A Privacy Data Encryption Method and System for an Information Service Platform

Abstract

This application relates to the technical field of data processing, and specifically relates to a privacy data encryption method and system for an information service platform. The method includes: obtaining the operation status data of the information service platform and the privacy data of users; constructing an adaptive homomorphic encryption parameter model according to the operation status data and the privacy data; and encrypting the privacy data of users by using the adaptive homomorphic encryption parameter model. In the embodiments of this application, by combining the operation status of the information service platform and the privacy evaluation of users' privacy data, an adaptive homomorphic encryption model with dynamic adjustment is adopted, so that the encryption strength can be automatically adjusted according to the resource load of the platform and the data privacy requirements. In this way, on the premise of protecting users' privacy, the encryption efficiency and effect can be improved, and the platform performance bottleneck caused by excessive load during the encryption process can be avoided.

Description

Technical Field

[0001] This application belongs to the technical field of data processing, and particularly relates to a method and system for encrypting privacy data of an information service platform. Background Art

[0002] While providing convenient services for users, information service platforms have also accumulated a large amount of users' privacy data, including personal identity information, payment information, communication records, etc. Since users' privacy data is an important part of their personal rights and interests, leakage or abuse of this data may lead to identity theft, economic losses, and other serious consequences. Therefore, it is necessary to encrypt users' privacy data.

[0003] However, when the current platform encryption algorithm encrypts users' privacy data, the encryption efficiency is poor and the encryption effect is not good. Therefore, how to improve the encryption efficiency and effect of users' privacy data has become an urgent problem to be solved by information service platforms. Summary of the Invention

[0004] To solve the above problems, embodiments of this application provide a method and system for encrypting privacy data of an information service platform.

[0005] According to the first aspect of the embodiments of this application, a method for encrypting privacy data of an information service platform is provided. The method includes: obtaining the operation status data of the information service platform and the privacy data of users;

[0006] Constructing an adaptive homomorphic encryption parameter model according to the operation status data and the privacy data;

[0007] Using the adaptive homomorphic encryption parameter model to encrypt the privacy data of users.

[0008] Optionally, the constructing of the adaptive homomorphic encryption parameter model includes:

[0009] Constructing the state surface of the information service platform according to the load data of the server of the information service platform;

[0010] Obtaining the usage rate gradient of the server according to the state surface;

[0011] Obtaining the encryption state parameter of the server according to the usage rate gradient of the server;

[0012] Evaluating the privacy data to obtain the privacy valuation of the privacy data;

[0013] Constructing an adaptive homomorphic encryption parameter model according to the encryption state parameter and the privacy valuation.

[0014] Optionally, constructing the state surface of the information service platform according to the load data of the server of the information service platform includes:

[0015] Obtain the utilization rates of the components of the server, where the components include: CPU, disk, and network;

[0016] Sort the utilization rates of the components of the server to obtain a sorted component utilization rate;

[0017] Construct a three-dimensional rectangular coordinate system with time as the horizontal axis, the sorted serial number of the component utilization rate as the vertical axis, and the utilization rate as the vertical axis;

[0018] Obtain the utilization rate curve of the components of the server according to the utilization rates of the components of the server;

[0019] Map the utilization rate curve of the components to the three-dimensional rectangular coordinate system, and perform surface fitting on all the curves in the three-dimensional rectangular coordinate system using the least squares method to obtain the state surface of the information service platform.

[0020] Optionally, obtaining the utilization rate gradient of the server includes:

[0021] Obtain the disk utilization rate of the server at the k-th moment;

[0022] Obtain the CPU utilization rate of the server at the k-th moment;

[0023] Obtain the network utilization rate of the server at the k-th moment;

[0024] Obtain the utilization rate gradient of the server according to the disk utilization rate, the CPU utilization rate, and the network utilization rate.

[0025] Optionally, obtaining the encryption status parameter of the server includes:

[0026] Obtain the CPU utilization rate of the server at the current moment;

[0027] Obtain the rate of change of the utilization rate gradient of the server at the current moment;

[0028] Obtain the deviation rate of the rate of change of the utilization rate gradient of the server at the current moment;

[0029] Obtain the encryption status parameter of the server according to the CPU utilization rate at the current moment, the rate of change of the utilization rate gradient, and the deviation rate of the rate of change of the utilization rate gradient.

[0030] Optionally, obtaining the rate of change of the utilization rate gradient of the server at the current moment includes:

[0031] Obtain the usage rate gradients of all moments within a preset proximity range of the current moment;

[0032] Perform a linear fit on the usage rate gradients to obtain the usage rate gradient change line at the current moment;

[0033] Take the slope of the usage rate gradient change line as the usage rate gradient change rate of the server at the current moment.

[0034] Optionally, the obtaining of the deviation rate of the usage rate gradient change of the server at the current moment includes:

[0035] Obtain the Euclidean distance data between the usage rate gradients of all moments and the usage rate gradient change line;

[0036] Obtain the standard deviation of the Euclidean distance data;

[0037] Take the standard deviation as the deviation rate of the usage rate gradient change of the server at the current moment.

[0038] Optionally, the evaluating of the privacy data to obtain the privacy valuation of the privacy data includes:

[0039] Classify the privacy data to obtain classified privacy data;

[0040] Obtain the information entropy of the classified privacy data;

[0041] Obtain the privacy valuation of the privacy data based on the information entropy of the classified privacy data.

[0042] Optionally, the adaptive homomorphic encryption parameter model includes the number of addition operations of the encryption algorithm for the privacy data, the number of multiplication operations of the encryption algorithm for the privacy data, and the total number of arithmetic operations of the encryption algorithm for the privacy data. The constructing of the adaptive homomorphic encryption parameter model includes:

[0043] Take the vector formed by the privacy valuations corresponding to the privacy data of different categories in the privacy data as a data point to obtain a scatter plot of all user privacy data;

[0044] According to the scatter plot of the privacy data, use the DBSCAN clustering algorithm to cluster all users in the information service platform to obtain several user clusters;

[0045] For any one of the user clusters, based on the privacy valuation of the privacy data and using a support vector machine to classify each category of privacy data in the user cluster to obtain several encrypted clusters;

[0046] Obtain the cumulative value of the privacy valuations of all privacy data in the encrypted cluster;

[0047] Obtain the first data volume of all private data in the encrypted cluster;

[0048] Obtain the second data volume of all private data in the user cluster where the encrypted cluster is located;

[0049] According to the encryption status parameter and the first data volume, obtain the number of addition operations of the encryption algorithm for all private data in the encrypted cluster;

[0050] According to the encryption status parameter, the accumulated value, the first data volume, and the second data volume, obtain the number of multiplication operations of the encryption algorithm for all private data in the encrypted cluster;

[0051] According to the sum value of the number of addition operations and the number of multiplication operations, obtain the total number of arithmetic operations of the encryption algorithm for all private data in the encrypted cluster.

[0052] According to the second aspect of the embodiments of the present application, there is provided a private data encryption system for an information service platform, the system includes an information service platform, and the information service platform includes:

[0053] A memory, on which a computer program is stored;

[0054] A processor, configured to execute the computer program in the memory to implement the steps of any of the methods in the first aspect.

[0055] In summary, the embodiments of the present application provide a method and system for encrypting private data of an information service platform. The method includes: obtaining the operation status data of the information service platform and the private data of the user; constructing an adaptive homomorphic encryption parameter model according to the operation status data and the private data; using the adaptive homomorphic encryption parameter model to encrypt the private data of the user. By combining the operation status of the information service platform and the privacy valuation of the user's private data, and adopting a dynamically adjusted adaptive homomorphic encryption model, the embodiments of the present application can automatically adjust the encryption strength according to the resource load of the platform and the data privacy requirements. In this way, it is possible to improve the encryption efficiency and effect on the premise of protecting user privacy, and avoid the platform performance bottleneck caused by excessive load during the encryption process. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the implementation solutions of the present application, the drawings required for the implementation will be briefly introduced below. It should be understood that the drawings only show some implementation solutions of the present application, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained according to the drawings without creative efforts.

[0057] Figure 1 It is a flowchart of a method for encrypting privacy data of an information service platform shown according to an exemplary embodiment;

[0058] Figure 2 It is a flowchart of a method for constructing an adaptive homomorphic encryption parameter model shown according to an exemplary embodiment;

[0059] Figure 3 It is a flowchart of a method for constructing a state surface of an information service platform shown according to an exemplary embodiment;

[0060] Figure 4 It is a flowchart of a method for obtaining the usage rate gradient of a server shown according to an exemplary embodiment;

[0061] Figure 5 It is a flowchart of a method for obtaining the encryption state parameters of a server shown according to an exemplary embodiment;

[0062] Figure 6 It is a flowchart of a method for obtaining the rate of change of the usage rate gradient of a server at the current moment shown according to an exemplary embodiment;

[0063] Figure 7 It is a flowchart of a method for obtaining the deviation rate of the change of the usage rate gradient of a server at the current moment shown according to an exemplary embodiment;

[0064] Figure 8 It is a flowchart of a method for obtaining the privacy evaluation value of privacy data shown according to an exemplary embodiment;

[0065] Figure 9 It is a flowchart of another method for constructing an adaptive homomorphic encryption parameter model shown according to an exemplary embodiment;

[0066] Figure 10 It is a block diagram of a privacy data encryption system of an information service platform shown according to an exemplary embodiment;

[0067] Figure 11 It is a block diagram of an information service platform shown according to an exemplary embodiment. Detailed implementation manners

[0068] To clearly illustrate the technical features of this solution, the present application will be elaborated in detail below through specific implementation manners and in conjunction with the accompanying drawings.

[0069] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not used to limit the protection scope of the present application.

[0070] It should be understood that the various steps recited in the method embodiments of the present application can be executed in different orders and / or executed in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this regard.

[0071] As used herein, the term "comprising" and its variations are open-ended, i.e., "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0072] It should be noted that the concepts such as "first", "second", etc. mentioned in the present application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0073] It should be noted that the modifications of "one" and "multiple" mentioned in the present application are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more". In the description of the present application, unless otherwise stated, "multiple" means two or more, and other quantifiers are similar; "at least one (item)", "one (item) or more (items)" or their similar expressions refer to any combination of these items (items), including any combination of single item (item) or plural items (items). For example, at least one (item) a can represent any number of a; for another example, one (item) or more (items) of a, b, and c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c can be single or multiple; "and / or" is a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural.

[0074] In the embodiments of the present application, although operations or steps are described in a specific order in the drawings, it should not be construed as requiring these operations or steps to be performed in the specific order shown or in a serial order, or requiring all the operations or steps shown to obtain the desired result. In the embodiments of the present application, these operations or steps can be performed serially; they can also be performed in parallel; or a part of these operations or steps can be performed.

[0075] Meanwhile, it can be understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) should comply with the requirements of the corresponding laws, regulations and related provisions. The present application will be described below with reference to specific embodiments.

[0076] Figure 1 It is a flowchart of a method for encrypting privacy data of an information service platform shown according to an exemplary embodiment. As Figure 1 shown, the embodiments of the present application provide a method for encrypting privacy data of an information service platform, and the method may include the following steps:

[0077] In step S10, obtain the operation status data of the information service platform and the privacy data of the user.

[0078] In this step, obtain the operation status data of the information service platform and the privacy data of the user. Exemplarily, monitoring tools (such as Prometheus, Grafana) can be used to obtain the operation status data of the information service platform in real time. The operation status data may include: server load: the usage rates of CPU, disk and network; response time: the average response time and latency of various requests; user request frequency: the number of requests per second, the most active users and access paths, etc.; log records: collect access logs and analyze the usage habits, operation frequencies and abnormal behaviors of users.

[0079] The acquisition of the user's privacy data may include: user registration / login information: collect the basic information (such as name, email, phone number) provided by the user when registering on the platform; transaction data: record the payment information (such as credit card number, payment method) of the user on the platform; user behavior data: record the operation records of the user (such as search history, browsing history).

[0080] The preprocessing of the data may include data cleaning: remove duplicate data and invalid data (such as missing values); format the information input by the user (for example, unify the date format). Data standardization: perform standardization processing on numerical data to ensure that various indicators are at the same magnitude for subsequent analysis.

[0081] Mark and classify the user's privacy data: Classify the user's payment information and data related to ID numbers as Class I data; classify communication records and data related to personal preferences as Class II data; classify the general usage habits of the user when using the information service platform as Class III data. Add labels to different data categories through data tagging to facilitate subsequent processing and the selection of encryption strategies.

[0082] In step S20, construct an adaptive homomorphic encryption parameter model according to the operating state data and the privacy data.

[0083] In this step, construct an adaptive homomorphic encryption parameter model according to the operating state data and the privacy data. Exemplarily, first construct the state surface of the information service platform according to the load data of the server of the information service platform, then obtain the usage rate gradient of the server according to the state surface, then obtain the encryption state parameter of the server according to the usage rate gradient of the server, then evaluate the user privacy data to obtain the privacy valuation of the user privacy data, and finally construct an adaptive homomorphic encryption parameter model according to the encryption state parameter and the privacy valuation.

[0084] In step S30, encrypt the user's privacy data by using the adaptive homomorphic encryption parameter model.

[0085] In this step, encrypt the user's privacy data by using the adaptive homomorphic encryption parameter model. The model combines the operating state of the information service platform and the privacy valuation of the user privacy data, and automatically adjusts the encryption intensity (the amount of encryption operations) according to the resource load of the platform and the data privacy requirements. Exemplarily, the encryption process of the user's privacy data can be implemented according to the adaptive homomorphic encryption parameter model to generate ciphertext, ensure that its format is compatible with the storage system, and record any exceptions during the generation process. Generate a key that meets security standards. The key should have sufficient randomness and complexity, and use a secure hardware module (HSM, Hardware Security Module) or a cloud key management service (KMS, Key Management Service) to store the key to ensure that the key can only be accessed by authorized services. Use an encrypted database or a dedicated ciphertext storage scheme to save the ciphertext to ensure the security of the data in static storage. Set up a monitoring mechanism to monitor the usage and security status of the encrypted data in real time to ensure that any abnormal behavior can be identified in time; collect the feedback of users on privacy protection measures, analyze the feedback data, and adjust the encryption strategy of the adaptive homomorphic encryption model to improve security and user experience.

[0086] In summary, the embodiments of the present application provide a method for encrypting privacy data of an information service platform. The method includes: obtaining the operation status data of the information service platform and the privacy data of users; constructing an adaptive homomorphic encryption parameter model according to the operation status data and the privacy data; and encrypting the privacy data of users by using the adaptive homomorphic encryption parameter model. By combining the operation status of the information service platform and the privacy evaluation of user privacy data, the embodiments of the present application adopt a dynamically adjusted adaptive homomorphic encryption model, so as to automatically adjust the encryption strength according to the resource load and data privacy requirements of the platform. In this way, the encryption efficiency and effect can be improved on the premise of protecting user privacy, and the platform performance bottleneck caused by excessive load during the encryption process can be avoided.

[0087] Figure 2 FIG. is a flowchart of a method for constructing an adaptive homomorphic encryption parameter model shown according to an exemplary embodiment. As Figure 2 shown, the construction of the adaptive homomorphic encryption parameter model may include the following steps:

[0088] In step S201, a state surface of the information service platform is constructed according to the load data of the server of the information service platform.

[0089] In this step, a state surface of the information service platform is constructed according to the load data of the server of the information service platform. Exemplarily, the usage rate of components of the server may be obtained first. The components include: CPU, disk, and network. Then, the usage rates of the components of the server are sorted to obtain a component usage rate ranking. Then, a three-dimensional rectangular coordinate system is constructed with time as the horizontal axis, the component usage rate ranking number as the vertical axis, and the usage rate as the vertical axis. Then, according to the usage rates of the components of the server, the usage rate curves of the components of the server are obtained. Finally, the usage rate curves of the components are mapped into the three-dimensional rectangular coordinate system, and the least squares method is used to perform surface fitting on all the curves in the three-dimensional rectangular coordinate system to finally obtain the state surface of the information service platform.

[0090] In step S202, the usage rate gradient of the server is obtained according to the state surface.

[0091] In this step, the usage rate gradient of the server is obtained according to the state surface. Exemplarily, the disk usage rate at the k-th moment of the server may be obtained first, then the CPU usage rate at the k-th moment of the server is obtained, and then the network usage rate at the k-th moment of the server is obtained. Finally, according to the disk usage rate, CPU usage rate, and network usage rate, the usage rate gradient of the server is obtained.

[0092] In step S203, the encryption state parameter of the server is obtained according to the usage rate gradient of the server.

[0093] In this step, according to the usage rate gradient of the server, the encryption status parameter of the server is obtained. Exemplarily, the CPU usage rate of the server at the current moment can be obtained first, then the change rate of the usage rate gradient at the current moment of the server is obtained, then the deviation rate of the change of the usage rate gradient at the current moment of the server is obtained, and finally, according to the CPU usage rate, the change rate of the usage rate gradient, and the deviation rate of the change of the usage rate gradient at the current moment, the encryption status parameter of the server is obtained.

[0094] In step S204, the privacy data is evaluated to obtain the privacy valuation of the privacy data.

[0095] In this step, the privacy data is evaluated to obtain the privacy valuation of the privacy data. Exemplarily, the privacy data can be classified first to obtain the classified privacy data, then the information entropy of the classified privacy data is obtained, and finally, according to the information entropy of the classified privacy data, the privacy valuation of the privacy data is obtained.

[0096] In step S205, an adaptive homomorphic encryption parameter model is constructed according to the encryption status parameter and the privacy valuation.

[0097] In this step, an adaptive homomorphic encryption parameter model is constructed according to the encryption status parameter and the privacy valuation. The adaptive homomorphic encryption parameter model includes the number of addition operations of the encryption algorithm for the privacy data, the number of multiplication operations of the encryption algorithm for the privacy data, and the total number of arithmetic operations of the encryption algorithm for the privacy data. Exemplarily, the scatter plot of all users' privacy data can be obtained first, then according to the scatter plot of the privacy data, the DBSCAN clustering algorithm is used to cluster all users in the information service platform to obtain several user clusters. Then, for any one user cluster, based on the privacy valuation of the privacy data, and using a support vector machine to classify each type of privacy data in the user cluster to obtain several encrypted clusters. Then, the accumulated value of the privacy valuations of all privacy data in the encrypted cluster is obtained, then the first data volume of all privacy data in the encrypted cluster is obtained, then the second data volume of all privacy data in the user cluster where the encrypted cluster is located is obtained, and then according to the encryption status parameter and the first data volume, the number of addition operations of the encryption algorithm for all privacy data in the encrypted cluster is obtained. Then, according to the encryption status parameter, the accumulated value, the first data volume, and the second data volume, the number of multiplication operations of the encryption algorithm for all privacy data in the encrypted cluster is obtained. Finally, according to the sum value of the number of addition operations and the number of multiplication operations, the total number of arithmetic operations of the encryption algorithm for all privacy data in the encrypted cluster is obtained. This model can automatically adjust the encryption intensity (the amount of encryption operations) according to the resource load situation of the platform and the data privacy valuation.

[0098] Figure 3It is a flowchart of a method for constructing a state surface of an information service platform shown according to an exemplary embodiment. As Figure 3 shown, constructing the state surface of the information service platform according to the load data of the server of the information service platform may include the following steps:

[0099] In step S2011, obtain the utilization rates of components of the server, where the components include: CPU, disk, and network.

[0100] In this step, obtain the utilization rates of components of the server, where the components include: CPU, disk, and network. Exemplarily, the utilization rates of components of the server may include CPU utilization rate, disk utilization rate, and network utilization rate.

[0101] In step S2012, sort the utilization rates of components of the server to obtain a sorted order of component utilization rates.

[0102] In this step, sort the utilization rates of components of the server, such as sorting the CPU utilization rate, disk utilization rate, and network utilization rate, to obtain a sorted order of component utilization rates. Exemplarily, this sorting can be from high to low or from low to high.

[0103] In step S2013, construct a three-dimensional rectangular coordinate system with time as the horizontal axis, the serial number of the sorted order of component utilization rates as the vertical axis, and the utilization rate as the vertical axis.

[0104] In this step, construct a three-dimensional rectangular coordinate system with time as the horizontal axis, the serial number of the sorted order of component utilization rates as the vertical axis, and the utilization rate as the vertical axis.

[0105] In step S2014, obtain the utilization rate curves of components of the server according to the utilization rates of components of the server.

[0106] In this step, obtain the utilization rate curves of components of the server according to the utilization rates of components of the server. Exemplarily, the utilization rate curves of components of the server may include CPU utilization rate curve, disk utilization rate curve, and network utilization rate curve.

[0107] In step S2015, map the utilization rate curves of the components to the three-dimensional rectangular coordinate system, and perform surface fitting on all the curves in the three-dimensional rectangular coordinate system using the least squares method to obtain the state surface of the information service platform.

[0108] In this step, the utilization rate curves of the components can be mapped to the three-dimensional rectangular coordinate system, and surface fitting is performed on all the curves in the three-dimensional rectangular coordinate system using the least squares method, so as to obtain the state surface of the information service platform.

[0109] Figure 4 is a flowchart of a method for obtaining the usage rate gradient of a server shown according to an exemplary embodiment. As Figure 4 shown, obtaining the usage rate gradient of the server may include the following steps:

[0110] In step S2021, obtain the disk usage rate of the server at the k-th moment.

[0111] In this step, obtain the disk usage rate of the server at the k-th moment .

[0112] In step S2022, obtain the CPU usage rate of the server at the k-th moment.

[0113] In this step, obtain the CPU usage rate of the server at the k-th moment .

[0114] In step S2023, obtain the network usage rate of the server at the k-th moment.

[0115] In this step, obtain the network usage rate of the server at the k-th moment .

[0116] In step S2024, obtain the usage rate gradient of the server according to the disk usage rate, the CPU usage rate, and the network usage rate.

[0117] In this step, according to the disk usage rate of the server , CPU usage rate and network usage rate , obtain the usage rate gradient of the server at the k-th moment . Exemplarily, the usage rate gradient of the server at the k-th moment can be obtained by the following formula:

[0118] Formula 1

[0119] where the usage rate gradient reflects the focus direction of the server when processing user privacy data at the corresponding k moment. When the usage rate gradient of the server is larger, the server is more inclined to encrypt the calculation process at the corresponding k moment; conversely, the server is more inclined to the data transmission and storage process at the corresponding k moment. It should be noted that the sum of the disk usage rate and the network usage rate is not zero.

[0120] Figure 5 is a flowchart of a method for obtaining the encryption state parameter of a server shown according to an exemplary embodiment. AsFigure 5 As shown, obtaining the encryption status parameter of the server may include the following steps:

[0121] In step S2031, obtain the CPU usage rate of the server at the current moment.

[0122] In this step, obtain the CPU usage rate of the server at the current moment .

[0123] In step S2032, obtain the rate of change of the usage rate gradient of the server at the current moment.

[0124] In this step, obtain the rate of change of the usage rate gradient of the server at the current moment . Exemplarily, the usage rate gradients at all moments within a preset proximity range of the current moment may be obtained first; for example, the preset proximity range may be the range of 10 adjacent moments of the current moment, and then a linear fitting is performed on the usage rate gradients to obtain the straight line of the rate of change of the usage rate gradient at the current moment; then, the slope of the straight line of the rate of change of the usage rate gradient is used as the rate of change of the usage rate gradient of the server at the current moment .

[0125] In step S2033, obtain the deviation rate of the change of the usage rate gradient of the server at the current moment.

[0126] In this step, obtain the deviation rate of the change of the usage rate gradient of the server at the current moment . Exemplarily, the Euclidean distance data between the usage rate gradients at all moments and the straight line of the rate of change of the usage rate gradient may be obtained first; then, the standard deviation of these Euclidean distance data is obtained; then, this standard deviation is used as the deviation rate of the change of the usage rate gradient of the server at the current moment .

[0127] In step S2034, obtain the encryption status parameter of the server according to the CPU usage rate, the rate of change of the usage rate gradient, and the deviation rate of the change of the usage rate gradient at the current moment.

[0128] In this step, according to the CPU usage rate at the current moment , the rate of change of the usage rate gradient , and the deviation rate of the change of the usage rate gradient , obtain the encryption status parameter of the server . Exemplarily, the encryption status parameter of the server may be obtained by the following formula:

[0129] Formula 2

[0130] where the encryption status parameter It reflects the encryption load situation when the information service platform encrypts user privacy data at the current moment, that is, the encryption status parameter The larger it is, the greater the data processing pressure faced by the CPU component of the server when the information service platform encrypts the user's privacy data. Therefore, in order to reduce the encryption calculation pressure of the CPU component during the encryption process of the information service platform, the encryption intensity (computation amount) used when encrypting using the homomorphic encryption algorithm currently should be dynamically adjusted to improve the encryption efficiency and effect, and avoid the platform performance bottleneck caused by excessive load during the encryption process.

[0131] Figure 6 It is a flowchart of a method for obtaining the rate of change of the usage rate gradient of the server at the current moment according to an exemplary embodiment. As Figure 6 shown, obtaining the rate of change of the usage rate gradient of the server at the current moment may include the following steps:

[0132] In step S20321, obtain the usage rate gradients at all moments within the preset adjacent range at the current moment.

[0133] In this step, obtain the usage rate gradients at all moments within the preset adjacent range at the current moment. Exemplarily, the preset adjacent range may be a range of 10 adjacent moments at the current moment.

[0134] In step S20322, perform a linear fitting on the usage rate gradients to obtain the usage rate gradient change line at the current moment.

[0135] In this step, perform a linear fitting on the usage rate gradients to obtain the usage rate gradient change line at the current moment. Exemplarily, the least squares method can be used to perform a linear fitting on the usage rate gradients to obtain the usage rate gradient change line at the current moment.

[0136] In step S20323, take the slope of the usage rate gradient change line as the rate of change of the usage rate gradient of the server at the current moment.

[0137] In this step, the slope of the usage rate gradient change line can be taken as the rate of change of the usage rate gradient of the server at the current moment. Exemplarily, the rate of change of the usage rate gradient reflects the change trend of the encryption load of the server within a short time range where the current moment is located.

[0138] Figure 7 It is a flowchart of a method for obtaining the deviation rate of the usage rate gradient change of the server at the current moment according to an exemplary embodiment. As Figure 7 shown, obtaining the deviation rate of the usage rate gradient change of the server at the current moment may include the following steps:

[0139] In step S20331, obtain the Euclidean distance data between the utilization rate gradients at all the moments and the straight line of the utilization rate gradient change.

[0140] In this step, obtain the Euclidean distance data between the utilization rate gradients at all the moments within a preset proximity range and the straight line of the utilization rate gradient change.

[0141] In step S20332, obtain the standard deviation of the Euclidean distance data.

[0142] In this step, obtain the standard deviation of these Euclidean distance data.

[0143] In step S20333, use the standard deviation as the deviation rate of the utilization rate gradient change at the current moment of the server.

[0144] In this step, use the standard deviation obtained in the previous step as the deviation rate of the utilization rate gradient change at the current moment of the server. Exemplarily, the deviation rate of the utilization rate gradient change reflects the confidence level of describing the encrypted load state of the CPU component of the server at the current moment using the utilization rate gradient change rate. The smaller the deviation rate of the utilization rate gradient change, the higher the confidence level. Conversely, the lower the confidence level.

[0145] Figure 8 is a flowchart of a method for obtaining a privacy valuation of private data shown according to an exemplary embodiment. As Figure 8 shown, the evaluating the private data to obtain the privacy valuation of the private data may include the following steps:

[0146] In step S2041, classify the private data to obtain classified private data.

[0147] In this step, classify the private data to obtain classified private data. Exemplarily, the method of classifying the private data to obtain classified private data may refer to the description of the embodiment of step S10 above, and details are not described herein again in this application.

[0148] In step S2042, obtain the information entropy of the classified private data.

[0149] In this step, obtain the information entropy of the i-th type of classified private data .

[0150] In step S2043, obtain the privacy valuation of the private data according to the information entropy of the classified private data.

[0151] In this step, according to the information entropy of the i-th type of classified private data , obtain the privacy valuation of the i-th type of private data . Exemplarily, the privacy valuation of the i-th type of privacy data can be obtained by the following formula:

[0152] Formula 3

[0153] . Exemplarily, where i can be equal to 1, 2, or 3. The privacy valuation reflects the encryption strength required when the i-th type of user privacy data is encrypted on the information service platform, that is, the privacy valuation is larger, indicating that the encryption strength required for the i-th type of user privacy data during the encryption process is higher. Thus, by dynamically adjusting the encryption calculation strength of different privacy data, the total amount of encryption operations can be reduced while ensuring data security, improving the efficiency and effect of the encryption algorithm.

[0154] Figure 9 is a flowchart showing another method for constructing an adaptive homomorphic encryption parameter model according to an exemplary embodiment. As Figure 9 shown, the adaptive homomorphic encryption parameter model includes the number of addition operations of the encryption algorithm for the privacy data, the number of multiplication operations of the encryption algorithm for the privacy data, and the total number of operation operations of the encryption algorithm for the privacy data. The constructing of the adaptive homomorphic encryption parameter model may include the following steps:

[0155] In step S2051, a vector formed by the privacy valuations corresponding to different types of privacy data in the privacy data is used as a data point to obtain a scatter plot of all user privacy data.

[0156] In this step, a vector formed by the privacy valuations corresponding to different types of privacy data in the privacy data is used as a data point to obtain a scatter plot of all user privacy data. Exemplarily, a vector formed by the privacy valuations corresponding to the I, II, and III types of privacy data of users can be used as a data point to obtain a scatter plot of all user privacy data.

[0157] In step S2052, according to the scatter plot of the privacy data, the DBSCAN clustering algorithm is used to cluster all users in the information service platform to obtain several user clusters.

[0158] In this step, according to the scatter plot of the privacy data, the DBSCAN clustering algorithm is used to cluster all users in the information service platform to obtain several user clusters.

[0159] In step S2053, for any one of the user clusters, based on the privacy valuation of the privacy data and using a support vector machine to classify each type of privacy data in the user cluster, several encryption clusters are obtained.

[0160] In this step, for any user cluster, based on the privacy valuation of the privacy data and using a support vector machine to classify each type of privacy data in the user cluster, a number of encrypted clusters are obtained. Exemplarily, users with similar privacy valuations of privacy data can be grouped into one category through a clustering method, and further classify the privacy data of the users in the user cluster. Adaptive homomorphic encryption for different types of privacy data can be achieved by partitioning the privacy data in the information service platform. By balancing the encryption load of the CPU component in the server of the information service platform during the encryption process and the encryption requirements of different users and different privacy data in the information service platform, adaptive encryption is performed to improve the encryption efficiency and encryption effect.

[0161] In step S2054, obtain the cumulative value of the privacy valuations of all the privacy data in the encrypted cluster.

[0162] In this step, obtain the cumulative value of the privacy valuations of all the privacy data in encrypted cluster j 。

[0163] In step S2055, obtain the first data volume of all the privacy data in the encrypted cluster.

[0164] In this step, obtain the first data volume of all the privacy data in encrypted cluster j ,First data volume is the data volume of all the privacy data in encrypted cluster j.

[0165] In step S2056, obtain the second data volume of all the privacy data in the user cluster where the encrypted cluster is located.

[0166] In this step, obtain the second data volume of all the privacy data in the user cluster where encrypted cluster j is located ,Second data volume is the data volume of all the privacy data in the user cluster where encrypted cluster j is located.

[0167] In step S2057, according to the encryption status parameter and the first data volume, obtain the number of addition operations of the encryption algorithm for all the privacy data in the encrypted cluster.

[0168] In this step, according to the encryption status parameter and the first data volume ,obtain the number of addition operations of the encryption algorithm for all the privacy data in encrypted cluster j 。Exemplarily, the number of addition operations of the encryption algorithm for all the privacy data in encrypted cluster j can be obtained by the following formula:

[0169] Formula 4

[0170] Among them, represents the logarithmic function with base 2.

[0171] In step S2058, according to the encryption status parameter, the accumulated value, the first data volume, and the second data volume, obtain the number of multiplication operations of the encryption algorithm for all the privacy data in the encryption cluster.

[0172] In this step, according to the encryption status parameter , the accumulated value , the first data volume and the second data volume , obtain the number of multiplication operations of the encryption algorithm for all the privacy data in encryption cluster j . Exemplarily, the number of multiplication operations of the encryption algorithm for all the privacy data in encryption cluster j can be obtained by the following formula:

[0173] Formula 5

[0174] Among them, represents the floor function.

[0175] In step S2059, according to the sum value of the number of addition operations and the number of multiplication operations, obtain the total number of arithmetic operations of the encryption algorithm for all the privacy data in the encryption cluster.

[0176] In this step, according to the number of addition operations and the number of multiplication operations of the sum value, obtain the total number of arithmetic operations of the encryption algorithm for all the privacy data in encryption cluster j . Exemplarily, the total number of arithmetic operations of the encryption algorithm for all the privacy data in encryption cluster j can be obtained by the following formula:

[0177] Formula 6

[0178] The adaptive homomorphic encryption parameter model adaptively processes the number of addition operations and the number of multiplication operations in the fully homomorphic encryption algorithm by combining the running state of the information service platform and the privacy evaluation of the currently processed privacy data, can effectively predict the server load demand, and dynamically adjusts the encryption strength during the encryption process, optimizes the utilization of resources, and improves the encryption efficiency and encryption effect.

[0179] In an exemplary embodiment, the present application further provides a computer-readable storage medium, on which computer program instructions are stored, and when the program instructions are executed by a processor, the steps of the privacy data encryption method of the information service platform provided by the present application are implemented.

[0180] Figure 10 It is a block diagram of a privacy data encryption system for an information service platform shown according to an exemplary embodiment.

[0181] As Figure 10 shown, an embodiment of the present application provides a privacy data encryption system 1000 for an information service platform, including an information service platform 1100.

[0182] Figure 11 It is a block diagram of an information service platform shown according to an exemplary embodiment. For example, the information service platform 1100 can be provided as a server. Referring to Figure 11 , the information service platform 1100 includes a processing component 1122, which further includes one or more processors, and memory resources represented by a memory 1132 for storing instructions executable by the processing component 1122, such as application programs. The application programs stored in the memory 1132 can include one or more modules each corresponding to a set of instructions. In addition, the processing component 1122 is configured to execute instructions to perform the privacy data encryption method of the above-mentioned information service platform.

[0183] The information service platform 1100 may further include a power supply component 1126 configured to perform power management of the information service platform 1100, a communication component 1150 configured to connect the information service platform 1100 to a network, and an input / output interface 1158. The information service platform 1100 can operate based on an operating system stored in the memory 1132.

[0184] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program capable of being executed by a programmable electronic device. The computer program has a code portion for performing the privacy data encryption method of the above-mentioned information service platform when executed by the programmable electronic device.

[0185] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but should not be construed as a limitation on the scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the present application.

Claims

1. A privacy data encryption method for an information service platform, characterized in that, The method includes: Obtaining the operation status data of the information service platform and the privacy data of the user; Constructing an adaptive homomorphic encryption parameter model according to the operation status data and the privacy data; Using the adaptive homomorphic encryption parameter model to encrypt the privacy data of the user; The constructing of the adaptive homomorphic encryption parameter model includes: Constructing the state surface of the information service platform according to the load data of the server of the information service platform; Obtaining the usage rate gradient of the server according to the state surface; Obtaining the encryption status parameter of the server according to the usage rate gradient of the server; Evaluating the privacy data to obtain the privacy valuation of the privacy data; Constructing an adaptive homomorphic encryption parameter model according to the encryption status parameter and the privacy valuation; The obtaining of the encryption status parameter of the server includes: Obtaining the CPU usage rate of the server at the current moment; Obtaining the change rate of the usage rate gradient of the server at the current moment; Obtaining the deviation rate of the change of the usage rate gradient of the server at the current moment; Obtaining the encryption status parameter of the server according to the CPU usage rate, the change rate of the usage rate gradient, and the deviation rate of the change of the usage rate gradient at the current moment.

2. The privacy data encryption method of the information service platform according to claim 1, wherein The constructing of the state surface of the information service platform according to the load data of the server of the information service platform includes: Obtaining the usage rates of the components of the server, where the components include: CPU, disk, and network; Sorting the usage rates of the components of the server to obtain the component usage rate sorting; Taking time as the horizontal axis, the sorting number of the component usage rate as the vertical axis, and the usage rate as the vertical axis, constructing a three-dimensional rectangular coordinate system; Obtaining the usage rate curve of the components of the server according to the usage rates of the components of the server; Mapping the usage rate curve of the components to the three-dimensional rectangular coordinate system, and using the least squares method to perform surface fitting on all the curves in the three-dimensional rectangular coordinate system to obtain the state surface of the information service platform.

3. The privacy data encryption method of the information service platform according to claim 1, characterized in that, The obtaining of the usage rate gradient of the server includes: Obtaining the disk usage rate of the server at the k-th moment; Obtaining the CPU usage rate of the server at the k-th moment; Obtaining the network usage rate of the server at the k-th moment; Obtaining the usage rate gradient of the server according to the disk usage rate, the CPU usage rate, and the network usage rate.

4. The privacy data encryption method of the information service platform according to claim 1, wherein The obtaining of the change rate of the usage rate gradient of the server at the current moment includes: Obtaining the usage rate gradients of all the moments within the preset adjacent range of the current moment; Performing linear fitting on the usage rate gradients to obtain the usage rate gradient change line at the current moment; Taking the slope of the usage rate gradient change line as the change rate of the usage rate gradient of the server at the current moment.

5. The privacy data encryption method of the information service platform according to claim 4, characterized in that, The obtaining of the deviation rate of the change of the usage rate gradient of the server at the current moment includes: Obtaining the Euclidean distance data between the usage rate gradients of all the moments and the usage rate gradient change line; Obtaining the standard deviation of the Euclidean distance data; Use the standard deviation as the deviation rate of the usage rate gradient change of the server at the current moment.

6. The privacy data encryption method of the information service platform according to claim 1, wherein The evaluating the privacy data to obtain the privacy valuation of the privacy data includes: Classify the privacy data to obtain classified privacy data; Obtain the information entropy of the classified privacy data; Obtain the privacy valuation of the privacy data according to the information entropy of the classified privacy data.

7. The privacy data encryption method of the information service platform according to claim 1, wherein The adaptive homomorphic encryption parameter model includes the number of addition operations of the encryption algorithm of the privacy data, the number of multiplication operations of the encryption algorithm of the privacy data, and the total number of arithmetic operations of the encryption algorithm of the privacy data. The constructing the adaptive homomorphic encryption parameter model includes: Use the vector formed by the privacy valuations corresponding to different categories of privacy data in the privacy data as a data point to obtain the scatter plot of all users' privacy data; Cluster all users in the information service platform using the DBSCAN clustering algorithm according to the scatter plot of the privacy data to obtain several user clusters; For any one of the user clusters, classify each category of privacy data in the user cluster using a support vector machine based on the privacy valuation of the privacy data to obtain several encrypted clusters; Obtain the cumulative value of the privacy valuations of all privacy data in the encrypted cluster; Obtain the first data volume of all privacy data in the encrypted cluster; Obtain the second data volume of all privacy data in the user cluster where the encrypted cluster is located; Obtain the number of addition operations of the encryption algorithm of all privacy data in the encrypted cluster according to the encryption status parameter and the first data volume; Obtain the number of multiplication operations of the encryption algorithm of all privacy data in the encrypted cluster according to the encryption status parameter, the cumulative value, the first data volume, and the second data volume; Obtain the total number of arithmetic operations of the encryption algorithm of all privacy data in the encrypted cluster according to the sum value of the number of addition operations and the number of multiplication operations.

8. A privacy data encryption system for an information service platform, characterized in that, The system includes an information service platform, and the information service platform includes: A memory storing a computer program thereon; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-7.

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