements. The error verification requirement includes setting the acceptable range of the difference in the intersection result data of the participating parties. In the intersection result set, the intersection is directly used as the data field that passes the verification, while the difference data needs to be obtained by calculating the difference of the corresponding data field new ID column data. If the difference data meets the set acceptable error range, it belongs to the difference data field that meets the verification requirements; otherwise, it does not meet the error verification requirements.

2. The method as described in claim 1, characterized in that, The consistency requirement includes performing the batch privacy intersection, obtaining the intersection of the intersection results as data that meets the consistency requirement, and the difference of the intersection results as data that does not meet the consistency requirement.

3. The method as described in claim 1, characterized in that, S3 also includes the option to replace the selected data field with a homomorphic commitment scheme by concatenating the data fields.

4. The method as described in claim 1, characterized in that, S2 sequentially selects the data fields and validation methods for intersection, wherein the selected data fields are subjected to semantic consistency checks to ensure corresponding processing in the event of inconsistent field names.

5. A data security intelligent verification system based on batch privacy intersection, characterized in that, include, The data processing module is used to upload the full amount of data to be compared by the participants to the local machine, and select the data fields and verification methods for intersection in sequence. The verification methods include consistency verification and error verification. Error verification includes setting the acceptable range of difference of the commitment value of the participants. The homomorphism commitment value calculation module concatenates the selected data fields, makes a commitment to the concatenated fields, forms the commitment value of the concatenated fields, and assigns the commitment value as a new ID column; The privacy intersection module is used to perform calculations by taking the new ID column as the input value for batch privacy intersection calculations; The result conversion module is used to convert the intersection result into a data intelligent verification result; the result conversion module is also used to determine whether the intersection result meets the verification requirements of the verification method, the verification requirements include consistency requirements and error verification requirements; The error verification requirement includes setting the acceptable range of the difference in the intersection result data of the participating parties. In the intersection result set, the intersection is directly used as the data field that passes the verification, while the difference data needs to be obtained by calculating the difference of the corresponding data field new ID column data. If the difference data meets the set acceptable error range, it belongs to the difference data field that meets the verification requirements; otherwise, it does not meet the error verification requirements.

6. An electronic device, characterized in that, Including processor and memory; The memory is used to store computer programs; The processor is configured to execute the method of any one of claims 1 to 4 by invoking a stored computer program.

7. A computer program product, comprising a computer program and / or instructions, characterized in that, When the computer program and / or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 4.

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