Social graph synthesis method, device, equipment and medium based on local differential privacy

Abstract

The present invention relates to privacy protection technology, and discloses a social graph synthesis method based on local differential privacy, including: obtaining the degree information of each node in the social network graph, and sending the noisy degree information to the server after adding noise to the degree information; the server receives the noisy degree information, calculates the density estimation value of the noisy degree information for the social network graph, and sends the density estimation value to each node; each node obtains the privacy probability of each node, and scrambles the neighbor list of each node according to the privacy probability to obtain a scrambled neighbor list; the server aggregates the scrambled neighbor list, and generates an adjacency matrix of the scrambled neighbor list, and generates a social synthesis graph according to the edge information in the adjacency matrix and the scrambled neighbor list. The present invention also proposes a social graph synthesis device, equipment and medium based on local differential privacy. The present invention can improve the security and accuracy of social graph synthesis based on local differential privacy.

Description

Technical Field

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

[0002] Differential privacy is a powerful privacy protection technology that introduces a certain amount of randomness into the published data to prevent attackers from recovering any personal information from the published data. Local Differential Privacy (LDP) is a special form of differential privacy. Its core is to place the introduction of randomness on the user side rather than on the server side where the data is collected. This processing method means that each user's data has undergone a certain degree of "disturbance" before leaving their device, thereby increasing the security of the data during transmission and use, and effectively reducing the need for trust in the central server. In the local differential privacy model, individuals fully control the privacy protection measures of their own data without relying on the privacy protection of the central server during data processing. This approach is particularly suitable for scenarios where sensitive information is frequently collected.

[0003] In the network graph of a social network, nodes usually represent individuals, while edges represent relationships (relatives, classmates, colleagues, alumni, etc.) or interactions (likes, chats, phone calls, etc.) between individuals. If two individuals have a relationship or interaction, there is an edge between the corresponding nodes of the two individuals. Otherwise, there is no edge. These graph data often contain a lot of sensitive information, such as personal social relationships, communication behaviors, and community affiliations. Using local differential privacy technology to process these graph data can analyze the graph structure and characteristics without leaking user privacy, and extract valuable information while protecting user privacy.

[0004] The release of synthetic graphs provides an efficient way to handle sensitive social graph data. By generating a synthetic graph that is statistically similar to the original social graph but does not contain any real user data, researchers and data scientists can perform graph analysis and data mining without worrying about personal information leakage. This method is particularly suitable for fields such as social network analysis. Another important advantage of synthetic graphs is that they can help various organizations comply with strict data protection regulations such as the EU General Data Protection Regulation (GDPR). In addition, synthetic data can also be used for data sharing and external collaboration. Information can be shared with other organizations or researchers without exposing the original data. However, current synthetic graph technology often sacrifices the practicality of data while enhancing privacy protection. For example, excessive noise addition causes the generated social synthetic graph data to deviate greatly from the original social graph data in structure and characteristics, making the expression accuracy of the original social graph data converted into social synthetic graph data low. Summary of the invention

[0005] The present invention provides a social graph synthesis method, device, equipment and medium based on local differential privacy, which can improve the security and accuracy of social graph synthesis based on local differential privacy.

[0006] To achieve the above object, the present invention provides a social graph synthesis method based on local differential privacy, comprising:

[0007] Obtaining degree information of each node in the social network graph, adding noise to the degree information to obtain noisy degree information, and then sending the noisy degree information to the server;

[0008] The server receives the noise information, calculates a density estimation value of the noise information for the social network graph, and sends the density estimation value to each node;

[0009] Each node obtains a privacy probability of each node, and scrambles a neighbor list of each node according to the privacy probability to obtain a scrambled neighbor list;

[0010] The server aggregates the scrambled neighbor lists and generates an adjacency matrix of the scrambled neighbor lists, and generates a social composite graph according to the edge information in the adjacency matrix and the scrambled neighbor lists.

[0011] Optionally, the step of adding noise to the degree information to obtain the noisy degree information includes:

[0012] Obtain the number of edges connected to each node in the social network graph to obtain degree information;

[0013] Calculate the noise value using probability distribution sampling;

[0014] The noise value is added to the degree information to obtain the noise-added degree information.

[0015] Optionally, scrambling the neighbor list of each node according to the privacy probability to obtain a scrambled neighbor list includes:

[0016] A perturbation function is generated according to the privacy probability, and the neighbor list is scrambled according to the perturbation function to obtain the scrambled neighbor list.

[0017] Optionally, the neighbor list is scrambled according to the perturbation function using the following formula:

[0018]

[0019] in, is the scrambled neighbor list, N i is the neighbor list of the ith node, R(·) is the perturbation function, Ni [1] indicates N i The connection relationship between the i-th node and the first node in N i [1] = 1, which means there is an edge connection between node i and node 1.

[0020] Optionally, the server aggregates the scrambled neighbor list and generates an adjacency matrix of the scrambled neighbor list, including:

[0021] The server defines a Bernoulli random variable under preset parameters, and generates a server sampling parameter according to the Bernoulli random variable;

[0022] The adjacency matrix is ​​generated according to the server sampling parameters and the scrambled neighbor list.

[0023] Optionally, generating the adjacency matrix according to the server sampling parameter and the scrambled neighbor list includes:

[0024] Start traversing from node i=1. After selecting node i=1, start traversing from the jth node j=i+1 in the scrambled neighbor list corresponding to the node i=1 until node j>n, then stop traversing;

[0025] Generate the adjacency matrix using the server sampling parameter b generated by each traversal

[0026] The traversal operation ends when node i>n;

[0027] Among them, when b = 1, the adjacency matrix When b≠1, the adjacency matrix

[0028] Optionally, the server defines a Bernoulli random variable under preset parameters, including:

[0029] The preset parameter α is generated using the following formula:

[0030]

[0031] in, is the density estimate, p is the privacy probability, where is the noise information of node i, and n is the number of nodes.

[0032] In order to solve the above problems, the present invention also provides a social graph synthesis device based on local differential privacy, the device comprising:

[0033] A degree information acquisition module, used to acquire the degree information of each node in the social network graph, add noise to the degree information to obtain the noisy degree information, and then send the noisy degree information to the server;

[0034] A scrambled neighbor list acquisition module is used for the server to receive the noise information, calculate the density estimation value of the noise information for the social network graph, and send the density estimation value to each node; each node obtains the privacy probability of each node, and scrambles the neighbor list of each node according to the privacy probability to obtain a scrambled neighbor list;

[0035] A social composite graph generation module is used for the server to aggregate the scrambled neighbor list and generate an adjacency matrix of the scrambled neighbor list, and to generate a social composite graph according to the edge information in the adjacency matrix and the scrambled neighbor list.

[0036] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0037] at least one processor; and,

[0038] a memory communicatively connected to the at least one processor; wherein,

[0039] 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 perform the above-mentioned social graph synthesis method based on local differential privacy.

[0040] In order to solve the above problems, 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 social graph synthesis method based on local differential privacy.

[0041] The present invention adds noise to the degree information to obtain the noisy degree information, which can blur the social behaviors of each node connected by the degree information, thereby protecting the user's privacy from being easily identified and preventing attackers from inferring sensitive information by analyzing the social network graph. In addition, by calculating the density estimation value of the noisy degree information for the social network graph, it can be ensured that the generated social composite graph has a graph density similar to the original social network graph and preserves the degree information in the graph as much as possible. In addition, by scrambling the neighbor list of each node according to the privacy probability, the privacy protection of each node and the neighbor list can be achieved, and the risk of privacy leakage in the social network graph can be reduced. It can ensure that malicious attackers cannot determine whether each edge in the social composite graph actually exists in the original social graph, and further ensure the privacy security of each node. In addition, by generating a social composite graph based on the edge information in the adjacency matrix and the scrambled neighbor list, it can be ensured that the generated composite graph data has a high similarity with the original data in structure and characteristics, and the expression accuracy of the original data converted into composite graph data is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of a process of a social graph synthesis method based on local differential privacy provided by an embodiment of the present invention;

[0043] Figure 2 A functional module diagram of a social graph synthesis device based on local differential privacy provided by an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of the structure of an electronic device for implementing a social graph synthesis method based on local differential privacy provided by an embodiment of the present invention.

[0045] 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

[0046] 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.

[0047] The embodiment of the present application provides a social graph synthesis method based on local differential privacy. The execution subject of the social graph synthesis 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 social graph synthesis 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.

[0048] Reference Figure 1 As shown, it is a flow chart of a social graph synthesis method based on local differential privacy provided by an embodiment of the present invention. In this embodiment, the social graph synthesis method based on local differential privacy includes:

[0049] S1. Obtain the degree information of each node in the social network graph, add noise to the degree information to obtain noisy degree information, and then send the noisy degree information to the server.

[0050] In the embodiment of the present invention, the social network graph is a graphical representation method for representing the relationship between individuals in a social network, and is usually composed of nodes and edges. The nodes represent individuals or entities in the social network, such as people, organizations or communities, and the edges represent social relationships between nodes, such as friendship, cooperation, communication, etc.

[0051] As an embodiment of the present invention, the step of adding noise to the degree information to obtain the noisy degree information includes:

[0052] Obtaining the number of connecting edges of each node in the social network graph to obtain degree information;

[0053] Calculate the noise value using probability distribution sampling;

[0054] The noise value is added to the degree information to obtain the noise-added degree information.

[0055] Further, the calculating of the noise value by using probability distribution sampling includes:

[0056] The noise value is calculated using the following formula:

[0057]

[0058] Wherein, Lap(·) is the Laplace operator, x is the noise value, b is the scaling function, b=1 / ∈1, ∈1 is the first privacy parameter of each node.

[0059] In the embodiment of the present invention, the noise value is added to the degree information using the following formula:

[0060] Among them, d i For degree information, is the added noise information, and x is the noise value.

[0061] The embodiment of the present invention adds noise to the degree information to obtain noisy degree information, which can blur the social behavior of each node connected by the degree information, thereby protecting the user's privacy from being easily identified and preventing attackers from inferring sensitive information by analyzing the social network graph.

[0062] S2. The server receives the noise information, calculates a density estimation value of the noise information for the social network graph, and sends the density estimation value to each node.

[0063] In the embodiment of the present invention, the server refers to a third-party data aggregation and processing unit, which is responsible for aggregating data from each node and performing tasks such as calculation and analysis.

[0064] In the embodiment of the present invention, the density estimation value of the noise information for the social network graph is calculated. Use the following formula:

[0065]

[0066] in, is the noise information, and n is the number of nodes in the social network graph.

[0067] S3. Each node obtains the privacy probability of each node, and scrambles the neighbor list of each node according to the privacy probability to obtain a scrambled neighbor list.

[0068] In the embodiment of the present invention, the privacy probability is generated by a second privacy parameter ∈2 of each node.

[0069] In the embodiment of the present invention, the privacy probability is expressed by the following formula: Among them, ∈2 is the second privacy parameter, and in addition,

[0070] As an embodiment of the present invention, scrambling the neighbor list of each node according to the privacy probability to obtain a scrambled neighbor list includes:

[0071] A perturbation function is generated according to the privacy probability, and the neighbor list is scrambled according to the perturbation function to obtain the scrambled neighbor list.

[0072] In the embodiment of the present invention, the disturbance function adopts the following formula:

[0073]

[0074] Among them, b i is the input event, 1-b i To remove other events generated after the input event.

[0075] Furthermore, the neighbor list is scrambled according to the perturbation function using the following formula:

[0076]

[0077] in, is the scrambled neighbor list, N i is the neighbor list of the ith node, R(·) is the perturbation function, N i [1] indicates N i The connection relationship between the i-th node and the first node in N i [1] = 1, which means there is an edge connection between node i and node 1.

[0078] The embodiment of the present invention scrambles the neighbor list of each node according to the privacy probability, so as to achieve privacy protection for each node and the neighbor list, reduce the risk of privacy leakage in the social network graph, and ensure that malicious attackers cannot determine whether each edge in the composite graph actually exists in the original graph.

[0079] S4. The server aggregates the scrambled neighbor list and generates an adjacency matrix of the scrambled neighbor list, and generates a social composite graph according to the edge information in the adjacency matrix and the scrambled neighbor list.

[0080] In the embodiment of the present invention, the adjacency matrix is ​​a data structure used to represent a graph of several nodes, usually represented by a matrix.

[0081] As an embodiment of the present invention, the server aggregates the scrambled neighbor list and generates an adjacency matrix of the scrambled neighbor list, including:

[0082] The server defines a Bernoulli random variable under preset parameters, and generates a server sampling parameter according to the Bernoulli random variable;

[0083] The adjacency matrix is ​​generated according to the server sampling parameters and the scrambled neighbor list.

[0084] Furthermore, the server defines a Bernoulli random variable under preset parameters, including:

[0085] The preset parameter α is generated using the following formula:

[0086]

[0087] in, is the density estimate, p is the privacy probability, where is the noise information, and n is the number of nodes.

[0088] Exemplarily, the generating the adjacency matrix of the scrambled neighbor list adopts the following steps:

[0089] Start traversing from node i=1. After selecting node i=1, start traversing from the jth node j=i+1 in the scrambled neighbor list corresponding to the node i=1 until node j>n, then stop traversing;

[0090] Generate the adjacency matrix using the server sampling parameter b generated by each traversal

[0091] The traversal operation ends when node i>n;

[0092] Among them, when b = 1, the adjacency matrix When b≠1, the adjacency matrix

[0093] In the embodiment of the present invention, the ∩ refers to the AND operation in the bitwise operator. When the numbers on both sides of the symbol are 1, =1; ∪ refers to the bitwise OR operation. When either side of the number on either side of the symbol is 1, is equal to 1.

[0094] The embodiment of the present invention adds noise to the degree information to obtain the noisy degree information, which can blur the social behaviors of each node connected by the degree information, thereby protecting the user's privacy from being easily identified and preventing attackers from inferring sensitive information by analyzing the social network graph. In addition, by calculating the density estimation value of the noisy degree information for the social network graph, it can be ensured that the generated social composite graph has a graph density similar to the original social network graph and preserves the degree information in the graph as much as possible. In addition, by scrambling the neighbor list of each node according to the privacy probability, the privacy protection of each node and the neighbor list can be achieved, and the risk of privacy leakage in the social network graph can be reduced. It can ensure that malicious attackers cannot determine whether each edge in the social composite graph actually exists in the original social graph, and further ensure the privacy security of each node. In addition, by generating a social composite graph based on the edge information in the adjacency matrix and the scrambled neighbor list, it can be ensured that the generated composite graph data has a high similarity with the original data in structure and characteristics, and the expression accuracy of the original data converted into composite graph data is improved.

[0095] like Figure 2 , is a functional module diagram of a social graph synthesis device based on local differential privacy provided by an embodiment of the present invention.

[0096] The social graph synthesis device 100 based on local differential privacy of the present invention can be installed in an electronic device. According to the implemented functions, the social graph synthesis device 100 based on local differential privacy can include a degree information acquisition module 101, a scrambled neighbor list acquisition module 102 and a social synthesis graph generation module 103.

[0097] 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.

[0098] In this embodiment, the functions of each module / unit are as follows:

[0099] The degree information acquisition module 101 is used to acquire the degree information of each node in the social network graph, add noise to the degree information to obtain the noisy degree information, and then send the noisy degree information to the server.

[0100] In the embodiment of the present invention, the social network graph is a graphical representation method for representing the relationship between individuals in a social network, and is usually composed of nodes and edges. The nodes represent individuals or entities in the social network, such as people, organizations or communities, and the edges represent social relationships between nodes, such as friendship, cooperation, communication, etc.

[0101] As an embodiment of the present invention, the step of adding noise to the degree information to obtain the noisy degree information includes:

[0102] Obtaining the number of connecting edges of each node in the social network graph to obtain degree information;

[0103] Calculate the noise value using probability distribution sampling;

[0104] The noise value is added to the degree information to obtain the noise-added degree information.

[0105] Further, the calculating of the noise value by using probability distribution sampling includes:

[0106] The noise value is calculated using the following formula:

[0107]

[0108] Wherein, Lap(·) is the Laplace operator, x is the noise value, b is the scaling function, b=1 / ∈1, ∈1 is the first privacy parameter of each node.

[0109] In the embodiment of the present invention, the noise value is added to the degree information using the following formula:

[0110] Among them, d i For degree information, is the added noise information, and x is the noise value.

[0111] The embodiment of the present invention adds noise to the degree information to obtain noisy degree information, which can blur the social behavior of each node connected by the degree information, thereby protecting the user's privacy from being easily identified and preventing attackers from inferring sensitive information by analyzing the social network graph.

[0112] The scrambled neighbor list acquisition module 102 is used for the server to receive the noise information, calculate the density estimation value of the noise information for the social network graph, and send the density estimation value to each node; each node obtains the privacy probability of each node, and scrambles the neighbor list of each node according to the privacy probability to obtain a scrambled neighbor list.

[0113] In the embodiment of the present invention, the server refers to a third-party data aggregation and processing unit, which is responsible for aggregating data from each node and performing tasks such as calculation and analysis.

[0114] In the embodiment of the present invention, the density estimation value of the noise information for the social network graph is calculated. Use the following formula:

[0115]

[0116] in, is the noise information, and n is the number of nodes in the social network graph.

[0117] In the embodiment of the present invention, the privacy probability is generated by a second privacy parameter ∈2 of each node.

[0118] In the embodiment of the present invention, the privacy probability is expressed by the following formula: Among them, ∈2 is the second privacy parameter, and in addition,

[0119] As an embodiment of the present invention, scrambling the neighbor list of each node according to the privacy probability to obtain a scrambled neighbor list includes:

[0120] A perturbation function is generated according to the privacy probability, and the neighbor list is scrambled according to the perturbation function to obtain the scrambled neighbor list.

[0121] In the embodiment of the present invention, the disturbance function adopts the following formula:

[0122]

[0123] Among them, b i is the input event, 1-b i To remove other events generated after the input event.

[0124] Furthermore, the neighbor list is scrambled according to the perturbation function using the following formula:

[0125]

[0126] in, is the scrambled neighbor list, N i is the neighbor list of the ith node, R(·) is the perturbation function, N i [1] indicates N i The connection relationship between the i-th node and the first node in N i [1] = 1, which means there is an edge connection between node i and node 1.

[0127] Furthermore, the neighbor list is scrambled according to the perturbation function using the following formula:

[0128]

[0129] in, is the scrambled neighbor list, N i is the neighbor list of the ith node, N i [1] represents the connection relationship between the i-th node and the first node, N i [1] = 1, which means there is an edge connection between node i and node 1.

[0130] The embodiment of the present invention scrambles the neighbor list of each node according to the privacy probability, so as to achieve privacy protection for each node and the neighbor list, reduce the risk of privacy leakage in the social network graph, and ensure that malicious attackers cannot determine whether each edge in the composite graph actually exists in the original graph.

[0131] The social composite graph generation module 103 is used for the server to aggregate the scrambled neighbor list and generate an adjacency matrix of the scrambled neighbor list, and generate a social composite graph according to the edge information in the adjacency matrix and the scrambled neighbor list.

[0132] In the embodiment of the present invention, the adjacency matrix is ​​a data structure used to represent a graph of several nodes, usually represented by a matrix.

[0133] As an embodiment of the present invention, the server aggregates the scrambled neighbor list and generates an adjacency matrix of the scrambled neighbor list, including:

[0134] The server defines a Bernoulli random variable under preset parameters, and generates a server sampling parameter according to the Bernoulli random variable;

[0135] The adjacency matrix is ​​generated according to the server sampling parameters and the scrambled neighbor list.

[0136] Furthermore, the server defines a Bernoulli random variable under preset parameters, including:

[0137] The preset parameter α is generated using the following formula:

[0138]

[0139] in, is the density estimate, p is the privacy probability, where is the noise information of node i, and n is the number of nodes.

[0140] Exemplarily, the generating the adjacency matrix of the scrambled neighbor list adopts the following steps:

[0141] Start traversing from node i=1. After selecting node i=1, start traversing from the jth node j=i+1 in the scrambled neighbor list corresponding to the node i=1 until node j>n, then stop traversing;

[0142] Generate the adjacency matrix using the server sampling parameter b generated by each traversal

[0143] The traversal operation ends when node i>n;

[0144] Among them, when b = 1, the adjacency matrix When b≠1, the adjacency matrix

[0145] In the embodiment of the present invention, the ∩ refers to the AND operation in the bitwise operator. When the numbers on both sides of the symbol are 1, =1; ∪ refers to the bitwise OR operation. When either side of the number on either side of the symbol is 1, is equal to 1.

[0146] like Figure 3 , is a schematic diagram of the structure of an electronic device for implementing a social graph synthesis method based on local differential privacy provided by an embodiment of the present invention.

[0147] 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 social graph synthesis method program based on local differential privacy.

[0148] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and 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 social graph synthesis method program based on local differential privacy, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device and process data.

[0149] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, 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 not only be used to store application software and various types of data installed in the electronic device, such as the code of a social graph synthesis method program based on local differential privacy, but also can be used to temporarily store data that has been output or is to be output.

[0150] 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.

[0151] 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.

[0152] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3The 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.

[0153] 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.

[0154] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0155] The program of a social graph synthesis method 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:

[0156] Obtaining degree information of each node in the social network graph, adding noise to the degree information to obtain noisy degree information, and then sending the noisy degree information to the server;

[0157] The server receives the noise information, calculates a density estimation value of the noise information for the social network graph, and sends the density estimation value to each node;

[0158] Each node obtains a privacy probability of each node, and scrambles a neighbor list of each node according to the privacy probability to obtain a scrambled neighbor list;

[0159] The server aggregates the scrambled neighbor lists and generates an adjacency matrix of the scrambled neighbor lists, and generates a social composite graph according to the edge information in the adjacency matrix and the scrambled neighbor lists.

[0160] 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.

[0161] 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).

[0162] 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:

[0163] Obtaining degree information of each node in the social network graph, adding noise to the degree information to obtain noisy degree information, and then sending the noisy degree information to the server;

[0164] The server receives the noise information, calculates a density estimation value of the noise information for the social network graph, and sends the density estimation value to each node;

[0165] Each node obtains a privacy probability of each node, and scrambles a neighbor list of each node according to the privacy probability to obtain a scrambled neighbor list;

[0166] The server aggregates the scrambled neighbor lists and generates an adjacency matrix of the scrambled neighbor lists, and generates a social composite graph according to the edge information in the adjacency matrix and the scrambled neighbor lists.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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 social graph synthesis method based on local differential privacy, characterized in that: The method comprises: Obtaining degree information of each node in the social network graph, adding noise to the degree information to obtain noisy degree information, and then sending the noisy degree information to the server; The server receives the noise information, calculates a density estimation value of the noise information for the social network graph, and sends the density estimation value to each node; Each node obtains a privacy probability of each node, and scrambles a neighbor list of each node according to the privacy probability to obtain a scrambled neighbor list; The server aggregates the scrambled neighbor list and generates an adjacency matrix of the scrambled neighbor list, wherein the server aggregates the scrambled neighbor list and generates the adjacency matrix of the scrambled neighbor list, including: the server defines a Bernoulli random variable under preset parameters, and generates a server sampling parameter according to the Bernoulli random variable; generates the adjacency matrix according to the server sampling parameter and the scrambled neighbor list, wherein the server defines a Bernoulli random variable under preset parameters, including: the preset parameter α is generated using the following formula: in, is the density estimate, p is the privacy probability, where is the noise degree information of node i, and n is the number of nodes; wherein, the generating the adjacency matrix according to the server sampling parameter and the scrambled neighbor list includes: starting from node i=1, after selecting node i=1, starting from the jth node j=i+1 in the scrambled neighbor list corresponding to the node i=1, until node j>n, then stopping the traversal; generating the adjacency matrix using the server sampling parameter b generated in each traversal The traversal operation ends when node i>n; when b=1, the adjacency matrix When b≠1, the adjacency matrix A social composite graph is generated according to the edge information in the adjacency matrix and the scrambled neighbor list.

2. The social graph synthesis method based on local differential privacy as claimed in claim 1, characterized in that: The step of adding noise to the degree information to obtain the noisy degree information comprises: Obtain the number of edges connected to each node in the social network graph to obtain degree information; Calculate the noise value using probability distribution sampling; The noise value is added to the degree information to obtain the noise-added degree information.

3. The social graph synthesis method based on local differential privacy as claimed in claim 1, characterized in that: The step of scrambling the neighbor list of each node according to the privacy probability to obtain a scrambled neighbor list includes: A perturbation function is generated according to the privacy probability, and the neighbor list is scrambled according to the perturbation function to obtain the scrambled neighbor list.

4. The social graph synthesis method based on local differential privacy as claimed in claim 3, characterized in that: The neighbor list is scrambled according to the perturbation function using the following formula: in, is the scrambled neighbor list, N i is the neighbor list of the ith node, R(·) is the perturbation function, N i [1] indicates N i The connection relationship between the i-th node and the first node in N i [1] = 1, which means there is an edge connection between node i and node 1.

5. A social graph synthesis device based on local differential privacy, characterized in that: The device can implement the social graph synthesis method based on local differential privacy as described in any one of claims 1 to 4, and the device includes: A degree information acquisition module, used to acquire the degree information of each node in the social network graph, add noise to the degree information to obtain the noisy degree information, and then send the noisy degree information to the server; A scrambled neighbor list acquisition module is used for the server to receive the noise information, calculate the density estimation value of the noise information for the social network graph, and send the density estimation value to each node; each node obtains the privacy probability of each node, and scrambles the neighbor list of each node according to the privacy probability to obtain a scrambled neighbor list; A social composite graph generation module is used for the server to aggregate the scrambled neighbor list and generate an adjacency matrix of the scrambled neighbor list, wherein the server aggregates the scrambled neighbor list and generates the adjacency matrix of the scrambled neighbor list, including: the server defines a Bernoulli random variable under preset parameters, and generates a server sampling parameter according to the Bernoulli random variable; generates the adjacency matrix according to the server sampling parameter and the scrambled neighbor list, wherein the server defines a Bernoulli random variable under preset parameters, including: the preset parameter α is generated using the following formula: in, is the density estimate, p is the privacy probability, where is the noise degree information of node i, and n is the number of nodes; wherein, the generating the adjacency matrix according to the server sampling parameter and the scrambled neighbor list includes: starting from node i=1, after selecting node i=1, starting from the jth node j=i+1 in the scrambled neighbor list corresponding to the node i=1, until node j>n, then stopping the traversal; generating the adjacency matrix using the server sampling parameter b generated in each traversal The traversal operation ends when node i>n; when b=1, the adjacency matrix When b≠1, the adjacency matrix A social composite graph is generated according to the edge information in the adjacency matrix and the scrambled neighbor list.

6. 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 social graph synthesis method based on local differential privacy as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the social graph synthesis method based on local differential privacy as described in any one of claims 1 to 4 is implemented.

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