Multi-party data set outsourcing privacy calculation intersection method

Abstract

The invention belongs to the technical field of privacy computing. The invention provides a multi-party data set outsourcing privacy calculation intersection method. According to the multi-party intersection-oriented block calculation method provided by the embodiment of the invention, the data is fragmented, and the calculation efficiency is improved while the privacy security is ensured in combination with a dynamic load balancing technology. Multi-party data security calculation without participation of a trusted third party is realized, and the decentration capability of the system is enhanced. A differential privacy technology and a secret sharing mechanism are fused, so that the privacy security of a calculation process is ensured, and the possibility that an attacker deduces original data through an output result is prevented. The method can dynamically adapt to the number change of multiple parties, supports distributed deployment and extension in a heterogeneous environment, and has relatively high practicability. And a compression transmission and data coding optimization strategy is introduced, so that the data transmission quantity and communication rounds are remarkably reduced, and the utilization efficiency of network resources is improved.

Description

Background Art

[0002] With the rapid development of big data and artificial intelligence technologies, the importance of data resources has become increasingly prominent. In many application scenarios, different organizations, enterprises or individuals often hold complementary data, and data cooperation can create higher value. However, for the sake of data privacy and security, it is not feasible to directly share the original data. This has led to the widespread application of privacy computing intersection technology. Privacy computing intersection technology can realize the calculation of the intersection of data sets without exposing the data of all parties. For example, in joint modeling, different companies can achieve collaborative marketing by calculating the intersection of customer data; in the medical field, different hospitals can use the intersection of patient data for joint research while effectively protecting patient privacy.

[0003] In practical applications, the scenarios involving privacy computing intersection are often not limited to two parties, but involve multi-party collaboration. However, most existing technologies focus on two-party intersection, which makes it difficult to effectively cope with the complex needs of multi-party collaboration. At the same time, with the continuous growth of data scale, privacy computing intersection also faces severe challenges in terms of communication overhead and computational complexity. On the one hand, the huge amount of data leads to a significant increase in communication overhead; on the other hand, encryption operations and complex data processing processes make it difficult for computing efficiency to meet actual needs. In addition, traditional privacy computing intersection technologies usually rely on trusted third parties to coordinate and process data.

[0004] In the related technologies, the intersection of privacy computing schemes is mainly divided into two-party privacy computing intersection and multi-party privacy computing intersection. The technical points of the intersection of two-party privacy computing mainly focus on the application of cryptographic methods, such as homomorphic encryption, Boolean circuits and blind transmission, to achieve the secure calculation of the intersection of data sets by both parties without exposing the original data. These schemes usually have low communication and computational complexity and are suitable for small and medium-scale data scenarios. In contrast, the intersection of multi-party privacy computing requires collaborative calculations among multiple participants, and the technical complexity is significantly increased. The intersection of multi-party privacy computing uses secret sharing or distributed Boolean circuits to achieve secure collaboration without a trusted third party, optimizes computing efficiency through parallel computing and block strategies, uses differential privacy to protect data output security, and combines blockchain or trusted execution environment to reduce dependence on trusted third parties. Multi-party schemes focus on communication round optimization and scalability design to meet the challenges of large-scale data and multi-party collaboration. However, due to the need for a large amount of secret sharing, Boolean circuit calculations or distributed protocol interactions, multi-party schemes have high communication rounds and large data transmission volume, which seriously affects efficiency.

[0005] Therefore, it is necessary to improve one or more problems existing in the above-mentioned related technical solutions.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0007] The purpose of the embodiments of the present disclosure is to provide a method for outsourcing privacy computing intersections of multiple data sets, thereby overcoming one or more problems caused by limitations and defects of related technologies at least to a certain extent.

[0008] According to a first aspect of an embodiment of the present disclosure, a method for outsourcing privacy computation intersection of multiple datasets is provided, the method comprising: Mapping the local data ID set to a Bloom filter set using multiple hash functions, and inverting the Bloom filter set to obtain an inverse Bloom filter; Encoding the inverse Bloom filter using a super-increasing sequence to obtain plaintext data; Encrypting the plaintext data to obtain ciphertext data, and uploading the ciphertext data to a cloud server; Obtaining the aggregated ciphertext data sent by the cloud server, and decrypting the aggregated ciphertext data to obtain aggregated plaintext data; Decoding the aggregated plaintext data using a super-incremental sequence to obtain multi-dimensional aggregated data; Permuting the multi-dimensional aggregate data to obtain an intersection Bloom filter; The intersection Bloom filter is queried to obtain the intersection of the local data.

[0009] Furthermore, the expression for encrypting the plaintext data is:

[0010] in, is the encrypted ciphertext data, is the public key generator, is plain text data, is a random number, is the public key modulus.

[0011] Furthermore, the expression for decrypting the aggregated ciphertext data is:

[0012] in, To aggregate plaintext data, is the Lagrangian function.

[0013] According to a second aspect of an embodiment of the present disclosure, a method for outsourcing privacy computation intersection of multiple datasets is provided, the method comprising: Get the ciphertext data uploaded by all clients; All the ciphertext data are aggregated to obtain aggregated ciphertext data.

[0014] Furthermore, the expression for aggregating all the ciphertext data is:

[0015]

[0016]

[0017] in, To aggregate ciphertext data, is the encrypted ciphertext data, is the public key generator, is plain text data, is a random number, is the public key modulus.

[0018] According to a third aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the steps of the method for outsourcing privacy computing intersection of multiple data sets described in any one of the above embodiments are implemented.

[0019] According to a fourth aspect of an embodiment of the present disclosure, there is provided an electronic device, including: Processor; and A memory, configured to store executable instructions of the processor; In which, the processor is configured to execute the steps of the method for outsourcing privacy computing intersection of multiple data sets described in any of the above embodiments by executing the executable instructions.

[0020] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: In the embodiments of the present disclosure, through the above-mentioned method of outsourcing the intersection of privacy calculation of multi-party data sets, a block calculation method for multi-party intersection is proposed, which processes data in pieces and combines it with dynamic load balancing technology to improve computing efficiency while ensuring privacy and security. Multi-party data security calculation is achieved without the participation of a trusted third party, enhancing the decentralization capability of the system. The integration of differential privacy technology and secret sharing mechanism not only ensures the privacy and security of the calculation process, but also prevents the possibility of attackers inferring the original data through the output results. It can dynamically adapt to changes in the number of parties, while supporting distributed deployment and expansion in heterogeneous environments, and has strong practicality. The introduction of compression transmission and data encoding optimization strategies can significantly reduce the amount of data transmission and communication rounds, and improve the utilization efficiency of network resources.

[0021] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0023] Figure 1 A diagram showing the steps of a method for outsourcing privacy computation intersection of multiple datasets in an exemplary embodiment of the present disclosure; Figure 2 A schematic diagram of a scene in an exemplary embodiment of the present disclosure is shown; Figure 3 A diagram showing the steps of another method for outsourcing privacy computation intersection of multi-party datasets in an exemplary embodiment of the present disclosure; Figure 4 A schematic diagram showing a program product in an exemplary embodiment of the present disclosure; Figure 5 A schematic diagram showing an electronic device in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0025] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0026] This example implementation first provides a method for outsourcing the intersection of privacy computing of multiple data sets. The method can be applied to a terminal device, such as a mobile phone, a personal digital assistant, a laptop, a tablet computer, a smart watch, or other mobile terminal [flexibly adjusted according to specific circumstances, such as the terminal device is a server, etc.]. Figure 1 As shown in , the method may include the following steps: Step S101: using multiple hash functions to map a local data ID set to a Bloom filter set, and inverting the Bloom filter set to obtain an inverse Bloom filter; Step S102: Encoding the inverse Bloom filter using a super-increasing sequence to obtain plaintext data; Step S103: encrypting the plaintext data to obtain ciphertext data, and uploading the ciphertext data to a cloud server; Step S104: obtaining the aggregated ciphertext data sent by the cloud server, and decrypting the aggregated ciphertext data to obtain aggregated plaintext data; Step S105: decoding the aggregated plaintext data using a super-incremental sequence to obtain multi-dimensional aggregated data; Step S106: permuting the multi-dimensional aggregated data to obtain an intersection Bloom filter; Step S107: query the intersection Bloom filter to obtain the intersection of local data.

[0027] Through the above-mentioned method of outsourcing the intersection of privacy computing of multi-party data sets, a block computing method for multi-party intersection is proposed. The data is processed in pieces and combined with dynamic load balancing technology to improve computing efficiency while ensuring privacy security. Multi-party data security computing without the participation of a trusted third party is realized, and the decentralization capability of the system is enhanced. The integration of differential privacy technology and secret sharing mechanism not only ensures the privacy security of the computing process, but also prevents the possibility of attackers inferring the original data through the output results. It can dynamically adapt to changes in the number of parties, and supports distributed deployment and expansion in heterogeneous environments, with strong practicality. The introduction of compression transmission and data encoding optimization strategies can significantly reduce the amount of data transmission and communication rounds, and improve the utilization efficiency of network resources.

[0028] Next, we will refer to Figure 1 to Figure 2 Each step of the above method in this example implementation is described in more detail.

[0029] In step S101, a local data ID set is mapped to a Bloom filter set using multiple hash functions, and the Bloom filter set is inverted to obtain an inverse Bloom filter.

[0030] For example, Figure 2 As shown, this application includes a cloud server S and several clients, each of which contains its own data set. This application requires a trusted third party to generate a pair of public and private keys ( ) , the key center sets the security parameters as , select the modulus , and is a large prime number. The key center selects the generator ;in, =1 , , where the public key , private key .

[0031] This application requires a trusted third party to generate a super-incremental sequence ,in, .

[0032] A trusted third party will provide the public and private keys ( ) and super-increasing sequences Distributed to each client, each client uses the public key Encrypt data using a private key Decrypt the data. Each client uses a super-increasing sequence to encode and decode the multi-dimensional data.

[0033] The local client uses the data A hash function maps the local data ID set to the Bloom filter ,in, is the Bloom filter constructed by client i, It is a Bloom filter A bit array containing k positions.

[0034] The local client inverts the Bloom filter to obtain the inverse Bloom filter , represents the client's inverse bloom filter, is the array of the number of bits of the inverse Bloom filter.

[0035] In step S102, the inverse Bloom filter is encoded using a super-increasing sequence to obtain plaintext data.

[0036] For example, each client has a Bloom filter Encode to obtain , is the encoding result of client i, which is a unique value representing the state of the inverse Bloom filter and is used for subsequent encryption and data aggregation. Represents a superorder increasing sequence.

[0037]

[0038] In step S103, the plaintext data is encrypted to obtain ciphertext data, and the ciphertext data is uploaded to the cloud server.

[0039] For example, each client encodes data Encryption to obtain , Represents the encrypted ciphertext, g public key generator, used for encryption, is the plaintext data, the input value to be encrypted, r is a random number used to enhance the security of the ciphertext, n is the public key modulus, which is equal to the product of two large prime numbers p and q, The range of the modulo operation is used to limit the size of the calculation result.

[0040]

[0041] Each client sends the encrypted data Upload to cloud server S.

[0042] In step S104, the aggregated ciphertext data sent by the cloud server is obtained, and the aggregated ciphertext data is decrypted to obtain aggregated plaintext data.

[0043] For example, each client decrypts the aggregation result to obtain , is the aggregated plaintext data, the result after decryption, : Lagrangian function, calculation .

[0044]

[0045] In step S105, the aggregated plaintext data is decoded using a super-incremental sequence to obtain multi-dimensional aggregated data.

[0046] For example, each client uses a super-incrementing sequence to sort the data Decode to obtain ， , the specific decoding is shown in Algorithm 1:

[0047] In step S106, the multi-dimensional aggregated data is permuted to obtain an intersection Bloom filter.

[0048] For example, each client decodes the data set ， Setting up a Bloom filter , the specific algorithm is as follows:

[0049] In step S107, the intersection Bloom filter is queried to obtain the intersection of local data.

[0050] For example, each client obtains a Bloom filter ,The client uses the local dataset ID to query the filter to obtain the intersection of the datasets of each client.

[0051] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc. In addition, it is also easy to understand that these steps may be, for example, executed synchronously or asynchronously in multiple modules / processes / threads.

[0052] Furthermore, in this example implementation, a method for outsourcing privacy computation intersection of multiple datasets is also provided. Figure 3 As shown in , the method may include the following steps: Step S201: Obtain the encrypted data uploaded by all clients; Step S202: Aggregate all the ciphertext data to obtain aggregated ciphertext data.

[0053] For example, the cloud server aggregates the ciphertext data of each client to obtain:

[0054]

[0055]

[0056] Each client will aggregate the key arts Returned to each client.

[0057] In one embodiment, privacy-preserving data mapping and permutation based on Bloom filters. A cluster of hash functions is used to map a data ID set to a Bloom filter, and an inverse Bloom filter is generated by permutation, thereby ensuring data privacy protection while improving data processing efficiency. By permuting the inverse Bloom filter, the ability to obfuscate data patterns is further enhanced to avoid directly exposing implicit characteristics of the data.

[0058] In one embodiment, a super-increasing sequence encoding and decoding mechanism is used to encode the Bloom filter using a super-increasing sequence, and multi-dimensional data is mapped into a single scalar to achieve efficient data packaging and processing. The unique properties of the super-increasing sequence are used to decode the aggregated multi-dimensional data to accurately restore the original data dimensions and content.

[0059] In one embodiment, data encryption and decryption based on homomorphic encryption. Homomorphic encryption technology (such as Paillier encryption) is applied to achieve secure aggregation of data uploaded by each client without decrypting the data. The security of encrypted data is ensured through public and private key pairing, and the client is allowed to decrypt the aggregation result to restore the data content. In one embodiment, a secure aggregation process is designed between the client and the cloud server. The collaborative workflow between the client and the cloud server is designed to allow each client to upload encrypted data. The cloud server only performs aggregation operations on the encrypted data without accessing the plaintext data. The cloud server returns the aggregated ciphertext to the client. Each client restores the aggregation result through decryption and performs subsequent processing to avoid leakage of private data in the cloud.

[0060] In one embodiment, an intersection Bloom filter is generated based on the decoded data. After the client decodes the aggregated data, an intersection Bloom filter is constructed through logical judgment to achieve privacy-preserving calculation of the intersection of sets. An algorithm design for generating the intersection of Bloom filters is proposed to ensure the efficiency and accuracy of intersection calculation.

[0061] In one embodiment, the privacy computing intersection of multiple parties is supported. The solution supports multi-client collaborative computing, combines super-increasing sequences and homomorphic encryption, and implements data intersection computing without relying on a trusted third party. Data intersection computing is scalable and suitable for multi-client scenarios, and can meet the privacy computing needs of large-scale data.

[0062] In one embodiment, a privacy protection mechanism against reverse reasoning is used. The data encoding, encryption, substitution and intersection calculation processes all adopt a distributed design to prevent a single party or the cloud from obtaining private data through reverse reasoning.

[0063] In one embodiment, the differential privacy of the result is enhanced. In the intersection result output stage, the output result is perturbed through the differential privacy mechanism to further enhance the privacy protection capability.

[0064] In a specific embodiment, the present application has higher privacy protection. By combining Bloom filter, inverse Bloom filter and homomorphic encryption, the present invention avoids plain text transmission and direct calculation in the entire data processing process, ensuring that the client data exists only in encrypted form on the cloud server, effectively avoiding privacy leakage.

[0065] This application supports efficient collaboration with multiple parties. Through the combination of super-incremental sequence coding and homomorphic encryption, the present invention can realize multi-client collaborative data intersection calculation without a trusted third party, is suitable for multi-party scenarios and has good scalability.

[0066] This application takes into account both coding efficiency and computational accuracy. By using the super-incremental sequence coding mechanism, the data of the Bloom filter is mapped into a single scalar, which greatly simplifies the data aggregation and transmission process, and can accurately restore multi-dimensional data through decoding to ensure computational accuracy.

[0067] The present invention has lower computational and communication overhead. Through the efficient data representation of Bloom filters and the encoding compression of super-incremental sequences, the present invention significantly reduces the amount of data transmission between the client and the cloud server, reducing the communication cost. At the same time, the aggregation characteristics of homomorphic encryption greatly optimize the computational overhead of the cloud.

[0068] The present application improves security against reverse reasoning. Through Bloom filter replacement, homomorphic encryption and logic flipping operations, the present invention introduces additional obfuscation protection measures at each stage of data processing to prevent intermediate data from being reversed, further improving security.

[0069] The present application is flexible and adaptable to multiple scenarios. The present invention uses Bloom filter as the underlying data structure, has good versatility, and can be flexibly applied to multiple scenarios such as large-scale data set intersection calculation, data matching and sharing.

[0070] The present invention is an innovative design for efficient set intersection calculation. Through inverse Bloom filter permutation and logic flipping operations, the present invention can accurately mark the intersection results, reduce the interference of false positive examples, and improve the accuracy of intersection calculation.

[0071] Through the above-mentioned method of outsourcing the intersection of privacy computing of multi-party data sets, a block computing method for multi-party intersection is proposed. The data is processed in pieces and combined with dynamic load balancing technology to improve computing efficiency while ensuring privacy security. Multi-party data security computing without the participation of a trusted third party is realized, and the decentralization capability of the system is enhanced. The integration of differential privacy technology and secret sharing mechanism not only ensures the privacy security of the computing process, but also prevents the possibility of attackers inferring the original data through the output results. It can dynamically adapt to changes in the number of parties, and supports distributed deployment and expansion in heterogeneous environments, with strong practicality. The introduction of compression transmission and data encoding optimization strategies can significantly reduce the amount of data transmission and communication rounds, and improve the utilization efficiency of network resources.

[0072] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0073] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of a module or unit described above can be further divided into multiple modules or units for concretization. The components displayed as modules or units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. Those of ordinary skill in the art can understand and implement it without paying creative work.

[0074] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, and when the program is included in the processor and executed, the steps of the method for outsourcing the intersection of multi-party datasets for privacy calculation can be implemented in any of the above embodiments. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of various exemplary embodiments of the present invention described in the method for outsourcing the intersection of multi-party datasets for privacy calculation in the above-mentioned section of this specification.

[0075] refer to Figure 4As shown, a program product 300 for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0076] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0077] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0078] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0079] In an exemplary embodiment of the present disclosure, an electronic device is also provided, which may include a processor and a memory for storing executable instructions of the processor. The processor is configured to execute the steps of the method for outsourcing privacy computation intersection of multi-party datasets in any of the above embodiments by executing the executable instructions.

[0080] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0081] Refer to the following Figure 5 The electronic device 600 according to this embodiment of the present invention is described. Figure 5 The electronic device 600 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0082] like Figure 5 As shown, the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0083] The storage unit stores a program code, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps of various exemplary embodiments of the present invention described in the method section of the above multi-party dataset outsourcing privacy computing intersection of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0084] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0085] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0086] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0087] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 650. In addition, the electronic device 600 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 through a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0088] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, server or network device, etc.) to execute the above-mentioned method of outsourcing privacy computing intersection of multi-party data sets according to the implementation of the present disclosure.

[0089] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A method for outsourcing privacy computation intersection of multiple datasets, characterized in that: The method includes: Mapping the local data ID set to a Bloom filter set using multiple hash functions, and inverting the Bloom filter set to obtain an inverse Bloom filter; Encoding the inverse Bloom filter using a super-increasing sequence to obtain plaintext data; Encrypting the plaintext data to obtain ciphertext data, and uploading the ciphertext data to a cloud server; Obtaining the aggregated ciphertext data sent by the cloud server, and decrypting the aggregated ciphertext data to obtain aggregated plaintext data; Decoding the aggregated plaintext data using a super-incremental sequence to obtain multi-dimensional aggregated data; Permuting the multi-dimensional aggregate data to obtain an intersection Bloom filter; The intersection Bloom filter is queried to obtain the intersection of the local data.

2. The method for outsourcing privacy computation intersection of multiple datasets according to claim 1, characterized in that: The expression for encrypting the plaintext data is: in, is the encrypted ciphertext data, is the public key generator, is plain text data, is a random number, is the public key modulus.

3. The method for outsourcing privacy computation intersection of multiple datasets according to claim 2, characterized in that: The expression for decrypting the aggregated ciphertext data is: in, To aggregate plaintext data, is the Lagrangian function.

4. A method for outsourcing privacy computation intersection of multiple datasets, characterized in that: The method includes: Get the ciphertext data uploaded by all clients; All the ciphertext data are aggregated to obtain aggregated ciphertext data.

5. The method for outsourcing privacy computation intersection of multiple datasets according to claim 4, characterized in that: The expression for aggregating all the ciphertext data is: in, To aggregate ciphertext data, is the encrypted ciphertext data, is the public key generator, is plain text data, is a random number, is the public key modulus.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for outsourcing privacy computing intersection of multiple data sets as described in any one of claims 1 to 3 are implemented.

7. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; In which, the processor is configured to execute the steps of the method for outsourcing privacy computing intersection of multi-party data sets as described in any one of claims 1 to 3 by executing the executable instructions.

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