Private computing node reputation evaluation method and device, storage medium and program product

Abstract

The invention provides a privacy computing node reputation evaluation method and device, a storage medium and a program product, and relates to the technical field of communication, and the method comprises the steps: obtaining an attribute reputation score, an interaction reputation score, a historical reputation score and a prediction reputation score of a to-be-evaluated privacy computing node; and based on the attribute reputation score, the interaction reputation score, the historical reputation score and the predicted reputation score, determining a comprehensive reputation score of the privacy computing node to be evaluated. The method is suitable for a private computing node reputation evaluation process and is used for providing a private computing node reputation evaluation scheme.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a privacy computing node reputation evaluation method, device, storage medium and program product. Background Art

[0002] Privacy computing specifically refers to the implementation of data analysis and calculation on the privacy information involved while processing video, audio, images, graphics, text, numbers, ubiquitous network behavioral information flows, etc., under the premise of protecting the privacy information from external disclosure.

[0003] Privacy computing nodes inevitably need to access private information during privacy computing. If anomalies occur in privacy computing nodes, it may lead to the leakage of private information. If the reputation of privacy computing nodes can be evaluated, the possibility of anomalies in privacy computing nodes can be estimated.

[0004] However, there is currently a lack of reputation evaluation methods for privacy computing nodes. Summary of the invention

[0005] The present application provides a privacy computing node reputation evaluation method, device, storage medium and program product, which can provide a more comprehensive privacy computing node reputation evaluation method.

[0006] In a first aspect, the present application provides a privacy computing node reputation evaluation method, the method comprising: obtaining an attribute reputation score, an interactive reputation score, a historical reputation score, and a predicted reputation score of a privacy computing node to be evaluated;

[0007] Among them, the attribute reputation score is used to evaluate the number of types of provable attribute information provided by the privacy computing node; the provable attribute information is determined based on the attribute information directly provided by the privacy computing node to be evaluated and the attribute information of the privacy computing node to be evaluated provided by the trusted information source; the interactive reputation score is used to evaluate the comprehensive reputation score of the interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated; the historical reputation score is used to evaluate the historical comprehensive reputation score of the privacy computing node to be evaluated; the predicted reputation score is used to evaluate the behavioral characteristics of the privacy computing node to be evaluated;

[0008] Based on the attribute reputation score, interaction reputation score, historical reputation score and predicted reputation score, the comprehensive reputation score of the privacy computing node to be evaluated is determined.

[0009] The privacy computing node reputation evaluation method provided in this application can obtain the attribute reputation score, interaction reputation score, historical reputation score and predicted reputation score of the privacy computing node to be evaluated, and determine the comprehensive reputation score of the privacy computing node to be evaluated based on the attribute reputation score, interaction reputation score, historical reputation score and predicted reputation score. It can comprehensively consider the reputation scores of multiple dimensions, thereby providing a more comprehensive and accurate reputation evaluation method to ensure the credibility of privacy computing nodes in heterogeneous environments.

[0010] Optionally, the trusted information source includes the operation log of the privacy computing node to be evaluated; obtaining the attribute reputation score of the privacy computing node to be evaluated includes: receiving attribute information sent from the privacy computing node to be evaluated; obtaining the operation log of the privacy computing node to be evaluated; determining provable attribute information from the operation log; wherein the provable attribute information is the attribute information in the operation log that is the same as the attribute information sent by the privacy computing node to be evaluated; determining the attribute reputation score based on the number of types of provable attribute information; the attribute reputation score is positively correlated with the number of types of provable attribute information.

[0011] Optionally, obtaining the interactive reputation score of the privacy computing node to be evaluated includes: performing weighted summation on the comprehensive reputation scores of the interactive privacy computing nodes that have an interactive relationship with the privacy computing node to be evaluated to obtain the interactive reputation score.

[0012] Optionally, the weight corresponding to the interactive privacy computing node is determined based on the reputation weight corresponding to the interactive privacy computing node and the connection weight corresponding to the interactive privacy computing node; the reputation weight is positively correlated with the degree of score deviation; the score deviation degree is the degree of deviation between the comprehensive reputation score of the corresponding interactive privacy computing node and the average reputation score; the average reputation score is the average of the comprehensive reputation scores of all privacy computing nodes in the trusted domain where the privacy computing node to be evaluated is located; the trusted domain is a regional network composed of privacy computing nodes that have issued CA certificates; the connection weight is positively correlated with the number of privacy computing nodes connected to the corresponding interactive privacy computing node.

[0013] Optionally, the weight corresponding to the interactive privacy computing node is calculated by the following formula:

[0014] Weight i =(Reputation Weight i + Connection Weight i ) / 2;

[0015] Among them, the weight i Represents the weight corresponding to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated; reputation weight i Represents the reputation weight corresponding to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated, and the connection weighti Represents the connection weight corresponding to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated;

[0016]

[0017] Among them, the comprehensive reputation score i Represents the comprehensive reputation score of the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated;

[0018]

[0019] The number of connected nodes i It represents the number of privacy computing nodes connected to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated; the total number of node connections represents the sum of the number of privacy computing nodes connected to each privacy computing node in the trusted domain.

[0020] Optionally, obtaining a historical reputation score of the privacy computing node to be evaluated includes: performing exponential decay on a historical comprehensive reputation score of the privacy computing node to be evaluated to obtain a historical reputation score of the privacy computing node to be evaluated.

[0021] Optionally, the historical comprehensive reputation score of the privacy computing node to be evaluated is exponentially decayed to obtain the historical reputation score of the privacy computing node to be evaluated, including:

[0022] Exponential decay is performed according to the following formula:

[0023]

[0024] Among them, Adjusted Score represents the historical credit score; Score j represents the historical comprehensive reputation score at the time of the jth evaluation; λ represents the decay factor, which is positively correlated with the decay rate; t j It represents the unit time difference between the j-th evaluation and the current time.

[0025] Optionally, obtaining a predicted reputation score of the privacy computing node to be evaluated includes: obtaining behavioral feature data of the privacy computing node to be evaluated; determining the probability of abnormal behavior of the privacy computing node to be evaluated based on the behavioral feature data and a reputation prediction model; the reputation prediction model is used to predict the probability of abnormal behavior based on the behavioral feature data of the privacy computing node; and determining the predicted reputation score of the privacy computing node to be evaluated based on the probability of abnormal behavior of the privacy computing node to be evaluated.

[0026] Optionally, determining the predicted reputation score of the privacy computing node to be evaluated according to the abnormal behavior probability of the privacy computing node to be evaluated includes: calculating the predicted reputation score of the privacy computing node to be evaluated according to the following formula:

[0027] Predicted reputation score = (1-abnormal behavior probability) × the most recent historical comprehensive reputation score of the privacy computing node to be evaluated.

[0028] Optionally, the method also includes: obtaining an initial training sample set; the initial training sample set includes a first proportion of positive samples and a second proportion of unlabeled samples; wherein each positive sample includes behavioral feature data of a privacy computing node and an abnormal behavior probability label of the privacy computing node; the abnormal behavior probability label of the positive sample is higher than a probability threshold; each unlabeled sample includes behavioral feature data of a privacy computing node; the second proportion is higher than the first proportion; using a naive Bayes model to label the unlabeled samples with abnormal behavior probability labels, and classifying the unlabeled samples into positive samples and negative samples; the abnormal behavior probability label of the negative sample is lower than the probability threshold; based on the positive samples in the initial sample set and the positive and negative samples labeled by the naive Bayes model, the initial model is trained to obtain a reputation prediction model.

[0029] Optionally, the initial model is trained based on the positive samples in the initial sample set and the positive samples and negative samples obtained by labeling the naive Bayes model to obtain a reputation prediction model, including: based on the positive samples in the initial sample set and the positive samples and negative samples obtained by labeling the naive Bayes model, the initial model is trained until a training stop condition is reached; based on the verification sample set, the initial model is verified to obtain a prediction result of the initial model; the verification sample set includes multiple positive samples; based on the prediction result of the initial model, a verification score of the initial model is calculated; when the verification score is higher than a score threshold, the initial model is determined as a reputation prediction model.

[0030] Optionally, based on the prediction results of the initial model, a validation score of the initial model is calculated, including:

[0031] The validation score of the initial model is calculated according to the following formula:

[0032]

[0033] Where S represents the validation score; r represents the recall rate; and Pr[f(x)=1] represents the probability that a sample in the validation set is determined to be a positive sample based on the abnormal behavior probability predicted by the initial model.

[0034] Optionally, based on the attribute reputation score, the interaction reputation score, the historical reputation score and the predicted reputation score, the comprehensive reputation score of the privacy computing node to be evaluated is determined, including: taking a weighted sum of the attribute reputation score, the interaction reputation score, the historical reputation score and the predicted reputation score to obtain the comprehensive reputation score of the privacy computing node to be evaluated; wherein the weight corresponding to the attribute reputation score is greater than or equal to the weight corresponding to the predicted reputation score, the weight corresponding to the predicted reputation score is greater than the weight corresponding to the historical reputation score, and the weight corresponding to the historical reputation score is greater than or equal to the weight corresponding to the interaction reputation score.

[0035] In a second aspect, the present application provides a reputation evaluation device, which includes various functional modules used in the method described in the first aspect above.

[0036] In a third aspect, the present application provides a computer program product, which includes: computer instructions; when the computer instructions are executed in an electronic device, the electronic device implements the method described in the first aspect above.

[0037] In a fourth aspect, the present application provides an electronic device, comprising: a processor and a memory; the memory stores instructions executable by the processor; when the processor is configured to execute the instructions, the electronic device implements the method described in the first aspect above.

[0038] In a fifth aspect, the present application provides a computer-readable storage medium, which includes: software instructions; when the software instructions are executed in an electronic device, the electronic device implements the method described in the first aspect above.

[0039] The beneficial effects of the second to fifth aspects mentioned above can be referred to the first aspect and will not be elaborated on again. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A schematic diagram of the composition of the privacy computing node reputation evaluation system provided in an embodiment of the present application;

[0042] Figure 2 A flowchart of a privacy computing node reputation evaluation method provided in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of a reputation evaluation process provided in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of the composition of a reputation evaluation device provided in an embodiment of the present application;

[0045] Figure 5 A schematic diagram of the composition of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] It should be noted that, in the embodiments of the present application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a specific way.

[0048] In order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first", "second", etc. are used to distinguish the same items or similar items with basically the same functions and effects. Those skilled in the art can understand that the words "first", "second", etc. are not limiting the quantity and execution order.

[0049] Privacy computing specifically refers to the implementation of data analysis and calculation on the privacy information involved while processing video, audio, images, graphics, text, numbers, ubiquitous network behavioral information flows, etc., under the premise of protecting the privacy information from external disclosure.

[0050] Privacy computing nodes inevitably need to access private information during privacy computing. If anomalies occur in privacy computing nodes, it may lead to the leakage of private information. If the reputation of privacy computing nodes can be evaluated, the possibility of anomalies in privacy computing nodes can be estimated.

[0051] However, there is currently a lack of reputation evaluation methods for privacy computing nodes.

[0052] Based on this, the embodiments of the present application provide a privacy computing node reputation evaluation method, device, storage medium and program product, which can provide a more comprehensive privacy computing node reputation evaluation method.

[0053] The following is an introduction with reference to the accompanying drawings.

[0054] Figure 1 A schematic diagram of the composition of the privacy computing node reputation evaluation system provided in the embodiment of the present application. Figure 1 As shown, the system includes: a privacy computing node 100 and a central node 200, and there is a communication connection between the privacy computing node 100 and the central node 200.

[0055] The privacy computing node 100 may include multiple privacy computing nodes ( Figure 1 In the figure, three privacy computing nodes, namely privacy computing node A, privacy computing node B and privacy computing node C, are used as an example).

[0056] The privacy computing node 100 can be an electronic device with computing and processing capabilities, such as a computer or a server.

[0057] Among them, the server can be a single server, or it can be a server cluster composed of multiple servers. In some implementations, the server cluster can also be a distributed cluster. Optionally, the server can also be implemented on a cloud platform, for example, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, and a multi-cloud, etc., or any combination thereof. The embodiments of the present application are not limited to this.

[0058] The privacy computing node 100 can be used to perform privacy computing.

[0059] The central node 200 can also be an electronic device with computing and processing functions, such as a computer or a server. For details, please refer to the description of the privacy computing node 100, which will not be repeated here.

[0060] The central node 200 can be used to perform reputation evaluation on the privacy computing node 100. The specific process can be referred to the privacy computing node reputation evaluation method provided in the following method embodiment, and will not be repeated here.

[0061] In some embodiments, the central node 200 can also be used to receive trusted domain joining application information sent by the privacy computing node 100, which may include the identity information of the privacy computing node and attribute information specific to the privacy computing architecture (such as the deployed privacy computing framework, version, deployment service, and data service directory that can be provided to the outside world, etc.).

[0062] Among them, the trusted domain can be understood as a regional network composed of privacy computing nodes that have been issued a Certificate Authority (CA) certificate by the central node 200 (certificate authority).

[0063] In some embodiments, the central node 200 can also be used to respond to the trusted domain joining application information, generate an identity mapping ID according to the hash algorithm based on the identity information of the privacy computing node and the attribute information of the privacy computing architecture, and generate a CA certificate, and send the identity mapping ID and CA certificate to the privacy computing node 100. The identity mapping ID and the CA integer issued by the central node are the basis for the privacy computing node 100 to perform interconnection tasks within the trusted domain, such as data interaction and joint modeling.

[0064] The executor of the privacy computing node reputation evaluation method provided in the embodiment of the present application is a reputation evaluation device. The reputation evaluation device can be the above-mentioned central node 200. For example, as mentioned above, the above-mentioned central node 200 can be an electronic device with computing and processing functions such as a computer or a server. Optionally, the reputation evaluation device can also be a processor (such as a central processing unit (CPU)) in the aforementioned electronic device; or, the reputation evaluation device can also be an application with a reputation evaluation function installed in the aforementioned electronic device; or, the reputation evaluation device can also be a software system or platform in the aforementioned electronic device; or, the reputation evaluation device can also be a functional module in the aforementioned electronic device for executing the privacy computing node reputation evaluation method, etc. The embodiment of the present application does not limit this.

[0065] For the sake of simplicity, the following will take the execution subject of the privacy computing node reputation evaluation method provided in the embodiment of the present application as an example of a reputation evaluation device.

[0066] Figure 2 A flowchart of a privacy computing node reputation evaluation method provided in an embodiment of the present application. Figure 2 As shown, the method includes the following steps:

[0067] S101. Obtain the attribute reputation score, interaction reputation score, historical reputation score, and predicted reputation score of the privacy computing node to be evaluated.

[0068] Among them, the attribute reputation score is used to evaluate the number of types of provable attribute information provided by the privacy computing node. The provable attribute information is determined based on the attribute information directly provided by the privacy computing node to be evaluated and the attribute information log of the privacy computing node to be evaluated provided by the trusted information source. The trusted information source may include the operation log of the privacy computing node to be evaluated, a trusted third-party organization, or a digital certificate issued by a trusted third-party organization that includes the attribute information of the privacy computing node to be evaluated. The embodiment of the present application does not limit the specific types of trusted information sources.

[0069] The interactive reputation score is used to evaluate the comprehensive reputation score of the interactive privacy computing nodes that have an interactive relationship with the privacy computing node to be evaluated.

[0070] The historical reputation score is used to evaluate the historical comprehensive reputation score of the privacy computing node to be evaluated. The predicted reputation score is used to evaluate the behavioral characteristics of the privacy computing node to be evaluated.

[0071] The specific determination process of the attribute reputation score, the interactive reputation score, the historical reputation score and the predicted reputation score can be referred to in the following embodiments and will not be repeated here.

[0072] S102: Determine the comprehensive reputation score of the privacy computing node to be evaluated based on the attribute reputation score, the interaction reputation score, the historical reputation score, and the predicted reputation score.

[0073] In one possible implementation, the reputation evaluation device may perform a weighted summation of the attribute reputation score, the interaction reputation score, the historical reputation score, and the predicted reputation score to obtain a comprehensive reputation score of the privacy computing node to be evaluated.

[0074] Among them, the weight corresponding to the attribute reputation score is greater than or equal to the weight corresponding to the predicted reputation score, the weight corresponding to the predicted reputation score is greater than the weight corresponding to the historical reputation score, and the weight corresponding to the historical reputation score is greater than or equal to the weight corresponding to the interactive reputation score.

[0075] For example, the reputation evaluation device may calculate the comprehensive reputation score according to the following formula:

[0076] Comprehensive reputation score = weight 1 × attribute reputation score + weight 2 × predicted reputation score + weight 3 × historical reputation score + weight 4 × interactive reputation score formula (1)

[0077] In formula (1), weight 1 + weight 2 + weight 3 + weight 4 = 1, and weight 1 ≥ weight 2 > weight 3 ≥ weight 4.

[0078] In the privacy computing node reputation evaluation method provided in the embodiment of the present application, the reputation evaluation device can obtain the attribute reputation score, interaction reputation score, historical reputation score and predicted reputation score of the privacy computing node to be evaluated, and determine the comprehensive reputation score of the privacy computing node to be evaluated based on the attribute reputation score, interaction reputation score, historical reputation score and predicted reputation score. The reputation scores of multiple dimensions can be comprehensively considered, thereby providing a more comprehensive and accurate reputation evaluation method to ensure the credibility of privacy computing nodes in heterogeneous environments.

[0079] The following is an introduction to the process of obtaining the attribute reputation score in S101 above.

[0080] In some possible embodiments, as described above, the trusted information source may include the operation log of the privacy computing node to be evaluated. In this case, the step of obtaining the attribute reputation score in S101 above may specifically include the following steps:

[0081] Step 1a: Receive attribute information sent from the privacy computing node to be evaluated.

[0082] Among them, the attribute information may include at least one of the following: type and version of the privacy computing framework, directory of data services provided to the outside world, data encryption measures and encryption algorithms, etc.

[0083] For example, as described above, the reputation evaluation device (central node 200) can receive trusted domain joining application information sent from the privacy computing node 100, and the trusted domain joining application information can include attribute information of the privacy computing node.

[0084] Step 2a: Obtain the operation log of the privacy computing node to be evaluated.

[0085] For example, the privacy computing framework used by the privacy computing node to be evaluated may have a log collection and management function built in. The reputation evaluation device may communicate and interact with the module corresponding to the log collection and management function to obtain the operation log of the privacy computing node to be evaluated.

[0086] Step 3a: Determine provable attribute information from the operation log.

[0087] Among them, the provable attribute information is the attribute information in the operation log that is the same as the attribute information sent by the privacy computing node to be evaluated.

[0088] For example, the reputation evaluation device may determine the attribute information directly sent by the privacy computing node to be evaluated that is identical to the operation log as provable attribute information.

[0089] Step 4a: Determine the attribute reputation score based on the number of types of provable attribute information.

[0090] Among them, the attribute reputation score is positively correlated with the number of types of provable attribute information.

[0091] In one possible implementation, the reputation evaluation device may multiply the number of types of provable attribute information of the privacy computing node to be evaluated by a proportional coefficient to obtain the attribute reputation score of the privacy computing node to be evaluated.

[0092] The proportionality coefficient is greater than 0. For example, the proportionality coefficient may be set to 1, 10, or 20. The embodiment of the present application does not limit the specific value of the proportionality coefficient.

[0093] In another possible implementation, the reputation evaluation device may preset a correspondence between the number of types of provable attribute information and the attribute reputation score. The reputation device may determine the attribute reputation score of the privacy computing node to be evaluated based on the number of types of provable attribute information of the privacy computing node to be evaluated and the aforementioned correspondence.

[0094] For example, the reputation evaluation device can use the number of types of provable attribute information of the privacy computing node to be evaluated as an index, traverse and search the aforementioned correspondence, and use the attribute reputation score corresponding to the number of types of provable attribute information of the privacy computing node to be evaluated in the aforementioned correspondence as the attribute reputation score of the privacy computing node to be evaluated.

[0095] For example, the corresponding relationship between the number of types of provable attribute information and the attribute reputation score may be specifically shown in the following Table 1:

[0096] Table 1

[0097]

[0098] As shown in Table 1, the table includes the type and quantity items of provable attribute information and the attribute reputation score items. The type and quantity items of provable attribute information include quantity 1, quantity 2, and quantity 3. The attribute reputation score items include score 1, score 2, and score 3. There is a corresponding relationship between quantity 1 and score 1; there is a corresponding relationship between quantity 2 and score 2; there is a corresponding relationship between quantity 3 and score 3.

[0099] It should be understood that the reputation evaluation of privacy computing nodes in related technologies is mainly concentrated within a single privacy computing framework and lacks cross-framework interoperability, which means that it is difficult to achieve effective interconnection and interoperability between privacy computing nodes between different privacy computing frameworks.

[0100] In the privacy computing node reputation evaluation method provided in the embodiment of the present application, the attribute information sent by the privacy computing node obtained by the reputation evaluation device may include the privacy computing framework and version deployed by the privacy computing node. In this way, the reputation evaluation device can perform reputation evaluation on the privacy computing node across privacy computing frameworks, which can improve the interoperability between different privacy computing frameworks and realize cross-framework interconnection.

[0101] The following introduces the steps of obtaining the interactive reputation score of the privacy computing node to be evaluated in the above S101.

[0102] In some possible embodiments, the step of obtaining the interactive reputation score of the privacy computing node to be evaluated in the above S101 may specifically include the following steps:

[0103] Step 1b: Perform weighted summation on the comprehensive reputation scores of the interactive privacy computing nodes that have an interactive relationship with the privacy computing node to be evaluated to obtain an interactive reputation score.

[0104] Among them, having an interactive relationship with the privacy computing node to be evaluated means being connected to the privacy computing node to be evaluated, or being connected to the privacy computing node to be evaluated and having an interaction history.

[0105] For example, the reputation evaluation device may be preset with a network topology relationship in a trusted domain, and the reputation evaluation device may determine an interactive privacy computing node connected to the privacy computing node to be evaluated based on the network topology relationship.

[0106] For another example, the reputation evaluation device can obtain the operation log of the privacy computing node to be evaluated, and then determine the interactive privacy computing nodes that have an interaction history with the privacy computing node to be evaluated based on the operation log.

[0107] Optionally, the reputation evaluation device may perform a weighted summation of the comprehensive reputation scores of the interactive privacy computing nodes that have an interactive relationship with the privacy computing node to be evaluated according to the following formula (2):

[0108]

[0109] In formula (2), k represents the number of all interactive privacy computing nodes that have an interactive relationship with the privacy computing node to be evaluated; the comprehensive reputation score i Represents the comprehensive reputation score of the ith interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated. Weight i Represents the weight corresponding to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated.

[0110] Optionally, the weight corresponding to the interactive privacy computing node (that is, the above weight i ) is determined based on the reputation weight corresponding to the interactive privacy computing node and the connection weight corresponding to the interactive privacy computing node.

[0111] Among them, the reputation weight is positively correlated with the degree of score deviation. The degree of score deviation is the degree of deviation between the comprehensive reputation score of the corresponding interactive privacy computing node and the average reputation score. The average reputation score is the average of the comprehensive reputation scores of all privacy computing nodes in the trusted domain where the privacy computing node to be evaluated is located.

[0112] The connection weight is positively correlated with the number of privacy computing nodes connected to the corresponding interactive privacy computing node.

[0113] As an example, the weight i It can be calculated by the following formula (3) to formula (5):

[0114] Weight i =(Reputation Weight i + Connection Weight i ) / 2 formula (3)

[0115] In formula (3), the reputation weight i Represents the reputation weight corresponding to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated, and the connection weight i Represents the connection weight corresponding to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated.

[0116]

[0117] In formula (4), the comprehensive reputation score i Represents the comprehensive reputation score of the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated.

[0118]

[0119] In formula (5), the number of connected nodes is i It represents the number of privacy computing nodes connected to the ith interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated. The total number of node connections represents the sum of the number of privacy computing nodes connected to each privacy computing node in the trusted domain.

[0120] For example, taking the trusted domain including privacy computing node A, privacy computing node B, and privacy computing node C as an example, assuming that privacy computing node A is connected to 2 other privacy computing nodes, privacy computing node B is connected to 2 other privacy computing nodes, and privacy computing node C is connected to 2 other privacy computing nodes, then the total number of node connections is 2+2+2=6.

[0121] The following introduces the steps of obtaining the historical reputation score of the privacy computing node to be evaluated in the above S101.

[0122] In some possible embodiments, the step of obtaining the historical reputation score of the privacy computing node to be evaluated in S101 may specifically include the following steps:

[0123] Step 1c: Exponentially decay the historical comprehensive reputation score of the privacy computing node to be evaluated to obtain the historical reputation score of the privacy computing node to be evaluated.

[0124] Optionally, the privacy evaluation device may perform exponential decay according to the following formula (6):

[0125]

[0126] Among them, Adjusted Score represents the historical credit score. j represents the historical comprehensive reputation score at the time of the jth evaluation. λ represents the decay factor, which is positively correlated with the decay rate. j The unit time difference between the jth evaluation and the current time can be a day, a week, or a month. The specific unit of the unit time difference is not limited in the embodiment of the present application.

[0127] For example, the comprehensive reputation score of the privacy computing node to be evaluated is 90 in January, 85 in February, 92 in March, and 88 in April. Assuming that the unit time difference is 1 month, the attenuation factor is 0.1, and the current time is a certain time point in April, then referring to the above formula (6) and substituting the specific values ​​into the formula (7) and formula (8), the following formulas (7) and (8) can be obtained: Adjusted Score = 90 × e -0.1×3 +85×e -0.1×2 +92×e -0.1×1 +88×e -0.1×0 Formula (7)

[0129] Adjusted Score = 90 × e -0.3 +85×e -0.2 +92×e -0.1 +88 formula(8)

[0130] The following introduces the steps of obtaining the predicted reputation score of the privacy computing node to be evaluated in the above S101.

[0131] In some possible embodiments, the step of obtaining the predicted reputation score of the privacy computing node to be evaluated in S101 may specifically include the following steps:

[0132] Step 1d: Obtain the behavioral characteristic data of the privacy computing node to be evaluated.

[0133] Among them, the behavioral characteristic data includes at least one of the following: node activity (or the frequency of participating in privacy computing tasks), task contribution, node response request speed, number of successfully completed tasks, number of failed tasks, operation logs, number of node trusted algorithms, and amount of data transmitted by the node, etc.

[0134] Step 2d: Determine the abnormal behavior probability of the privacy computing node to be evaluated based on the behavioral feature data and the reputation prediction model.

[0135] Among them, the reputation prediction model is used to predict the probability of abnormal behavior based on the behavior feature data of the privacy computing node. For example, the reputation prediction model can be used to predict the current probability of abnormal behavior based on the current behavior feature data of the privacy computing node, or it can also be used to predict the probability of future abnormal behavior based on the current behavior characteristics of the privacy computing node. The embodiments of this application are not limited to this. The specific training process of the reputation prediction model can be referred to the following embodiments, which will not be repeated here.

[0136] Step 3d: Determine the predicted reputation score of the privacy computing node to be evaluated based on the abnormal behavior probability of the privacy computing node to be evaluated.

[0137] Optionally, the reputation evaluation device may specifically calculate the predicted reputation score of the privacy computing node to be evaluated according to the following formula (9):

[0138] Predicted reputation score = (1-abnormal behavior probability) × the most recent historical comprehensive reputation score of the privacy computing node to be evaluated Formula (9)

[0139] In some embodiments, before the above step 1d, the reputation evaluation device may also obtain a trained reputation prediction model.

[0140] In a possible implementation, the reputation evaluation device may directly obtain a trained reputation prediction model from other devices.

[0141] For example, the reputation evaluation device may obtain a trained reputation prediction model from other devices by downloading or transferring it to an intermediate storage medium.

[0142] In another possible implementation, the reputation evaluation device may also use the training samples to train a reputation prediction model. In this case, before the above step 1d, the method may further include the following steps:

[0143] Step 1e, obtaining an initial training sample set; the initial training sample set includes a first proportion of positive samples and a second proportion of unlabeled samples.

[0144] Among them, each positive sample includes behavioral feature data of a privacy computing node and an abnormal behavior probability label of the privacy computing node, and the abnormal behavior probability label of the positive sample is higher than the probability threshold. The probability threshold can be preset in the reputation evaluation device, for example, the probability threshold can be set to 50%, 60%, or 70%. The embodiment of the present application does not limit the specific value of the probability threshold. Each unlabeled sample includes behavioral feature data of a privacy computing node. The second ratio is higher than the first ratio.

[0145] For example, the initial training sample set may include 20% positive samples and 80% unlabeled samples, or 30% positive samples and 70% unlabeled samples, etc. The embodiment of the present application does not limit the specific values ​​of the first ratio and the second ratio.

[0146] As an example, each positive sample or unlabeled sample is associated with an identity mapping ID of a privacy computing node.

[0147] Step 2e: Use the naive Bayes model to label the unlabeled samples with abnormal behavior probability labels and classify the unlabeled samples into positive samples and negative samples.

[0148] Among them, the abnormal behavior probability label of the negative sample is lower than the probability threshold.

[0149] Step 3e: Based on the positive samples in the initial sample set and the positive samples and negative samples marked by the naive Bayes model, the initial model is trained to obtain a reputation prediction model.

[0150] Optionally, the reputation evaluation device can input one or more positive samples or negative samples into the initial model each time to obtain the predicted abnormal behavior probability output by the initial model, and then use Pipeline to implement serial pipeline processing of parameter optimization, and GridSearch to realize the self-combination of multiple parameter indicators in the parameter optimization process, and automatically screen the optimal parameters.

[0151] Optionally, the reputation evaluation device may first train the initial model until the training stop condition is reached, and then use the verification sample set to verify the initial model to obtain the reputation prediction model. In this case, the above step 3e may specifically include the following steps:

[0152] Step 3.1e: Based on the positive samples in the initial sample set and the positive and negative samples labeled by the naive Bayes model, the initial model is trained until the training stop condition is reached.

[0153] Among them, the training stop conditions may include: the number of times the reputation evaluation device inputs the training sample (positive sample or negative sample) into the initial model reaches a number threshold, and / or the error between the predicted abnormal behavior probability predicted by the initial model and the abnormal behavior probability label of the training sample (positive sample or negative sample) is less than an error threshold.

[0154] The number threshold can be preset in the reputation evaluation device, for example, the number threshold can be set to 500 times, 1000 times, or 10000 times, etc. The embodiment of the present application does not limit the specific value of the number threshold. The error threshold can be preset in the reputation evaluation device, for example, the error threshold can be set to 5%, 10%, or 15%, etc. The embodiment of the present application does not limit the specific value of the error threshold.

[0155] Step 3.2e: Based on the validation sample set, the initial model is validated to obtain the prediction results of the initial model.

[0156] Among them, the verification sample set includes multiple positive samples.

[0157] Step 3.3e: Based on the prediction results of the initial model, calculate the validation score of the initial model.

[0158] As an example, the reputation evaluation device may use precision, recall, accuracy, or F1 score as the verification score.

[0159] As another example, the reputation evaluation device may calculate the verification score of the initial model according to the following formula (10):

[0160]

[0161] In formula (10), S represents the validation score; r represents the recall rate; and Pr[f(x)=1] represents the probability that a sample in the validation set is determined to be a positive sample based on the abnormal behavior probability predicted by the initial model.

[0162] Step 3.4e: When the verification score is higher than the score threshold, the initial model is determined as the reputation prediction model.

[0163] Based on the understanding of the above embodiments, Figure 3 This is a schematic diagram of the reputation evaluation process provided by the embodiment of this application. Figure 3 As shown, the central node can send a private computing node ( Figure 3 In the figure, a CA certificate is issued using three privacy computing nodes, namely privacy computing node A, privacy computing node B and privacy computing node C, as an example.

[0164] Taking privacy computing node A as an example, the central node can determine the attribute reputation score according to the number of types of provable attribute information of privacy computing node A, and determine the comprehensive interactive reputation score according to the comprehensive reputation score of privacy computing node B and the comprehensive reputation score of privacy computing node C, and obtain the interactive reputation score of privacy computing node A. According to the historical comprehensive reputation score of privacy computing node A ( Figure 3 The historical reputation score is determined by taking historical reputation score 1, historical reputation score 2, historical reputation score 3, etc. as examples, and then the historical reputation score is determined by statistical scores. The reputation evaluation device can obtain a predicted reputation score based on multiple behavioral feature data (of privacy computing node A) using a semi-supervised classification model. Finally, the reputation evaluation device can obtain a comprehensive reputation score of privacy computing node A based on the attribute reputation score, the interactive reputation score, the historical reputation score, and the predicted reputation score.

[0165] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to achieve the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. It should be easy to realize that the technical goals in this field are combined with the units and algorithm steps of each example described in the embodiments disclosed in this article, and the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical goals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0166] In an exemplary embodiment, the present application also provides a reputation evaluation device. Figure 4 This is a schematic diagram of the composition of the reputation evaluation device provided in the embodiment of the present application. Figure 4 As shown, the device includes: an acquisition module 401 and a processing module 402.

[0167] An acquisition module 401 is used to acquire the attribute reputation score, interactive reputation score, historical reputation score and predicted reputation score of the privacy computing node to be evaluated;

[0168] Among them, the attribute reputation score is used to evaluate the number of types of provable attribute information provided by the privacy computing node; the provable attribute information is determined based on the attribute information directly provided by the privacy computing node to be evaluated and the attribute information of the privacy computing node to be evaluated provided by the trusted information source; the interactive reputation score is used to evaluate the comprehensive reputation score of the interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated; the historical reputation score is used to evaluate the historical comprehensive reputation score of the privacy computing node to be evaluated; the predicted reputation score is used to evaluate the behavioral characteristics of the privacy computing node to be evaluated;

[0169] The processing module 402 is used to determine the comprehensive reputation score of the privacy computing node to be evaluated based on the attribute reputation score, the interaction reputation score, the historical reputation score and the predicted reputation score.

[0170] In some possible embodiments, the trusted information source includes the operation log of the privacy computing node to be evaluated; the acquisition module 401 is specifically used to receive attribute information sent from the privacy computing node to be evaluated; obtain the operation log of the privacy computing node to be evaluated; determine the provable attribute information from the operation log; wherein the provable attribute information is the attribute information in the operation log that is the same as the attribute information sent by the privacy computing node to be evaluated; determine the attribute reputation score based on the number of types of provable attribute information; the attribute reputation score is positively correlated with the number of types of provable attribute information.

[0171] In some other possible embodiments, the acquisition module 401 is specifically configured to perform weighted summation on the comprehensive reputation scores of the interactive privacy computing nodes that have an interactive relationship with the privacy computing node to be evaluated, so as to obtain an interactive reputation score.

[0172] In some other possible embodiments, the weight corresponding to the interactive privacy computing node is determined based on the reputation weight corresponding to the interactive privacy computing node and the connection weight corresponding to the interactive privacy computing node; the reputation weight is positively correlated with the degree of score deviation; the score deviation is the degree of deviation between the comprehensive reputation score of the corresponding interactive privacy computing node and the average reputation score; the average reputation score is the average of the comprehensive reputation scores of all privacy computing nodes in the trusted domain where the privacy computing node to be evaluated is located; the trusted domain is a regional network composed of privacy computing nodes that have issued CA certificates; the connection weight is positively correlated with the number of privacy computing nodes connected to the corresponding interactive privacy computing node.

[0173] In some other possible embodiments, the weight corresponding to the interactive privacy computing node is calculated by the following formula:

[0174] Weight i =(Reputation Weight i + Connection Weight i ) / 2;

[0175] Among them, the weight i Represents the weight corresponding to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated; reputation weight i Represents the reputation weight corresponding to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated, and the connection weight i Represents the connection weight corresponding to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated;

[0176]

[0177] Among them, the comprehensive reputation score i Represents the comprehensive reputation score of the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated;

[0178]

[0179] The number of connected nodes i It represents the number of privacy computing nodes connected to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated; the total number of node connections represents the sum of the number of privacy computing nodes connected to each privacy computing node in the trusted domain.

[0180] In some other possible embodiments, the acquisition module 401 is specifically used to perform exponential decay on the historical comprehensive reputation score of the privacy computing node to be evaluated to obtain the historical reputation score of the privacy computing node to be evaluated.

[0181] In some other possible embodiments, the acquisition module 401 is specifically used to perform exponential decay on the historical comprehensive reputation score of the privacy computing node to be evaluated to obtain the historical reputation score of the privacy computing node to be evaluated, including:

[0182] Exponential decay is performed according to the following formula:

[0183]

[0184] Among them, Adjusted Score represents the historical credit score; Score j represents the historical comprehensive reputation score at the time of the jth evaluation; λ represents the decay factor, which is positively correlated with the decay rate; t j It represents the unit time difference between the j-th evaluation and the current time.

[0185] In some other possible embodiments, the acquisition module 401 is specifically used to obtain behavioral characteristic data of the privacy computing node to be evaluated; determine the abnormal behavior probability of the privacy computing node to be evaluated based on the behavioral characteristic data and the reputation prediction model; the reputation prediction model is used to predict the abnormal behavior probability based on the behavioral characteristic data of the privacy computing node; and determine the predicted reputation score of the privacy computing node to be evaluated based on the abnormal behavior probability of the privacy computing node to be evaluated.

[0186] In some other possible embodiments, the acquisition module 401 is specifically used to calculate the predicted reputation score of the privacy computing node to be evaluated according to the following formula:

[0187] Predicted reputation score = (1-abnormal behavior probability) × the most recent historical comprehensive reputation score of the privacy computing node to be evaluated.

[0188] In some other possible embodiments, the acquisition module 401 is further used to acquire an initial training sample set; the initial training sample set includes a first proportion of positive samples and a second proportion of unlabeled samples; wherein each positive sample includes behavioral feature data of a privacy computing node and an abnormal behavior probability label of the privacy computing node; the abnormal behavior probability label of the positive sample is higher than the probability threshold; each unlabeled sample includes behavioral feature data of a privacy computing node; the second proportion is higher than the first proportion;

[0189] The processing module 402 is also used to use the naive Bayes model to label the unlabeled samples with abnormal behavior probability labels, and classify the unlabeled samples into positive samples and negative samples; the abnormal behavior probability labels of the negative samples are lower than the probability threshold; based on the positive samples in the initial sample set and the positive and negative samples labeled by the naive Bayes model, the initial model is trained to obtain a reputation prediction model.

[0190] In some other possible embodiments, the processing module 402 is specifically used to train the initial model based on the positive samples in the initial sample set and the positive samples and negative samples marked by the naive Bayes model until the training stop condition is reached; based on the verification sample set, the initial model is verified to obtain the prediction result of the initial model; the verification sample set includes multiple positive samples; based on the prediction result of the initial model, the verification score of the initial model is calculated; when the verification score is higher than the score threshold, the initial model is determined as a credit prediction model.

[0191] In some other possible embodiments, the processing module 402 is specifically configured to calculate the verification score of the initial model according to the following formula:

[0192]

[0193] Where S represents the validation score; r represents the recall rate; and Pr[f(x)=1] represents the probability that a sample in the validation set is determined to be a positive sample based on the abnormal behavior probability predicted by the initial model.

[0194] In some other possible embodiments, processing module 402 is specifically used to perform weighted summation of the attribute reputation score, the interaction reputation score, the historical reputation score and the predicted reputation score to obtain a comprehensive reputation score of the privacy computing node to be evaluated; wherein the weight corresponding to the attribute reputation score is greater than or equal to the weight corresponding to the predicted reputation score, the weight corresponding to the predicted reputation score is greater than the weight corresponding to the historical reputation score, and the weight corresponding to the historical reputation score is greater than or equal to the weight corresponding to the interaction reputation score.

[0195] In an exemplary embodiment, the present application also provides an electronic device, and the above-mentioned reputation evaluation device can be applied to the above-mentioned electronic device. Figure 5 The following is a schematic diagram of the composition of the electronic device provided in the embodiment of the present application. Figure 5 As shown, the electronic device includes:

[0196] The processor 10 , the memory 20 , the communication line 30 , the communication interface 40 , and the input / output interface 50 .

[0197] The processor 10 , the memory 20 , the communication interface 40 and the input / output interface 50 may be connected via a communication line 30 .

[0198] The processor 10 is used to execute instructions stored in the memory 20 to implement the privacy computing node reputation evaluation method provided in the above embodiments of the present application. The processor 10 can be a CPU, a general-purpose processor network processor (network processor, NP), a digital signal processor (digital signal processing, DSP), a microprocessor, a microcontroller (micro control unit, MCU) / single-chip microcomputer / single-chip microcomputer, a programmable logic device (programmable logic device, PLD) or any combination thereof. The processor 10 can also be any other device with processing functions, such as a circuit, a device or a software module, which is not limited in the embodiments of the present application. In one example, the processor 10 may include one or more CPUs, such as Figure 5 As an optional implementation, the electronic device may include multiple processors, for example, in addition to the processor 10, it may also include a processor 60 ( Figure 5 The dashed line is used as an example.

[0199] The memory 20 is used to store instructions. For example, the instructions may be computer programs. Optionally, the memory 20 may be a read-only memory (ROM) or other types of static storage devices that can store static information and / or instructions, or a random access memory (RAM) or other types of dynamic storage devices that can store information and / or instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage devices, etc., and the embodiments of the present application are not limited to this.

[0200] It should be noted that the memory 20 may exist independently of the processor 10, or may be integrated with the processor 10. The memory 20 may be located inside the electronic device, or may be located outside the electronic device, which is not limited in the embodiment of the present application.

[0201] The communication line 30 is used to transmit information between various components included in the electronic device.

[0202] The communication interface 40 is used to communicate with other devices or other communication networks. The other communication networks may be Ethernet, radio access network (RAN), wireless local area network (WLAN), etc. The communication interface 40 may be a module, a circuit, a transceiver or any device capable of achieving communication.

[0203] The input / output interface 50 is used to implement human-computer interaction between a user and an electronic device, for example, to implement action interaction or information interaction between a user and an electronic device.

[0204] Exemplarily, the input / output interface 50 may be a mouse, a keyboard, a display screen, or a touch display screen, etc. Action interaction or information interaction between a user and an electronic device may be achieved through a mouse, a keyboard, a display screen, or a touch display screen, etc.

[0205] It should be noted that Figure 5 The structure shown in the figure does not constitute a limitation on the electronic device, except Figure 5 In addition to the components shown, the electronic device may include more or fewer components than shown, or a combination of certain components, or a different arrangement of components.

[0206] In an exemplary embodiment, the embodiment of the present application further provides a computer program product, which includes computer instructions. When the computer instructions are executed in an electronic device, the electronic device implements the method in the aforementioned method embodiment.

[0207] In an exemplary embodiment, the present application also provides a readable storage medium, which includes software instructions. When the software instructions are executed in an electronic device, the electronic device implements the method in the aforementioned method embodiment. The readable storage medium can be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device.

[0208] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer-executable instructions. When loading and executing computer-executable instructions on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer-executable instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer-executable instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0209] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other changes to the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit can implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0210] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0211] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A privacy computing node reputation evaluation method, characterized in that: The method comprises: Obtain the attribute reputation score, interactive reputation score, historical reputation score, and predicted reputation score of the privacy computing node to be evaluated; Among them, the attribute reputation score is used to evaluate the number of types of provable attribute information provided by the privacy computing node to be evaluated; the provable attribute information is determined based on the attribute information directly provided by the privacy computing node to be evaluated and the attribute information of the privacy computing node to be evaluated provided by a trusted information source; the interactive reputation score is used to evaluate the comprehensive reputation score of the interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated; the historical reputation score is used to evaluate the historical comprehensive reputation score of the privacy computing node to be evaluated; the predicted reputation score is used to evaluate the behavioral characteristics of the privacy computing node to be evaluated; Based on the attribute reputation score, the interaction reputation score, the historical reputation score, and the predicted reputation score, a comprehensive reputation score of the privacy computing node to be evaluated is determined.

2. The method according to claim 1, characterized in that The trusted information source includes the operation log of the privacy computing node to be evaluated; and obtaining the attribute reputation score of the privacy computing node to be evaluated includes: Receiving attribute information sent from the privacy computing node to be evaluated; Obtaining the operation log of the privacy computing node to be evaluated; Determining the provable attribute information from the operation log; The provable attribute information is the attribute information in the operation log that is the same as the attribute information sent by the privacy computing node to be evaluated; The attribute reputation score is determined according to the number of types of the provable attribute information; the attribute reputation score is positively correlated with the number of types of the provable attribute information.

3. The method according to claim 1, characterized in that The step of obtaining the interactive reputation score of the privacy computing node to be evaluated includes: The comprehensive reputation scores of the interactive privacy computing nodes that have an interactive relationship with the privacy computing node to be evaluated are weighted and summed to obtain the interactive reputation score.

4. The method according to claim 3, characterized in that The weight corresponding to the interactive privacy computing node is determined based on the reputation weight corresponding to the interactive privacy computing node and the connection weight corresponding to the interactive privacy computing node; The reputation weight is positively correlated with the degree of score deviation; the degree of score deviation is the degree of deviation between the comprehensive reputation score of the corresponding interactive privacy computing node and the average reputation score; The average reputation score is the average of the comprehensive reputation scores of all privacy computing nodes in the trusted domain where the privacy computing node to be evaluated is located; the trusted domain is a regional network composed of privacy computing nodes that have issued CA certificates; The connection weight is positively correlated with the number of privacy computing nodes connected to the corresponding interactive privacy computing node.

5. The method according to claim 4, characterized in that The weight corresponding to the interactive privacy computing node is calculated by the following formula: Weight i =(Reputation Weight i + Connection Weight i ) / 2; Among them, the weight i Represents the weight corresponding to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated; reputation weight i Represents the reputation weight corresponding to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated, and the connection weight i Represents the connection weight corresponding to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated; Among them, the comprehensive reputation score i Represents the comprehensive reputation score of the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated; The number of connected nodes i It represents the number of privacy computing nodes connected to the i-th interactive privacy computing node that has an interactive relationship with the privacy computing node to be evaluated; the total number of node connections represents the sum of the number of privacy computing nodes connected to each privacy computing node in the trusted domain.

6. The method according to claim 1, characterized in that The step of obtaining the historical reputation score of the privacy computing node to be evaluated includes: The historical comprehensive reputation score of the privacy computing node to be evaluated is exponentially decayed to obtain the historical reputation score of the privacy computing node to be evaluated.

7. The method according to claim 6, characterized in that The exponential decay of the historical comprehensive reputation score of the privacy computing node to be evaluated to obtain the historical reputation score of the privacy computing node to be evaluated includes: Exponential decay is performed according to the following formula: Wherein, AdjustedScore represents the historical credit score; Score j represents the historical comprehensive reputation score at the jth evaluation; λ represents the decay factor, which is positively correlated with the decay rate; t j It represents the unit time difference between the j-th evaluation and the current time.

8. The method according to claim 1, characterized in that The step of obtaining the predicted reputation score of the privacy computing node to be evaluated includes: Obtain behavioral characteristic data of the privacy computing node to be evaluated; Determine the abnormal behavior probability of the privacy computing node to be evaluated according to the behavior feature data and the reputation prediction model; the reputation prediction model is used to predict the abnormal behavior probability according to the behavior feature data of the privacy computing node; Determine a predicted reputation score of the privacy computing node to be evaluated based on the abnormal behavior probability of the privacy computing node to be evaluated.

9. The method according to claim 8, characterized in that The step of determining the predicted reputation score of the privacy computing node to be evaluated according to the abnormal behavior probability of the privacy computing node to be evaluated includes: The predicted reputation score of the privacy computing node to be evaluated is calculated according to the following formula: Predicted reputation score = (1-abnormal behavior probability) × the most recent historical comprehensive reputation score of the privacy computing node to be evaluated.

10. The method according to claim 8, characterized in that The method further comprises: Acquire an initial training sample set; the initial training sample set includes a first proportion of positive samples and a second proportion of unlabeled samples; Wherein, each positive sample includes behavioral feature data of a privacy computing node and an abnormal behavior probability label of the privacy computing node; the abnormal behavior probability label of the positive sample is higher than the probability threshold; each unlabeled sample includes behavioral feature data of a privacy computing node; the second ratio is higher than the first ratio; Using a naive Bayes model to label the unlabeled samples with abnormal behavior probability labels, the unlabeled samples are classified into positive samples and negative samples; the abnormal behavior probability labels of the negative samples are lower than the probability threshold; Based on the positive samples in the initial sample set and the positive samples and negative samples marked by the naive Bayes model, the initial model is trained to obtain the reputation prediction model.

11. The method according to claim 10, characterized in that The step of training the initial model based on the positive samples in the initial sample set and the positive samples and negative samples marked by the naive Bayes model to obtain the reputation prediction model includes: Based on the positive samples in the initial sample set and the positive samples and negative samples marked by the naive Bayes model, the initial model is trained until a training stop condition is reached; Based on the verification sample set, the initial model is verified to obtain the prediction result of the initial model; the verification sample set includes multiple positive samples; Based on the prediction results of the initial model, calculating the verification score of the initial model; When the verification score is higher than a score threshold, the initial model is determined as the reputation prediction model.

12. The method according to claim 11, characterized in that The step of calculating the verification score of the initial model based on the prediction result of the initial model comprises: The validation score of the initial model is calculated according to the following formula: Wherein, S represents the verification score; r represents the recall rate; and Pr[f(x)=1] represents the probability that a sample in the verification set is determined as a positive sample based on the abnormal behavior probability predicted by the initial model.

13. The method according to any one of claims 1 to 12, characterized in that: The step of determining the comprehensive reputation score of the privacy computing node to be evaluated based on the attribute reputation score, the interaction reputation score, the historical reputation score, and the predicted reputation score includes: Taking a weighted sum of the attribute reputation score, the interaction reputation score, the historical reputation score, and the predicted reputation score to obtain a comprehensive reputation score of the privacy computing node to be evaluated; Among them, the weight corresponding to the attribute reputation score is greater than or equal to the weight corresponding to the predicted reputation score, the weight corresponding to the predicted reputation score is greater than the weight corresponding to the historical reputation score, and the weight corresponding to the historical reputation score is greater than or equal to the weight corresponding to the interactive reputation score.

14. An electronic device, characterized in that: The electronic device comprises: a processor and a memory; The memory stores instructions executable by the processor; When the processor is configured to execute the instructions, the electronic device implements the method according to any one of claims 1 to 13.

15. A readable storage medium, characterized in that: The readable storage medium includes: software instructions; When the software instructions are executed in an electronic device, the electronic device implements the method according to any one of claims 1 to 13.

16. A computer program product, characterized in that The computer program product comprises: computer instructions; When the computer instructions are executed in an electronic device, the electronic device implements the method according to any one of claims 1 to 13.

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