Local differential privacy-based distance query method, apparatus and device, and medium

Abstract

The invention relates to a privacy protection technology, and discloses a distance query method based on local differential privacy, which comprises the following steps: acquiring a social network graph, and initializing a distance vector of each client in the social network graph; the client adds a noise value to the distance vector, distributes the distance vector to a neighbor client, and updates the distance vector of each client after aggregation operation; and repeating the steps of adding noise to the distance vector of the client, distributing the distance vector to the neighbor client and updating the distance vector of each client through aggregation operation until a repeat stop condition is met, thereby obtaining a distance query result. The invention further provides a local differential privacy-based distance query device, equipment and a medium. According to the invention, the security and efficiency of distance query based on local differential privacy can be improved.

Description

Technical Field

[0001] The present invention relates to the field of privacy protection technology, and in particular to a distance query method, device, equipment and medium based on local differential privacy. Background Art

[0002] In today's digital age, with the popularity of big data, the role of graph data in society is becoming increasingly important. Graph data reveals complex network structures and interactions through relationship mapping between nodes (vertices) and edges (edges), from social networks to the Internet of Things, from ecosystems to economic models.

[0003] However, with the gradual digitization of personal information, how to use these graph data for effective analysis without exposing user privacy has become a key issue in modern society. Local differential privacy (LDP) provides a powerful tool to solve this problem. It allows users to process their data locally, so that even if the data is obtained by an external third party, personal information cannot be accurately interpreted, which greatly protects personal privacy. Local Differential Privacy (LDP) is an important development in the field of privacy protection in recent years. It is a special case of differential privacy, which was proposed by Dwork et al. in 2006 to provide a framework for quantifying the level of data privacy protection. Unlike traditional differential privacy, it implements privacy protection at the data collection stage rather than the data release stage. Local differential privacy randomizes the data on the user side so that no third party, including the data collector, can accurately know the data of any specific user, thereby ensuring the security of user privacy.

[0004] The applications of graph data in different fields are diverse. For example, in social networks, graph data analysis can help identify influential users, predict information dissemination patterns, and optimize advertising targeting. In supply chain management, by analyzing the graph data of the supply network, companies can optimize inventory, reduce costs, and improve efficiency. In the financial field, the analysis of transaction networks can be used to prevent fraud and money laundering. And in urban planning, graph data analysis of transportation networks helps optimize traffic flow and reduce congestion.

[0005] Distance query is a basic and important function in graph data analysis. It not only relates to the topological structure of the graph, but also reflects the closeness of the relationship between nodes. In a graph, distance usually refers to the length of the shortest path between two nodes, which is called the "shortest path distance". It is one of the key indicators for evaluating the speed and efficiency of information propagation in a network. In social networks, the shortest path distance can help us understand the close relationship between users; in logistics networks, it helps calculate the most efficient route from one point to another. However, ensuring privacy often means a certain degree of perturbation of the original data, which may affect the utility of the query results.

[0006] Therefore, in order to perform efficient distance queries while protecting user privacy, a distance query method based on local differential privacy is urgently needed to balance privacy protection and the utility of query results. Summary of the invention

[0007] The present invention provides a distance query method, device, equipment and medium based on local differential privacy, which can improve the security and efficiency of distance query based on local differential privacy.

[0008] To achieve the above object, the present invention provides a distance query method based on local differential privacy, comprising:

[0009] Obtain a social network graph and initialize a distance vector of each client in the social network graph;

[0010] The client adds a noise value to the distance vector and distributes it to neighboring clients, and updates the distance vector of each client after performing an aggregation operation;

[0011] Repeat the steps of adding noise to the distance vector of the client and then distributing it to neighboring clients and performing aggregation operations to update the distance vector of each client until a repetition stop condition is met to obtain a distance query result.

[0012] Optionally, before initializing the distance vector of each client in the social network graph, the method further includes:

[0013] Each client presets distance thresholds and privacy parameters.

[0014] Optionally, the client adds a noise value to the distance vector and distributes the noise value to neighbor clients, including:

[0015] A noise value is obtained by a probability distribution sampling function, and the noise value is added to the distance vector.

[0016] Optionally, obtaining the noise value through a probability distribution sampling function includes:

[0017] The noise value is obtained using the following probability distribution sampling function:

[0018]

[0019] Among them, x is the noise value and b is the scale parameter.

[0020] Optionally, adding the noise value to the distance vector comprises:

[0021] The noise value is added to the distance vector using the following formula:

[0022]

[0023] in, is the distance vector of client u after adding the noise value, is the initial distance vector of the client, ∈ is the privacy parameter, R is the distance threshold, and X is the independent random variable parameter.

[0024] Optionally, updating the distance vector of each client after performing the aggregation operation includes:

[0025] Each client traverses the clients other than itself, and uses a preset aggregation formula to calculate the distance vector between the client and the clients other than itself to obtain the target distance vector;

[0026] The target distance vector is used to update the distance vector between each client and the clients other than itself, and the distance vector collection between each client and the clients other than itself is obtained by summarizing.

[0027] Optionally, the calculating the distance vector between the client and clients other than the client by using a preset aggregation formula includes:

[0028] The following aggregation formula is used to calculate the distance between a client and other clients except itself:

[0029]

[0030] in, is the distance from client u to client j obtained by the k+1th aggregation, is the distance from client i to client j in the kth aggregation, is the distance from client u to client j obtained in the kth aggregation, [n] represents the set {1,2,…,n}, and N(u) represents the set of neighboring clients of client u.

[0031] In order to solve the above problem, the present invention also provides a distance query device based on local differential privacy, the device comprising:

[0032] A distance vector initialization module, used to obtain a social network graph and initialize the distance vector of each client in the social network graph;

[0033] A distance vector aggregation module, which is used for the client to add a noise value to the distance vector and distribute it to neighboring clients, and to update the distance vector of each client after performing an aggregation operation;

[0034] The distance query result acquisition module is used to repeatedly add noise to the distance vector of the client and distribute it to neighboring clients and perform aggregation operations to update the distance vector of each client until a repetition stop condition is reached to obtain the distance query result.

[0035] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0036] at least one processor; and,

[0037] a memory communicatively connected to the at least one processor; wherein,

[0038] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned distance query method based on local differential privacy.

[0039] In order to solve the above problem, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned distance query method based on local differential privacy.

[0040] The present invention adds a noise value to the distance vector by the client and distributes it to the neighboring clients. The security of each client in the social network graph can be improved by adding a noise value, and the privacy security is enhanced. Only one noise value is added to the result of each distance query, and the disturbance is small. In addition, by repeatedly adding noise to the distance vector of the client and distributing it to the neighboring clients and performing an aggregation operation to update the distance vector of each client, the efficiency of the distance query can be improved and the minimum value of the distance vector can be obtained during the distance query. The present invention can improve the utility of the distance query result while protecting the user's privacy. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of a flow chart of a distance query method based on local differential privacy provided by an embodiment of the present invention;

[0042] Figure 2A functional module diagram of a distance query device based on local differential privacy provided by an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of the structure of an electronic device for implementing a distance query method based on local differential privacy provided by an embodiment of the present invention.

[0044] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0045] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0046] The embodiment of the present application provides a distance query method based on local differential privacy. The execution subject of the distance query method based on local differential privacy includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the distance query method based on local differential privacy can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0047] Reference Figure 1 As shown, it is a flow chart of a distance query method based on local differential privacy provided by an embodiment of the present invention. In this embodiment, the distance query method based on local differential privacy includes:

[0048] S1. Obtain a social network graph and initialize a distance vector of each client in the social network graph.

[0049] In an embodiment of the present invention, the social network graph refers to a network depicting the relationships between individuals (such as people or organizations), wherein each node, also called a client, represents an individual, and an edge represents the relationship between individuals, such as friendship, cooperation or communication path.

[0050] In the embodiment of the present invention, the distance vector refers to the distance from the client to other clients in the social network graph.

[0051] As an embodiment of the present invention, before initializing the distance vector of each client in the social network graph, the method further includes:

[0052] Each client presets distance thresholds and privacy parameters.

[0053] In the embodiment of the present invention, the distance threshold refers to the limit of distance query from each client to other clients in the social network graph, which is used to determine the maximum error acceptable in distance calculation. The distance threshold affects the error and calculation efficiency of distance query.

[0054] In the embodiment of the present invention, the privacy parameter refers to the degree of privacy protection, which is used to measure the degree of privacy loss when data is released. The smaller the privacy parameter, the higher the degree of privacy protection.

[0055] Exemplarily, the initialization of the distance vector of each client in the social network graph may be performed by the following steps:

[0056] Each client generates its own initial distance vector Where 0 represents the result of the 0th aggregation, where the length of the initial distance vector is n, and n is the number of all clients in the social network graph. The distance between client u and client j is P; if It means that the distance between client u and client j is 1.

[0057] S2. The client adds a noise value to the distance vector and distributes it to neighboring clients, and updates the distance vector of each client after performing an aggregation operation.

[0058] In the embodiment of the present invention, the noise value refers to a randomly generated data in differential privacy protection, which is used to cover or disrupt the original data to prevent sensitive information from being leaked.

[0059] As an embodiment of the present invention, the client adds a noise value to the distance vector and distributes it to neighbor clients, including:

[0060] A noise value is obtained by a probability distribution sampling function, and the noise value is added to the distance vector.

[0061] In the embodiment of the present invention, the probability distribution sampling function is a method for obtaining a noise value. For example, a Laplace noise value may be obtained through a Laplace probability distribution.

[0062] Furthermore, the noise value is obtained through the probability distribution sampling function, including:

[0063] The noise value is obtained using the following probability distribution sampling function:

[0064]

[0065] Among them, x is the noise value and b is the scale parameter.

[0066] In the embodiment of the present invention, the value of the scale parameter is usually 1.

[0067] Further, the adding the noise value to the distance vector comprises:

[0068] The noise value is added to the distance vector using the following formula:

[0069]

[0070] in, is the distance vector of client u after adding the noise value, is the initial distance vector of the client, ∈ is the privacy parameter, T is the distance threshold, and X is the independent random variable parameter.

[0071] Furthermore, the updating of the distance vector of each client after the aggregation operation includes:

[0072] Each client traverses the clients other than itself, and uses a preset aggregation formula to calculate the distance vector between the client and the clients other than itself to obtain the target distance vector;

[0073] The target distance vector is used to update the distance vector between each client and the clients other than itself, and the distance vector collection between each client and the clients other than itself is obtained by summarizing.

[0074] Furthermore, the calculating of the distance vector between the client and the client other than the client by using a preset aggregation formula includes:

[0075] The following aggregation formula is used to calculate the distance between a client and other clients except itself:

[0076]

[0077] Among them, j∈[n] / N(u), is the distance from client u to client j obtained by the k+1th aggregation, is the distance from client i to client j in the kth aggregation, is the distance from client u to client j obtained in the kth aggregation, [n] represents the set {1,2,…,n}, and N(u) represents the set of neighboring clients of client u.

[0078] Exemplarily, in the polymerization, the number of polymerizations k=0.

[0079] For example, the client obtains the minimum distance from the distance vector collection as follows: Client u knows that the distance from its neighbor i to client j is Then its distance to client j can be represented by the minimum distance from neighbors to client j + 1, that is, At the same time, add this distance to the previous distance to client j Compare and update the minimum value.

[0080] In the embodiment of the present invention, the client adds a noise value to the distance vector and distributes it to neighbor clients. The security of each client in the social network graph can be improved by adding the noise value, thereby enhancing privacy security.

[0081] S3, repeating the steps of adding noise to the distance vector of the client and then distributing it to neighboring clients and performing aggregation operations to update the distance vector of each client until a repetition stop condition is reached and a distance query result is obtained.

[0082] In an embodiment of the present invention, the repeated stopping condition may be that the distance queried between each client under the distance threshold is the shortest distance. The repeated stopping condition may also be the number of iterations. The number of iterations refers to the number of queries corresponding to the distance threshold minus 1, that is, minus the 0th query. The number of iterations is expressed as T-1.

[0083] In the embodiment of the present invention, the client adds a noise value to the distance vector and distributes it to neighbor clients. The security of each client in the social network graph can be improved by adding a noise value, and privacy security is enhanced. In addition, only one noise value is added to the result of each distance query, and the disturbance is small. In addition, by repeatedly adding noise to the distance vector of the client and distributing it to neighbor clients and performing aggregation operations to update the distance vector of each client, the efficiency of the distance query can be improved and the minimum value of the distance vector can be obtained during the distance query. The embodiment of the present invention can perform efficient distance query while protecting user privacy.

[0084] like Figure 2 , is a functional module diagram of a distance query device based on local differential privacy provided by an embodiment of the present invention.

[0085] The distance query device 100 based on local differential privacy of the present invention can be installed in an electronic device. According to the implemented functions, the distance query device 100 based on local differential privacy can include a distance vector initialization module 101, a distance vector aggregation module 102 and a distance query result acquisition module 103.

[0086] The module described in the present invention may also be referred to as a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and is stored in a memory of the electronic device.

[0087] In this embodiment, the functions of each module / unit are as follows:

[0088] The distance vector initialization module 101 is used to obtain a social network graph and initialize the distance vector of each client in the social network graph.

[0089] In an embodiment of the present invention, the social network graph refers to a network depicting the relationships between individuals (such as people or organizations), wherein each node, also called a client, represents an individual, and an edge represents the relationship between individuals, such as friendship, cooperation or communication path.

[0090] In the embodiment of the present invention, the distance vector refers to the distance from the client to other clients in the social network graph.

[0091] As an embodiment of the present invention, before initializing the distance vector of each client in the social network graph, the method further includes:

[0092] Each client presets distance thresholds and privacy parameters.

[0093] In the embodiment of the present invention, the distance threshold refers to the limit of distance query from each client to other clients in the social network graph, which is used to determine the maximum error acceptable in distance calculation. The distance threshold affects the error and calculation efficiency of distance query.

[0094] In the embodiment of the present invention, the privacy parameter refers to the degree of privacy protection, which is used to measure the degree of privacy loss when data is released. The smaller the privacy parameter, the higher the degree of privacy protection.

[0095] Exemplarily, the initialization of the distance vector of each client in the social network graph may be performed by the following steps:

[0096] Each client generates its own initial distance vector Where 0 represents the result of the 0th aggregation, where the length of the initial distance vector is n, and n is the number of all clients in the social network graph. The distance between client u and client j is P; if It means that the distance between client u and client j is 1.

[0097] The distance vector aggregation module 102 is used for the client to add a noise value to the distance vector and distribute it to neighboring clients, and to update the distance vector of each client after performing an aggregation operation.

[0098] In the embodiment of the present invention, the noise value refers to a randomly generated data in differential privacy protection, which is used to cover or disrupt the original data to prevent sensitive information from being leaked.

[0099] As an embodiment of the present invention, the client adds a noise value to the distance vector and distributes it to neighbor clients, including:

[0100] A noise value is obtained by a probability distribution sampling function, and the noise value is added to the distance vector.

[0101] In the embodiment of the present invention, the probability distribution sampling function is a method for obtaining a noise value. For example, a Laplace noise value may be obtained through a Laplace probability distribution.

[0102] Furthermore, the noise value is obtained through the probability distribution sampling function, including:

[0103] The noise value is obtained using the following probability distribution sampling function:

[0104]

[0105] Among them, x is the noise value and b is the scale parameter.

[0106] In the embodiment of the present invention, the value of the scale parameter is usually 1.

[0107] Further, the adding the noise value to the distance vector comprises:

[0108] The noise value is added to the distance vector using the following formula:

[0109]

[0110] in, is the distance vector of client u after adding the noise value, is the initial distance vector of the client, ∈ is the privacy parameter, R is the distance threshold, and X is the independent random variable parameter.

[0111] Furthermore, the updating of the distance vector of each client after the aggregation operation includes:

[0112] Each client traverses the clients other than itself, and uses a preset aggregation formula to calculate the distance vector between the client and the clients other than itself to obtain the target distance vector;

[0113] The target distance vector is used to update the distance vector between each client and the clients other than itself, and the distance vector collection between each client and the clients other than itself is obtained by summarizing.

[0114] Furthermore, the calculating of the distance vector between the client and the client other than the client by using a preset aggregation formula includes:

[0115] The following aggregation formula is used to calculate the distance between a client and other clients except itself:

[0116]

[0117] Among them, j∈[n] / N(u), is the distance from client u to client j obtained by the k+1th aggregation, is the distance from client i to client j in the kth aggregation, is the distance from client u to client j obtained in the kth aggregation, [n] represents the set {1,2,…,n}, and N(u) represents the set of neighboring clients of client u.

[0118] Exemplarily, in the polymerization, the number of polymerizations k=0.

[0119] For example, the client obtains the minimum distance from the distance vector collection as follows: Client u knows that the distance from its neighbor i to client j is Then its distance to client j can be represented by the minimum distance from neighbors to client j + 1, that is, At the same time, add this distance to the previous distance to client j Compare and update the minimum value.

[0120] In the embodiment of the present invention, the client adds a noise value to the distance vector and distributes it to neighbor clients. The security of each client in the social network graph can be improved by adding the noise value, thereby enhancing privacy security.

[0121] The distance query result acquisition module 103 is used to repeatedly add noise to the distance vector of the client and distribute it to neighboring clients and perform aggregation operations to update the distance vector of each client until a repetition stop condition is met to obtain a distance query result.

[0122] In an embodiment of the present invention, the repeated stopping condition may be that the distance queried between each client under the distance threshold is the shortest distance. The repeated stopping condition may also be the number of iterations. The number of iterations refers to the number of queries corresponding to the distance threshold minus 1, that is, minus the 0th query. The number of iterations is expressed as T-1.

[0123] like Figure 3 , is a schematic diagram of the structure of an electronic device for implementing a distance query method based on local differential privacy provided by an embodiment of the present invention.

[0124] The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a distance query method program based on local differential privacy.

[0125] Among them, the processor 10 can be composed of an integrated circuit in some embodiments, for example, it can be composed of a single packaged integrated circuit, or it can be composed of multiple integrated circuits with the same function or different functions, including one or more central processing units (CPU), microprocessors, digital processing chips, graphics processors and various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, which uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, executing a distance query method program based on local differential privacy, etc.), and calls the data stored in the memory 11 to execute various functions of the electronic device and process data.

[0126] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can be used not only to store application software and various types of data installed in the electronic device, such as a code of a distance query method program based on local differential privacy, but also to temporarily store data that has been output or is to be output.

[0127] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0128] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0129] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0130] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0131] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0132] The distance query method program based on local differential privacy stored in the memory 11 of the electronic device is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0133] Obtain a social network graph and initialize a distance vector of each client in the social network graph;

[0134] The client adds a noise value to the distance vector and distributes it to neighboring clients, and updates the distance vector of each client after performing an aggregation operation;

[0135] Repeat the steps of adding noise to the distance vector of the client and then distributing it to neighboring clients and performing aggregation operations to update the distance vector of each client until a repetition stop condition is met to obtain a distance query result.

[0136] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0137] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0138] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0139] Obtain a social network graph and initialize a distance vector of each client in the social network graph;

[0140] The client adds a noise value to the distance vector and distributes it to neighboring clients, and updates the distance vector of each client after performing an aggregation operation;

[0141] Repeat the steps of adding noise to the distance vector of the client and then distributing it to neighboring clients and performing aggregation operations to update the distance vector of each client until a repetition stop condition is met to obtain a distance query result.

[0142] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0143] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0144] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0145] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0146] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0147] The blockchain referred to in this invention is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0148] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0149] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A distance query method based on local differential privacy, characterized in that: The method comprises: Obtain a social network graph and initialize a distance vector of each client in the social network graph; The client adds a noise value to the distance vector and distributes it to neighboring clients, and updates the distance vector of each client after performing an aggregation operation; Repeat the steps of adding noise to the distance vector of the client and then distributing it to neighboring clients and performing aggregation operations to update the distance vector of each client until a repetition stop condition is met to obtain a distance query result.

2. The distance query method based on local differential privacy as claimed in claim 1, characterized in that: Before initializing the distance vector of each client in the social network graph, the method further includes: Each client presets distance thresholds and privacy parameters.

3. The distance query method based on local differential privacy as claimed in claim 1, characterized in that: The client adds a noise value to the distance vector and distributes it to neighbor clients, including: A noise value is obtained by a probability distribution sampling function, and the noise value is added to the distance vector.

4. The distance query method based on local differential privacy as claimed in claim 3, characterized in that: The obtaining of the noise value by using the probability distribution sampling function includes: The noise value is obtained using the following probability distribution sampling function: Among them, x is the noise value and b is the scale parameter.

5. The distance query method based on local differential privacy as claimed in claim 3, characterized in that: The adding the noise value to the distance vector comprises: The noise value is added to the distance vector using the following formula: in, is the distance vector of client u after adding the noise value, is the initial distance vector of client u, ∈ is the privacy parameter, T is the distance threshold, and X is the independent random variable parameter.

6. The distance query method based on local differential privacy as claimed in claim 1, characterized in that: The distance vector of each client is updated after the aggregation operation, including: Each client traverses the clients other than itself, and uses a preset aggregation formula to calculate the distance vector between the client and the clients other than itself to obtain the target distance vector; The target distance vector is used to update the distance vector between each client and the clients other than itself, and the distance vector collection between each client and the clients other than itself is obtained by summarizing.

7. The distance query method based on local differential privacy as claimed in claim 6, characterized in that: The method of calculating the distance vector between the client and other clients except the client by using a preset aggregation formula includes: The following aggregation formula is used to calculate the distance between a client and other clients except itself: Among them, j∈[n] / N(u), is the distance from client u to client j obtained by the k+1th aggregation, is the distance from client i to client j in the kth aggregation, is the distance from client u to client j obtained in the kth aggregation, [n] represents the set {1,2,…,n}, and N(u) represents the set of neighboring clients of client u.

8. A distance query device based on local differential privacy, characterized in that: The device can implement the distance query method based on local differential privacy as described in any one of claims 1 to 7, and the device includes: A distance vector initialization module, used to obtain a social network graph and initialize the distance vector of each client in the social network graph; A distance vector aggregation module, which is used for the client to add a noise value to the distance vector and distribute it to neighboring clients, and to update the distance vector of each client after performing an aggregation operation; The distance query result acquisition module is used to repeatedly add noise to the distance vector of the client and distribute it to neighboring clients and perform aggregation operations to update the distance vector of each client until a repetition stop condition is reached to obtain the distance query result.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the distance query method based on local differential privacy as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the distance query method based on local differential privacy as described in any one of claims 1 to 7 is implemented.

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