Data security intelligent verification method and system based on batch privacy intersection

Abstract

The invention relates to a data security intelligent verification method and system based on batch privacy intersection, electronic equipment and a program product, and the method is characterized in that the method comprises the steps: uploading the total data to be compared by a participant to the local; selecting data fields and verification modes for intersection according to the sequence; the selected data fields are spliced, the spliced fields are committed, committed values of the spliced fields are formed, and the committed values are designated as new ID columns; performing calculation by taking the new ID column as an input numerical value of batch privacy intersection calculation; and an intersection result is converted into a data intelligent verification result, so that consistency verification and error verification of various data fields are realized.

Description

Technical Field

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

[0002] In many scenarios, there is a need for intelligent data verification, that is, to determine whether two pieces of data are consistent, whether the difference is within the floating range, etc. The existing technology is to directly compare data based on plain text. If the data is located on two parties, in order to protect the privacy and security of the data of both parties, the hashed data is used for comparison.

[0003] Current privacy computing technologies only support intersection operations on specified ID columns, and cannot perform intersection and matching operations on the entire data, and the intersection range is limited. Intelligent data verification currently only supports two methods: direct comparison of plain text and comparison after hashing. The direct comparison of plain text cannot guarantee the security of data when the data belongs to different institutions; the comparison after hashing can only support one verification method to check whether the data is consistent, and cannot perform more detailed verification with errors. In addition, the hash-based method requires sending the full amount of hashed data to the other party, and there is a risk of leakage of the difference set data. Summary of the invention

[0004] In order to address the deficiencies of the prior art, the present invention provides a data security intelligent verification method, device and equipment based on batch privacy intersection, which supports batch and arbitrary field intersection functions on the basis of privacy intersection technology. The fields of a data set are often diverse and complex. Supporting the intersection of arbitrary fields means that users can select the fields that need to be compared according to specific needs, thereby more accurately locating the intersection data. Based on the batch privacy intersection method, a variety of secure data intelligent verification methods are implemented by introducing a range proof algorithm, which supports consistency verification and error verification of data without leaving the domain. The range proof algorithm can verify the range of the data to ensure the accuracy and consistency of the data. By combining the privacy intersection technology, there is no need to transfer the data to other domains or platforms. This feature helps to protect the privacy and security of the data and avoid the risk of data leakage and abuse. To achieve the above purposes, the technical solutions adopted by the present invention include:

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

[0006] S1. Upload all the data to be compared by the participants to the local computer;

[0007] S2. Select the data fields and verification methods for intersection in order;

[0008] S3. Concatenate the selected data fields, and commit the concatenated fields to form a committed value of the concatenated fields, and designate the committed value as a new ID column;

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

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

[0011] Specifically, the present invention is a new technical solution for privacy data comparison. Specifically, the comparison data of the participating parties can be uploaded to the set local database to complete the data comparison with other participating parties. 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 participating parties are similar. The participating parties obtain their own new ID columns, and use traditional privacy data intersection methods to perform general intersection processing on the obtained new ID columns to obtain the intersection and difference of the intersection results.

[0012] If the selected verification method is consistency verification, the intersection of the intersection result is the data field that passed the verification, and the difference is the data field that failed the verification. If the selected verification method is error verification, the values ​​that meet the set acceptance range are distinguished from the data set of the intersection result, where the intersection is directly used as the data field that passed the verification, and the difference data needs to be obtained by calculating the difference of the new ID column data of the corresponding data field. If the difference data meets the set acceptance error range, it belongs to the difference data field that meets the verification requirements, and the obtained data field is fed back to the display of the intelligent verification results. On the contrary, if the difference data does not meet the set acceptance error range, the corresponding difference data field will be deemed to fail to meet the verification requirements.

[0013] In one embodiment of the present invention, the verification method for sequentially selecting the intersection in S2 includes consistency verification and error verification, wherein the error verification includes setting an acceptable range for the difference of the intersection result data of the participants.

[0014] In an embodiment of the present invention, the 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, and the verification requirements include meeting consistency requirements and error verification requirements.

[0015] Specifically, the consistency requirement is to use the obtained commitment value as the new ID column for batch privacy intersection. The intersection part of the intersection result directly determines that the corresponding data field is consistent, and the difference part does not meet the consistency requirement. If error checking is performed, the obtained commitment value is subtracted to obtain the difference data of the corresponding data, and the difference data is compared with the set acceptable error range. If it is within the acceptable error range, the corresponding data field within the error range is judged to meet the verification requirements of error checking, otherwise, it does not meet the verification requirements.

[0016] In an embodiment of the present invention, the consistency requirement includes executing the batch private intersection, obtaining an intersection of the intersection results as data that meets the consistency requirement, and a difference set of the intersection results as data that does not meet the consistency requirement.

[0017] In one embodiment of the present invention, the error checking requirement includes setting an acceptable range of differences of the intersection result data of the participating party, and performing a range proof check on the number set of the intersection result. If the range proof can be generated, it is determined that the data of the intersection result meets the error checking requirement. Otherwise, it is considered that the number set of the intersection result does not meet the error checking requirement.

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

[0019] In one embodiment of the present invention, the S3 also includes the selected data fields being spliced ​​and replaced with a commitment scheme having homomorphism.

[0020] Specifically, there are multiple options for computing commitment values, including additive homomorphic commitments: This is a commitment scheme that allows addition of commitment values. Specifically, if there are two commitments C1 and C2, which are commitments to x1 and x2, respectively, then there is a valid commitment C3, which is a commitment to x1+x2. This property makes additive homomorphic commitments particularly useful in privacy-preserving computations, as it allows participants to add commitment values ​​without exposing the actual values. Multiplicative homomorphic commitments: Similar to additive homomorphic commitments, multiplicative homomorphic commitments allow multiplication of commitment values. That is, if there are two commitments C1 and C2, which are commitments to x1 and x2, respectively, then there is a valid commitment C3, which is a commitment to x1*x2. Fully homomorphic commitments: Fully homomorphic commitments are a more powerful commitment scheme that allows any number of addition and multiplication operations on commitment values, and the result is still a valid commitment. This means that once 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. Fully homomorphic commitments have broad application prospects in building complex privacy-preserving computing protocols.

[0021] In one embodiment of the present invention, the S2 sequentially selects the data fields and verification methods to be intersected, wherein a semantic consistency check is performed on the selected data fields to ensure corresponding processing when the field names are inconsistent.

[0022] Specifically, since the field names of participants A and B may be different but have the same meaning, in order to ensure that the two parties have a consistent understanding of the fields with the same meaning and avoid matching errors, it is necessary to perform a semantic consistency check on the data fields selected by the participants.

[0023] The present invention also provides a data security intelligent verification system based on batch privacy intersection, which is characterized by comprising:

[0024] The data processing module is used to upload the full amount of data to be compared by the participants to the local computer, and select the data fields and verification methods to be intersected in order;

[0025] A homomorphic commitment value calculation module is used to concatenate the selected data fields, and to commit to the concatenated fields to form a commitment value of the concatenated fields, and to designate the commitment value as a new ID column;

[0026] A privacy intersection module, used for calculating the new ID column as an input value of batch privacy intersection calculation;

[0027] The result conversion module is used to convert the intersection result into the data intelligent verification result.

[0028] The present invention also provides an electronic device, characterized in that it includes a processor and a memory;

[0029] The memory is used to store computer programs;

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

[0031] The present invention also relates to 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, the steps of the method described in any one of claims 1 to 7 are implemented.

[0032] The beneficial effects of the present invention are: based on the privacy intersection technology, it supports batch and arbitrary field intersection functions. The fields of a data set are often diverse and complex. Supporting the intersection of arbitrary fields means that users can select the fields that need to be compared according to specific needs, thereby locating the intersection data more accurately. Based on the batch privacy intersection method, a variety of secure data intelligent verification methods are implemented by introducing a range proof algorithm, which supports consistency verification and error verification of data without leaving the domain. Among them, the range proof algorithm can verify the range of the data to ensure the accuracy and consistency of the data. By combining the privacy intersection technology, there is no need to transfer the data to other domains or platforms. This feature helps to protect the privacy and security of the data and avoid the risk of data leakage and abuse. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The figure is a flow chart of the data security intelligent verification method based on batch privacy intersection according to the present invention.

[0034] Figure 2 This is a structural schematic diagram of the data security intelligent verification system based on batch privacy intersection of the present invention.

[0035] Figure 3 It is a schematic diagram of the operation of the data security intelligent verification method based on batch privacy intersection of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0037] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0038] The first aspect of the present invention relates to a data security intelligent verification method based on batch privacy intersection, such as Figure 1 The process shown includes:

[0039] S1. Upload all the data to be compared by the participants to the local computer;

[0040] S2. Select the data fields and verification methods for intersection in order;

[0041] S3. Concatenate the selected data fields, and commit the concatenated fields to form a committed value of the concatenated fields, and designate the committed value as a new ID column;

[0042] S4. Calculate the new ID column as the input value of batch privacy intersection calculation;

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

[0044] In this specific embodiment, see Figure 3As shown in the operation flow diagram, at the beginning, the participants upload the full amount of data to be compared to the local database, and then select the data fields according to the set rules. The commitment algorithm is calculated according to the selected data fields to obtain the commitment value with the same number of digits as the new ID column, and then the value of the new ID is used as the input value of the selected verification method for consistency verification or error verification. If the consistency verification is selected, the traditional privacy algorithm technology is used to obtain the difference set and intersection set. If the error verification is performed, the commitment value is directly added and subtracted to obtain the number set of the calculation results, and the range proof algorithm is performed on the obtained number set to obtain the corresponding data field that meets the error verification setting requirements.

[0045] If the consistency check method is selected, the difference set and intersection data 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 displayed in the corresponding lists respectively. If the error check is selected, the corresponding data fields of the data set that meet the error check requirements are converted into corresponding lists for display, and the size of the errors are arranged in order in the corresponding table.

[0046] Another aspect of the present invention also relates to a system for intelligent data security verification method based on batch privacy intersection, the structure of which is as follows: Figure 2 As shown, including:

[0047] The data processing module is used to upload the full amount of data to be compared by the participants to the local computer, and select the data fields and verification methods to be intersected in order;

[0048] In this specific embodiment, the data fields to be intersected are specified in order, and the field names are not required to be the same (the text does not need to be consistent, but the meaning does need to be consistent). For example, the name, mobile phone number, and home address of participant A, and the name, contact information, and contact address of participant B are selected.

[0049] A homomorphic commitment value calculation module is used to concatenate the selected data fields, and to commit to the concatenated fields to form a commitment value of the concatenated fields, and to designate the commitment value as a new ID column;

[0050] Specifically, concatenate the selected fields and commit to them, such as

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

[0052] And assign the committed value as the new ID column.

[0053] A privacy intersection module, used for calculating the new ID column as an input value of batch privacy intersection calculation;

[0054] Specifically, the privacy intersection algorithm that can be selected is based on key exchange, as follows:

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

[0056] Bob chooses a random number as the private key. For each data y, Bob first calculates the commitment value, then encrypts it with the private key based on the commitment value, and sends the ciphertext to the participant Alice. For the ciphertext received from Alice, Bob uses the private key to re-encrypt it. Alice re-encrypts the received ciphertext based on the private key and compares the two ciphertexts. If they are consistent, it means that they have the same data.

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

[0058] Specifically, if the consistency check method is selected, the difference set and intersection data of the intersection result are directly converted into a corresponding data field list, and the data fields that meet the requirements and the data fields that do not meet the requirements are displayed in the corresponding list. If the error check is selected, the corresponding data fields of the data set that meet the error check requirements are converted into a corresponding list display, and the size of the error is arranged in order in the corresponding table.

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

[0060] An embodiment of the present invention also provides an electronic device for executing the above method. As an implementation device of the method, the electronic device has at least a processor and a memory, and in particular, the memory stores 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 implementation method and obtains the corresponding technical effect.

[0061] Preferably, the electronic device may include a bus architecture, the bus may include any number of interconnected buses and bridges, and the bus will include various circuits linked together by one or more processors and memories. The bus may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are all well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the receiver and transmitter. The receiver and transmitter may be the same component, i.e., a transceiver, providing 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 may be used to store data used by the processor when performing operations.

[0062] In addition, the electronic device may further include components such as a communication module, an input unit, an audio processor, a display, and a power supply. The processor (or controller, operation control) used may include a microprocessor or other processor device and / or a logic device, which receives input and controls the operation of various components of the electronic device; the memory may be one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices, which may store the above-mentioned related data information, and may also store a program for executing related information, and the processor may execute the program stored in the memory to realize information storage or processing, etc.; the input unit is used to provide input to the processor, for example, it may be a key or a touch input device; 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, for example, it may be an LCD display. The communication module is a transmitter / receiver that sends 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 may be the same as the case of a conventional mobile communication terminal. Based on different communication technologies, multiple communication modules may 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 a speaker and a microphone via an audio processor to provide an audio output via the speaker and receive an audio input from the microphone, thereby realizing a common telecommunication function. The audio processor may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor is also coupled to a central processor, so that recording can be performed on the machine through the microphone, and the sound stored on the machine can be played through the speaker.

[0063] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, electronic devices or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0065] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0067] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on 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 computer; S2. Select the data fields and verification methods for intersection in order; S3. concatenate the selected data fields, and commit the concatenated fields to form a committed value of the concatenated fields, and designate the committed value as a new ID column; S4. Calculate the new ID column as the input value of batch privacy intersection calculation; S5. Convert the intersection result into a data intelligent verification result.

2. The method according to claim 1, characterized in that The verification method for sequentially selecting the intersection in S2 includes consistency verification and error verification, wherein the error verification includes setting an acceptable range for the difference of the commitment values ​​of the participants.

3. The method according to claim 2, characterized in that The S5 converts the intersection result into a data intelligent verification result, including judging whether the intersection result meets the verification requirements of the verification method, and the verification requirements include meeting consistency requirements and error verification requirements.

4. The method according to claim 3, characterized in that The consistency requirement includes executing the batch private intersection, obtaining an intersection of the intersection results as data that meets the consistency requirement, and a difference set of the intersection results as data that does not meet the consistency requirement.

5. The method according to claim 3, characterized in that The error checking requirement includes setting an acceptable range of differences of the intersection result data of the participating party, and performing a range proof check on the intersection result data set. If the range proof can be generated, it is determined that the intersection result data meets the error checking requirement. Otherwise, it is considered that the intersection result data set does not meet the error checking requirement.

6. The method according to claim 1, characterized in that The S3 also includes the selected data fields being spliced ​​and replaced with a commitment scheme having homomorphism.

7. The method according to claim 1, characterized in that The S2 sequentially selects the data fields and verification methods to be intersected, wherein the selected data fields are subjected to a semantic consistency check to ensure corresponding processing when the field names are inconsistent.

8. 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 computer, and select the data fields and verification methods to be intersected in order; A homomorphic commitment value calculation module is used to concatenate the selected data fields, and to commit to the concatenated fields to form a commitment value of the concatenated fields, and to designate the commitment value as a new ID column; A privacy intersection module, used for calculating the new ID column as an input value of batch privacy intersection calculation; The result conversion module is used to convert the intersection result into the data intelligent verification result.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store computer programs; The processor is configured to execute the method according to any one of claims 1 to 7 by calling a stored computer program.

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

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