A fitness personal privacy data encryption protection method

Abstract

This invention relates to the field of data processing technology, specifically to a method for encrypting and protecting personal fitness data. The method includes: acquiring personal fitness data from multiple users; determining multiple user categories based on the personal fitness data; processing the personal fitness data corresponding to each user category according to a preset hash function processing strategy to obtain a hash code for each user's personal fitness data, wherein the preset hash function processing strategies for each user category are different; and transmitting the hash code to a target terminal to achieve encrypted protection of the personal fitness data. In this application, the personal fitness data corresponding to different user categories can be processed according to different preset hash function processing strategies to obtain hash codes of different lengths, reducing the probability of hash collisions and thus improving the accuracy and reliability of the encrypted personal fitness data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a method for encrypting and protecting personal privacy data related to fitness. Background Technology

[0002] The fitness and bodybuilding industry has grown rapidly in recent years, and at the same time, the collection and processing of fitness data has become more widespread. Fitness apps have become a part of many people's daily lives, not only helping users track their health but also providing personalized workout plans and dietary advice.

[0003] Fitness apps typically collect users' personal fitness data, such as weight, heart rate, exercise habits, and dietary records. This highly personalized data relates to a user's health and lifestyle and is crucial for helping people improve their health and fitness levels. When this personal fitness data is illegally collected, disseminated, or stolen, it can be used for fraud or identity theft, impacting an individual's work and life, and even causing financial losses. Therefore, personal privacy rights must be fully respected and protected during data storage and transmission. Encryption technology can prevent the alteration or destruction of personal fitness data, thereby maintaining its accuracy and reliability.

[0004] In existing technologies, hash functions are commonly used to encrypt and protect personal fitness data. A hash function converts data into a fixed-length hash code, thus encrypting the data. Any slight modification to the input data will produce a different hash code. Encrypting data transmission using hash functions helps detect any tampering or corruption during storage or transmission, ensuring data integrity. Furthermore, the original data cannot be directly recovered from the hash code, preventing unauthorized access and data leakage, thus improving the protection of personal fitness data. However, because a hash function maps data of arbitrary length to a fixed-length hash code, the input space (i.e., all possible data combinations) is much larger than the output space (i.e., all possible hash values). According to the "pigeonhole principle," different inputs will inevitably produce the same output; this situation is called a hash collision. Although modern hash function designs strive to minimize the probability of hash collisions, collisions can still occur due to the algorithm of the hash function and the limited length of the hash code. Hash collisions threaten data integrity and create security vulnerabilities, thereby reducing the accuracy and reliability of encrypted personal fitness data.

[0005] Therefore, how to improve the accuracy and reliability of encrypted personal privacy data for fitness is an urgent problem to be solved. Summary of the Invention

[0006] To address the technical problem of improving the accuracy and reliability of encrypted fitness personal privacy data, the present invention aims to provide a method for encrypting and protecting fitness personal privacy data, the specific technical solution of which is as follows:

[0007] This application provides a method for encrypting and protecting personal privacy data related to fitness, the method comprising:

[0008] Obtaining personal fitness data from multiple users;

[0009] Based on the fitness personal privacy data of the aforementioned users, multiple user categories were determined;

[0010] According to the preset hash function processing strategy corresponding to each user category, the fitness personal privacy data corresponding to each user category is processed to obtain the hash code corresponding to the fitness personal privacy data of each user. The preset hash function processing strategies corresponding to each user category are different.

[0011] The hash code is transmitted to the target terminal to achieve encrypted protection of the personal privacy data related to fitness.

[0012] In some embodiments, the fitness personal privacy data includes:

[0013] Personal data, including age, gender, height, weight, and measurements;

[0014] Fitness data, including the number of workouts, workout duration, workout frequency, fitness equipment used, and fitness course completion information;

[0015] Consumer data, including member registration time, fitness class purchase information, and fitness equipment purchase information.

[0016] In some embodiments, determining multiple user categories based on the fitness personal privacy data of the multiple users includes:

[0017] Based on preset classification indicators, the multiple users are divided into multiple user groups;

[0018] The fitness personal privacy data of multiple users within each user group are sorted to obtain the data sequence of each user group.

[0019] By traversing the data sequences of each user group, the similarity between the fitness personal privacy data of each user in each user group is obtained;

[0020] Based on the similarity, users in each user group are classified to obtain the multiple user categories.

[0021] In some embodiments, classifying users within each user group based on the similarity to obtain the plurality of user categories includes:

[0022] Based on the similarity, the data distance between each user's fitness personal privacy data and other users' fitness personal privacy data is obtained;

[0023] Obtain multiple preset cluster numbers, and cluster each user's fitness personal privacy data according to each cluster number to obtain multiple clusters corresponding to each cluster number;

[0024] For each cluster number, based on the data distance, the weighted intra-cluster distance and weighted nearest inter-cluster distance of the user's fitness personal privacy data in each cluster are obtained, and the weighted profile coefficient of the user's fitness personal privacy data is obtained based on the weighted intra-cluster distance and the weighted nearest inter-cluster distance.

[0025] The average weighted profile coefficient of each cluster is obtained based on the weighted profile coefficient of each user's fitness personal privacy data.

[0026] The optimal number of clusters is determined based on the average weighted profile coefficient of each cluster corresponding to the number of clusters, and clustering is performed based on the optimal number of clusters to obtain multiple target clusters, each of which is used to indicate the user category.

[0027] In some embodiments, after determining the multiple user categories, the method further includes:

[0028] Obtain the number of users and data complexity corresponding to each user category;

[0029] Based on the number of users, determine the initial hash code length of the fitness personal privacy data corresponding to each user category;

[0030] Based on the complexity of the data, the initial hash code length is adjusted to obtain the target hash code length of the fitness personal privacy data corresponding to each user category;

[0031] Based on the target hash code length of the fitness personal privacy data corresponding to each user category, a preset hash function processing strategy corresponding to each user category is determined.

[0032] In some embodiments, after obtaining the hash code corresponding to the fitness personal privacy data of each user, the method further includes:

[0033] Obtain the first separating weight corresponding to personal data, the second separating weight corresponding to fitness data, and the third separating weight corresponding to consumption data in each user category;

[0034] Based on the first separation weight, the second separation weight, and the third separation weight, a separator symbol is inserted into the hash code to obtain a new hash code corresponding to the fitness personal privacy data of each user. The new hash code corresponding to the fitness personal privacy data of each user is used to transmit to the target terminal.

[0035] In some embodiments, obtaining the first separating weight corresponding to personal data, the second separating weight corresponding to fitness data, and the third separating weight corresponding to consumption data in each user category includes:

[0036] Obtain the first weighted profile coefficient corresponding to personal data, the second weighted profile coefficient corresponding to fitness data, and the third weighted profile coefficient corresponding to consumption data in each user category;

[0037] The first actual data quantity of the personal data is compared with the first preset data quantity to obtain the first data integrity corresponding to the personal data, and the first separation weight corresponding to the personal data is obtained based on the first weighted contour coefficient and the first data integrity.

[0038] The second actual data quantity of the fitness data is compared with the second preset data quantity to obtain the second data integrity corresponding to the fitness data, and the second separation weight corresponding to the fitness data is obtained based on the second weighted contour coefficient and the second data integrity.

[0039] The third actual data quantity of the consumption data is compared with the third preset data quantity to obtain the third data integrity corresponding to the consumption data. Based on the third weighted contour coefficient and the third data integrity, the third separation weight degree corresponding to the consumption data is obtained.

[0040] In some embodiments, transmitting the hash code to the target terminal to achieve encrypted protection of the fitness personal privacy data includes:

[0041] The fitness personal privacy data of the multiple users are encrypted using a preset asymmetric encryption algorithm to obtain encrypted data corresponding to the fitness personal privacy data of each user.

[0042] By using a preset digital signature strategy, each user is processed to obtain a data signature corresponding to each user.

[0043] The encrypted data and hash code corresponding to the fitness personal privacy data of each user, and the data signature corresponding to each user are packaged into a data packet, and the data packet is transmitted to the target terminal.

[0044] In some embodiments, the method further includes:

[0045] The target terminal performs signature verification on the received data packet;

[0046] If the signature verification fails, the target terminal discards the data packet;

[0047] When the signature verification is successful, the target terminal uses the preset hash function processing strategy to process the encrypted data to obtain a parsed hash code, compares the parsed hash code with the hash code in the data packet, and processes the encrypted data according to the comparison result.

[0048] In some embodiments, processing the encrypted data based on the comparison result includes:

[0049] When the comparison result between the parsed hash code and the hash code in the data packet is the same, the encrypted data is stored based on the user's identity information;

[0050] If the comparison result between the parsed hash code and the hash code in the data packet is different, the encrypted data is discarded and a new data packet is requested.

[0051] The present invention has the following beneficial effects:

[0052] First, the system acquires fitness personal privacy data from multiple users. Then, based on this data, it determines multiple user categories. Next, it processes the fitness personal privacy data corresponding to each user category using a pre-defined hash function processing strategy to obtain a hash code for each user's fitness personal privacy data. The pre-defined hash function processing strategies for each user category are different. Finally, the hash code is transmitted to the target terminal to achieve encrypted protection of the fitness personal privacy data. In this application, multiple user categories are determined based on the fitness privacy data of multiple users. This allows for the identification of user groups with similar characteristics or behavioral patterns, enabling the consideration of individual differences during data processing and thus improving the accuracy of the processing results. The fitness privacy data corresponding to different user categories can be processed using different preset hash function processing strategies to obtain hash codes of different lengths. This increases the distinguishability of the hash codes and improves their accuracy as data identifiers. Compared to using the same hash function processing strategy, this reduces the probability of hash collisions, strengthens the one-to-one correspondence between the original fitness privacy data and its corresponding hash code, ensures data integrity, and more reliably detects any unauthorized data modification behavior. This, in turn, improves the accuracy and reliability of the encrypted fitness privacy data. Attached Figure Description

[0053] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a schematic diagram illustrating the implementation environment of a fitness personal privacy data encryption and protection method according to an embodiment of the present invention;

[0055] Figure 2 This is a flowchart illustrating a method for encrypting and protecting personal privacy data related to fitness, provided in one embodiment of the present invention.

[0056] Figure 3 This is a schematic diagram of the structure of a fitness personal privacy data encryption and protection device provided in one embodiment of the present invention;

[0057] Figure 4 This is a schematic diagram of the structure of a computer system suitable for electronic devices provided in one embodiment of the present invention. Detailed Implementation

[0058] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a fitness personal privacy data encryption and protection method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0059] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, apparatus, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0061] The following description, in conjunction with the accompanying drawings, details a specific scheme for an encryption and protection method for personal privacy data related to fitness provided by the present invention.

[0062] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the implementation environment of a method for encrypting and protecting personal privacy data related to fitness, as provided in an embodiment of the present invention. Figure 1 As shown, the implementation environment includes a data encryption protection terminal 101, a first target terminal 102, and a second target terminal 103. The data encryption protection terminal 101 can be a terminal device equipped with a data encryption protection device, including but not limited to mobile devices, laptops, tablets, PDAs, desktop computers, etc., with local computing capabilities. The data encryption protection device can be implemented in the form of a target client, which can be a video client, instant messaging client, browser client, or other client that supports encryption protection of personal privacy data related to fitness. The data encryption protection terminal 101 can communicate with the first target terminal 102 via a network, including but not limited to wired networks and wireless networks. The wired network includes local area networks (LANs), metropolitan area networks (MANs), and wide area networks (WANs), while the wireless network includes Bluetooth, Wi-Fi, and other networks that enable wireless communication. The data encryption protection terminal 101 may include, but is not limited to, a human-computer interaction screen, a processor, and a memory. The processor can be used, but is not limited to, to respond to human-computer interaction operations, execute corresponding operations, or generate corresponding instructions.

[0063] As an optional approach, the data encryption protection terminal 101 can be a mobile phone, which can collect the fitness personal privacy data of multiple users in real time; the first target terminal 102 can also be a mobile phone; the second target terminal 103 is a server, which can be a single server, a server cluster composed of multiple servers, or a cloud server. The above is only an example, and no limitation is made in this embodiment.

[0064] As an alternative approach, the following steps of the fitness personal privacy data encryption protection method can be performed on the data encryption protection terminal 101:

[0065] Obtaining personal fitness data from multiple users;

[0066] Based on the fitness personal privacy data of the aforementioned users, multiple user categories were determined;

[0067] According to the preset hash function processing strategy corresponding to each user category, the fitness personal privacy data corresponding to each user category is processed to obtain the hash code corresponding to the fitness personal privacy data of each user. The preset hash function processing strategies corresponding to each user category are different.

[0068] The hash code is transmitted to the target terminal to achieve encrypted protection of the personal privacy data related to fitness.

[0069] Based on the above approach, multiple user categories are determined using the fitness privacy data of multiple users. This allows for the identification of user groups with similar characteristics or behavioral patterns, enabling the processing of data to account for individual differences and improve the accuracy of the processing results. The fitness privacy data corresponding to different user categories can be processed using different preset hash function processing strategies to obtain hash codes of varying lengths. This increases the distinguishability of the hash codes and improves their accuracy as data identifiers. Compared to using the same hash function processing strategy, this reduces the probability of hash collisions, resulting in a stronger one-to-one correspondence between the original fitness privacy data and its corresponding hash code. This ensures data integrity while also more reliably detecting any unauthorized data modification, thereby improving the accuracy and reliability of the encrypted fitness privacy data.

[0070] As an optional example, this embodiment does not limit the subject executing the above-mentioned fitness personal privacy data encryption and protection method. The above-mentioned fitness personal privacy data encryption and protection method can be executed on the data encryption and protection terminal 101. For example, if the data encryption and protection terminal 101 is a mobile phone, some or all of the steps of the above-mentioned fitness personal privacy data encryption and protection method can be executed on the mobile phone.

[0071] The above section introduced the exemplary implementation environment of the technical solution of this application. Next, we will continue to introduce the method of encrypting and protecting personal privacy data related to fitness.

[0072] To address the problem of improving the accuracy and reliability of encrypted fitness personal privacy data in the prior art, embodiments of this application propose a method for encrypting and protecting fitness personal privacy data, a device for encrypting and protecting fitness personal privacy data, an electronic device, a computer-readable storage medium, and a computer program product, which will be described in detail below.

[0073] Please see Figure 2 , Figure 2 This is a flowchart illustrating a method for encrypting and protecting personal privacy data related to fitness, provided in one embodiment of the present invention. This method can be applied to... Figure 1 The implementation environment is shown. It should be understood that this method can also be applied to other exemplary implementation environments and specifically executed by devices in other implementation environments. This embodiment does not limit the implementation environment to which the method is applicable.

[0074] like Figure 2As shown, in an exemplary embodiment, the method for encrypting and protecting personal privacy data related to fitness includes at least steps S210 to S240, which are described in detail below:

[0075] In step S210, fitness personal privacy data of multiple users are obtained.

[0076] One data encryption protection terminal 101 can be used by multiple users, and each user can register an ID (Identity Document) on the data encryption protection terminal 101. When users use the fitness application installed on the data encryption protection terminal 101 for fitness and bodybuilding, the data encryption protection terminal 101 obtains each user's personal fitness privacy data in real time.

[0077] In step S220, multiple user categories are determined based on the fitness personal privacy data of the multiple users.

[0078] A hash function is an algorithm that maps input data of arbitrary length to a hash value of fixed length. However, due to the finite hash space and the infinite length of input data, the output length is fixed. Therefore, theoretically, there is a high probability that different inputs will produce the same hash result. This hash collision is accidental. When the characteristics of the dataset do not match the design of the hash function, the probability of hash collisions increases significantly, leading to increased query time and complexity, increased memory usage, reduced storage efficiency, destruction of the randomness of the hash function, and potential performance degradation and data integrity breaches. User data in fitness applications is complex and diverse. By analyzing users' personal fitness data, similarities can be obtained across different user data sequences. Based on these similarities, users can be categorized into different user groups, allowing for the determination of different hash function processing strategies for each user group.

[0079] For example, before determining multiple user categories based on the fitness privacy data of multiple users, the fitness privacy data can be preprocessed. Preprocessing may include removing duplicates, filling in missing values, correcting erroneous data, and may also include standardizing numerical data to make them on a common scale.

[0080] For example, cluster analysis, user profiling, and classification models can be used to analyze the fitness personal privacy data of multiple users to determine multiple user categories. Cluster analysis can include: K-means clustering, a commonly used unsupervised learning method that groups users according to similarity, selecting an appropriate K value (number of categories), and using Euclidean distance or other distance metrics to evaluate the similarity between data points; hierarchical clustering, which can construct a hierarchical structure among users, helping to discover more complex category relationships; and DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which can identify clusters of arbitrary shapes and discover these clusters in noisy data. User profiling can include: behavioral pattern analysis, analyzing users' exercise habits, preferences, etc., to form user behavior patterns; and health status assessment, assessing users' health status based on physiological indicators such as BMI (Body Mass Index) and heart rate. Classification models can use supervised learning methods for classification, such as logistic regression, support vector machines, and decision trees.

[0081] For example, user categories could include: beginner fitness enthusiasts, who exercise occasionally and have little knowledge of fitness; intermediate fitness enthusiasts, who exercise regularly and have some fitness experience and knowledge; and advanced fitness enthusiasts, who exercise frequently and may have a professional training background.

[0082] In step S230, the fitness personal privacy data corresponding to each user category is processed according to the preset hash function processing strategy corresponding to each user category to obtain the hash code corresponding to the fitness personal privacy data of each user. The preset hash function processing strategies corresponding to each user category are different.

[0083] For example, a hash function processing strategy suitable for each user category should be designed. The design of these hash function processing strategies should take into account the characteristics of the data and security requirements. For instance, a simple but effective hash function can be used to quickly process data for beginner fitness enthusiasts; while a more complex and secure hash function can be used to ensure data security for professional athletes.

[0084] In step S240, the hash code is transmitted to the target terminal to achieve encrypted protection of the fitness personal privacy data.

[0085] For example, the hash code can be encapsulated in a secure data packet, which may also include metadata (such as the sending time and the receiver's identifier). The hash code itself can also be encrypted, especially when the transmitted data contains sensitive information; for example, symmetric or asymmetric encryption can be used to encrypt the hash code. The target terminal can receive the encrypted data packet, then decrypt it, extract the hash code, verify data integrity, and store the hash code.

[0086] As can be seen from steps S210 to S240 above, the solution proposed in this embodiment determines multiple user categories based on the fitness personal privacy data of multiple users. This allows for the identification of user groups with similar characteristics or behavioral patterns, enabling the consideration of individual differences when processing data and thus improving the accuracy of the processing results. The fitness personal privacy data corresponding to different user categories can be processed according to different preset hash function processing strategies to obtain hash codes of different lengths, increasing the distinguishability of the hash codes and improving the accuracy of the hash codes as data identifiers. Compared with using the same hash function processing strategy for data processing, the probability of hash collisions is reduced, and the one-to-one correspondence between the original fitness personal privacy data and its corresponding hash code is stronger. This ensures data integrity while also more reliably detecting any unauthorized data modification behavior, thereby improving the accuracy and reliability of the encrypted fitness personal privacy data.

[0087] In one embodiment of this application, the fitness personal privacy data includes:

[0088] Personal data, including age, gender, height, weight, and measurements;

[0089] Fitness data, including the number of workouts, workout duration, workout frequency, fitness equipment used, and fitness course completion information;

[0090] Consumer data, including member registration time, fitness class purchase information, and fitness equipment purchase information.

[0091] Among them, circumference refers to the circumference measurement of various parts of the body, including waist circumference, hip circumference, chest circumference, arm circumference, thigh circumference, and calf circumference.

[0092] In one embodiment of this application, determining multiple user categories based on the fitness personal privacy data of the multiple users includes:

[0093] Based on preset classification indicators, the multiple users are divided into multiple user groups;

[0094] The fitness personal privacy data of multiple users within each user group are sorted to obtain the data sequence of each user group.

[0095] By traversing the data sequences of each user group, the similarity between the fitness personal privacy data of each user in each user group is obtained;

[0096] Based on the similarity, users in each user group are classified to obtain the multiple user categories.

[0097] The preset classification indicators can be selected based on the characteristics of personal fitness data, such as age, gender, exercise frequency, exercise type, heart rate zone, and BMI. For example, if age, exercise frequency, and BMI are selected as classification indicators, users can be initially divided into different groups. For instance: a young and active group (younger age, higher exercise frequency, normal BMI); a middle-aged health-conscious group (moderate age, moderate exercise frequency, higher BMI); and an elderly rehabilitation group (older age, lower exercise frequency, higher or lower BMI).

[0098] For example, for each user group, further segmentation can be achieved by calculating similarity. Similarity can be calculated in various ways, such as using Euclidean distance, Manhattan distance, cosine similarity, etc. For instance, the similarity between two users can be assessed by calculating numerical differences in aspects such as exercise frequency and BMI. After obtaining the similarity, users can be further segmented into more specific categories. Clustering algorithms can be used to automatically discover subgroups within a user group. These subgroups can be divided based on user similarity, thus forming more granular user categories.

[0099] For example, users can first be divided into two major user groups based on age. For each user group, fitness personal privacy data can be sorted as follows: personal data, fitness data, and consumption data. Personal data is arranged in the order of age, gender, height, weight, and body measurements, while fitness data is arranged in the order of number of workouts, workout duration, workout frequency, fitness equipment used, and fitness course completion information. After normalizing all data, a data sequence is formed. If there is no data in a corresponding position, 0 is filled in to fill the position, thus obtaining the data sequence for each user group.

[0100] For example, the similarity between the fitness personal privacy data of users in different user groups can be represented as follows:

[0101]

[0102] in, This indicates the similarity between personal privacy data related to fitness; This represents the value of the k-th data item in user 1's data sequence; This represents the value of the k-th data point in user 2's data sequence; n represents the number of data points in the data sequence.

[0103] in, This represents the difference between corresponding positions in two data sequences. The smaller the difference, the higher the similarity between the data.

[0104] in, This represents the average similarity among all data points in a data sequence. The lower the average similarity, the more similar the users are, and the greater the likelihood that they belong to the same category.

[0105] In this embodiment, users are initially divided into different groups using preset classification indicators. This ensures that users within each group share certain commonalities, allowing for more precise analysis of specific group characteristics in subsequent processing, thus improving the accuracy and precision of data processing. By sorting the data within user groups and calculating the similarity between users, the inherent connections between them can be discovered. Similarity calculation helps reveal the internal structure and patterns of user groups, thereby providing a better understanding of user behavior and needs.

[0106] In one embodiment of this application, classifying users in each user group based on the similarity to obtain the plurality of user categories includes:

[0107] Based on the similarity, the data distance between each user's fitness personal privacy data and other users' fitness personal privacy data is obtained;

[0108] Obtain multiple preset cluster numbers, and cluster each user's fitness personal privacy data according to each cluster number to obtain multiple clusters corresponding to each cluster number;

[0109] For each cluster number, based on the data distance, the weighted intra-cluster distance and weighted nearest inter-cluster distance of the user's fitness personal privacy data in each cluster are obtained, and the weighted profile coefficient of the user's fitness personal privacy data is obtained based on the weighted intra-cluster distance and the weighted nearest inter-cluster distance.

[0110] The average weighted profile coefficient of each cluster is obtained based on the weighted profile coefficient of each user's fitness personal privacy data.

[0111] The optimal number of clusters is determined based on the average weighted profile coefficient of each cluster corresponding to the number of clusters, and clustering is performed based on the optimal number of clusters to obtain multiple target clusters, each of which is used to indicate the user category.

[0112] The weighted intra-cluster distance measures the similarity of users within the same cluster, while the weighted nearest cluster distance measures the separation between different clusters. For each number of clusters, the average weighted silhouette coefficient of all users is calculated, providing a quantitative metric to evaluate the clustering effect under different cluster numbers. The cluster number with the highest average weighted silhouette coefficient is selected as the optimal cluster number, indicating the most reasonable placement of users within their respective clusters.

[0113] For example, similarity can be used as the data distance between personal fitness data. K-means clustering (a commonly used unsupervised learning algorithm) can be used to obtain target clusters. First, multiple preset numbers of clusters are selected. For each number of clusters, the k-means clustering algorithm is used to cluster the data, resulting in multiple clusters corresponding to each number of clusters. For the sample points in each cluster, the weighted distances are calculated based on the proportion of fitness data to all fitness data in the group, and the weighted distances between nearest clusters are calculated.

[0114] For example, the weighted co-cluster distance can be represented as follows:

[0115]

[0116] in, This represents the weighted cluster distance of the fitness personal privacy data of the i-th user; This represents the weight of the j-th user's data; The distance between the fitness privacy data of user i and user j is represented by m; The number of users in each cluster; This represents the cluster to which the i-th user belongs.

[0117] in, This represents the weighted distance between several data points belonging to the same cluster as the fitness personal privacy data of the i-th user and the fitness personal privacy data of the i-th user; The weights represent the distances between several data points belonging to the same cluster as the fitness privacy data of the i-th user and the fitness privacy data of the i-th user.

[0118] in, The smaller the ratio, the smaller the distance between the fitness personal privacy data of the i-th user and several data points in the same cluster. The data points in the cluster are relatively close in the feature space, have higher similarity on the selected features, and the clustering effect is better.

[0119] For example, the weighted nearest cluster distance can be represented as follows:

[0120]

[0121] in, This represents the weighted nearest cluster distance of the fitness personal privacy data of the i-th user; This represents the weight of the j-th user's data; M represents the data distance between the fitness privacy data of the i-th user and the fitness privacy data of the j-th user; M represents the distance between the i-th user and the j-th user. The number of users in each cluster; The k-th cluster is excluding the cluster containing the fitness personal privacy data of the i-th user; min represents the method of calculating the minimum value in mathematical calculations.

[0122] in, This represents the weighted distance between several data points that do not belong to the same cluster as the fitness personal privacy data of the i-th user and the fitness personal privacy data of the i-th user; This represents the weight of the distance between several data points that do not belong to the same cluster as the fitness personal privacy data of the i-th user and the fitness personal privacy data of the i-th user.

[0123] in, This represents the minimum average weighted distance between all data points in any cluster outside the cluster containing the fitness privacy data of user i and the fitness privacy data of user i. The smaller the weighted nearest cluster distance, the higher the similarity between the two clusters, indicating that their data are relatively close in the feature space.

[0124] For example, the weighted contour coefficients can be represented as follows:

[0125]

[0126] in, Represents the weighted profile coefficient of the fitness personal privacy data of the i-th user; This represents the weighted cluster distance of the fitness personal privacy data of the i-th user; represents the weighted nearest cluster distance of the fitness personal privacy data of the i-th user; max represents the maximum value calculation method in mathematical calculation.

[0127] in, express and The maximum value represents the similarity between the cluster to which the fitness personal privacy data of the i-th user belongs and other clusters.

[0128] in, This represents the difference between the weighted nearest cluster distance and the weighted intra-cluster distance of the fitness personal privacy data of the i-th user. The larger the difference, the closer the cluster is and the greater the distance between it and other clusters. The smaller the difference, the closer it is to other clusters, and the poorer the clustering effect.

[0129] For example, based on the weighted profile coefficients of each user's fitness privacy data, the average weighted profile coefficient of each cluster is obtained, and a plot of the average weighted profile coefficients corresponding to different numbers of clusters is drawn. The number of clusters corresponding to the highest average weighted profile coefficient is selected as the optimal number of clusters. Clustering is re-run using the determined optimal number of clusters to obtain multiple target clusters. Based on the multiple target clusters, the fitness privacy data is divided into several categories, resulting in several user categories, and the number of users in each user category is counted.

[0130] In this embodiment, by calculating the weighted profile coefficients of users and determining the optimal number of clusters accordingly, the quality of clustering can be significantly improved, avoiding the problem of poor clustering results caused by improper human settings. The weighted profile coefficients comprehensively consider the compactness within clusters and the separation between clusters, resulting in more reasonable clusters and a more even distribution of users within their respective clusters.

[0131] In one embodiment of this application, after determining multiple user categories, the method further includes:

[0132] Obtain the number of users and data complexity corresponding to each user category;

[0133] Based on the number of users, determine the initial hash code length of the fitness personal privacy data corresponding to each user category;

[0134] Based on the complexity of the data, the initial hash code length is adjusted to obtain the target hash code length of the fitness personal privacy data corresponding to each user category;

[0135] Based on the target hash code length of the fitness personal privacy data corresponding to each user category, a preset hash function processing strategy corresponding to each user category is determined.

[0136] The output value of a hash function is typically a fixed-length string, with the output hash code generally composed of 26 letters and 10 random Arabic numerals. By selecting the most suitable hash function processing strategy for different user categories, more efficient data processing can be achieved while increasing the difficulty of cracking. For example, determining different output hash code lengths based on the number of users in different user categories increases the complexity of the output hash code, reduces the probability of hash collisions, improves the security of hash function processing, and ultimately enhances the overall performance and security of the system.

[0137] For example, calculate the information entropy for each user category, and use the information entropy... This indicates the complexity of the data for user categories.

[0138] For example, the initial hash code length can be represented as follows:

[0139]

[0140] in, Take the integer part, representing the first integer. The initial hash code length of fitness personal privacy data corresponding to each user category; Indicates the first The number of users corresponding to each user category; log is the logarithmic function in mathematical calculations. With 36 as the base The logarithm of .

[0141] For example, the length of the target hash code can be represented as follows:

[0142]

[0143] in, Indicates the length of the target hash code for the f-th user category; Indicates the data complexity of the f-th user category; indicates the complexity of the data of the f-th user category. The initial hash code length of the fitness personal privacy data corresponding to each user category.

[0144] In this embodiment, the initial hash code length is determined based on the number of users, and the target hash code length is adjusted according to the data complexity. This optimizes resource usage while ensuring data security. For example, for categories with fewer users and simpler data, a shorter hash code can be used to save storage space and computing resources; while for categories with more users and more complex data, a longer hash code can be used to ensure sufficient security.

[0145] In one embodiment of this application, after obtaining the hash code corresponding to the fitness personal privacy data of each user, the method further includes:

[0146] Obtain the first separating weight corresponding to personal data, the second separating weight corresponding to fitness data, and the third separating weight corresponding to consumption data in each user category;

[0147] Based on the first separation weight, the second separation weight, and the third separation weight, a separator symbol is inserted into the hash code to obtain a new hash code corresponding to the fitness personal privacy data of each user. The new hash code corresponding to the fitness personal privacy data of each user is used to transmit to the target terminal.

[0148] The user data includes personal data, fitness data, and consumption data. The weight of the hash code length of each data category is calculated based on the importance of the data in the three data categories.

[0149] In this embodiment, due to the varying complexity of the data, the hash code length corresponding to each category of data is also inconsistent. By segmenting the user data to obtain hash codes, the distribution of hash codes becomes more uniform. Since the hash code segments of different users' fitness personal privacy data are located at different positions, the possibility of hash collisions can be effectively reduced, and errors are more easily identified during the verification process.

[0150] In one embodiment of this application, obtaining the first separating weight corresponding to personal data, the second separating weight corresponding to fitness data, and the third separating weight corresponding to consumption data in each of the user categories includes:

[0151] Obtain the first weighted profile coefficient corresponding to personal data, the second weighted profile coefficient corresponding to fitness data, and the third weighted profile coefficient corresponding to consumption data in each user category;

[0152] The first actual data quantity of the personal data is compared with the first preset data quantity to obtain the first data integrity corresponding to the personal data, and the first separation weight corresponding to the personal data is obtained based on the first weighted contour coefficient and the first data integrity.

[0153] The second actual data quantity of the fitness data is compared with the second preset data quantity to obtain the second data integrity corresponding to the fitness data, and the second separation weight corresponding to the fitness data is obtained based on the second weighted contour coefficient and the second data integrity.

[0154] The third actual data quantity of the consumption data is compared with the third preset data quantity to obtain the third data integrity corresponding to the consumption data. Based on the third weighted contour coefficient and the third data integrity, the third separation weight degree corresponding to the consumption data is obtained.

[0155] Among them, within the same user category, the degree of difference in data is similar, and the first weighted profile coefficient corresponding to the personal data of users, the second weighted profile coefficient corresponding to the fitness data, and the third weighted profile coefficient corresponding to the consumption data can be calculated separately.

[0156] For example, data integrity can be represented as follows:

[0157]

[0158] in, This indicates the data integrity corresponding to the d-th data category; This represents the actual number of data points in the d-th data category; This indicates the preset number of data items for the d-th data category.

[0159] When the preset number of data in the d-th data category is fixed, the larger the actual number of data in the d-th data category, the higher the data integrity.

[0160] For example, the hash code length corresponding to the new fitness personal privacy data of each user can be represented as:

[0161]

[0162] in, Indicates the length of the target hash code for the f-th user category; This represents the length of the hash code corresponding to the personal data in the f-th user category; This represents the length of the hash code corresponding to the fitness data in the f-th user category; This represents the length of the hash code corresponding to the consumption data in the f-th user category.

[0163] For example, The representation can be as follows:

[0164]

[0165] in, This indicates the first data integrity corresponding to the personal data in the f-th user category; d represents the first separating weight corresponding to the personal data in the f-th user category; d represents the d-th data category, where d=1 indicates the first data category is personal data, d=2 indicates the second data category is fitness data, and d=3 indicates the third data category is consumption data.

[0166] in, This represents the sum of the products of data integrity and weighted profile coefficients for the three data categories in the f-th user category.

[0167] in, This represents the first separator weight. The larger the first separator weight, the greater the importance of the personal data, and the longer the hash code corresponding to the personal data should be.

[0168] In this embodiment, by calculating the first, second, and third weighted profile coefficients for different data types, the importance and representativeness of each data type within the user category can be assessed more precisely. By comparing the actual data quantity with the preset data quantity, data integrity can be evaluated, helping to ensure data integrity during processing and transmission, and reducing misjudgments or analytical biases caused by missing data. The weighted profile coefficient reflects the suitability of the data in clustering, while data integrity reflects the actual usability of the data. Combining these two factors to determine the separating weights can more accurately reflect the discriminative power of different data types within the user category, thereby improving the accuracy of data processing.

[0169] In one embodiment of this application, transmitting the hash code to the target terminal to achieve encrypted protection of the fitness personal privacy data includes:

[0170] The fitness personal privacy data of the multiple users are encrypted using a preset asymmetric encryption algorithm to obtain encrypted data corresponding to the fitness personal privacy data of each user.

[0171] By using a preset digital signature strategy, each user is processed to obtain a data signature corresponding to each user.

[0172] The encrypted data and hash code corresponding to the fitness personal privacy data of each user, and the data signature corresponding to each user are packaged into a data packet, and the data packet is transmitted to the target terminal.

[0173] In this embodiment, an asymmetric encryption algorithm is used to encrypt the user's fitness personal privacy data, ensuring that even if the encrypted data is intercepted, attackers cannot easily decrypt it. The asymmetric encryption algorithm uses a public key to encrypt the data, and only the person holding the private key can decrypt it, thus improving data security. Signing the user data using a preset digital signature strategy verifies the authenticity and integrity of the data. Digital signatures are typically generated using the user's private key, and the recipient uses the public key to verify the validity of the signature, ensuring that the data has not been tampered with.

[0174] In one embodiment of this application, the method further includes:

[0175] The target terminal performs signature verification on the received data packet;

[0176] If the signature verification fails, the target terminal discards the data packet;

[0177] When the signature verification is successful, the target terminal uses the preset hash function processing strategy to process the encrypted data to obtain a parsed hash code, compares the parsed hash code with the hash code in the data packet, and processes the encrypted data according to the comparison result.

[0178] In this embodiment, signature verification ensures the data packet's origin is trustworthy. If signature verification fails, the target terminal discards the data packet, helping to prevent malicious data intrusion and potential security threats. When signature verification succeeds, the target terminal processes the encrypted data using a preset hash function processing strategy to obtain a parsed hash code. This process helps verify whether the data remains intact and untampered during transmission. By comparing the parsed hash code with the hash code in the data packet, data reliability can be further ensured.

[0179] In one embodiment of this application, processing the encrypted data based on the comparison result includes:

[0180] When the comparison result between the parsed hash code and the hash code in the data packet is the same, the encrypted data is stored based on the user's identity information;

[0181] If the comparison result between the parsed hash code and the hash code in the data packet is different, the encrypted data is discarded and a new data packet is requested.

[0182] In this embodiment, by comparing the parsed hash code with the hash code in the data packet, it can be verified whether the data has maintained its integrity during transmission, improving the reliability of data integrity verification and ensuring that only untampered data is stored. If the parsed hash code does not match the hash code in the data packet, it indicates that the data may have been tampered with during transmission. In this case, discarding the data and requesting a new data packet can prevent corrupted data from being stored, thereby improving data security.

[0183] Figure 3 This is a schematic diagram of a fitness personal privacy data encryption and protection device provided in one embodiment of the present invention. This device can be applied to... Figure 1 The implementation environment shown is not limited to this embodiment. This device can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applicable.

[0184] like Figure 3 As shown, this exemplary fitness personal privacy data encryption protection device includes:

[0185] Data acquisition module 301 is used to acquire fitness personal privacy data of multiple users;

[0186] User classification module 302 is used to determine multiple user categories based on the fitness personal privacy data of the multiple users;

[0187] The hash processing module 303 is used to process the fitness personal privacy data corresponding to each user category according to the preset hash function processing strategy corresponding to each user category, and obtain the hash code corresponding to the fitness personal privacy data of each user, wherein the preset hash function processing strategies corresponding to each user category are different.

[0188] The transmission module 304 is used to transmit the hash code to the target terminal to achieve encrypted protection of the fitness personal privacy data.

[0189] In this exemplary fitness personal privacy data encryption and protection device, multiple user categories are determined based on the fitness personal privacy data of multiple users. This allows for the identification of user groups with similar characteristics or behavioral patterns, enabling the consideration of individual differences during data processing and thus improving the accuracy of the processing results. The fitness personal privacy data corresponding to different user categories can be processed according to different preset hash function processing strategies to obtain hash codes of different lengths. This increases the distinguishability of the hash codes and improves the accuracy of the hash codes as data identifiers. Compared to using the same hash function processing strategy for data processing, it reduces the probability of hash collisions. The one-to-one correspondence between the original fitness personal privacy data and its corresponding hash code is stronger, ensuring data integrity while also more reliably detecting any unauthorized data modification behavior, thereby improving the accuracy and reliability of the encrypted fitness personal privacy data.

[0190] It should be noted that the fitness personal privacy data encryption and protection device provided in the above embodiments and the fitness personal privacy data encryption and protection method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the fitness personal privacy data encryption and protection device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0191] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, enable the electronic device to implement the fitness personal privacy data encryption protection method provided in the above embodiments.

[0192] Figure 4This is a schematic diagram of a computer system suitable for electronic devices according to an embodiment of the present invention. It should be noted that... Figure 4 The computer system 400 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0193] like Figure 4 As shown, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from Storage Unit 408 into Random Access Memory (RAM) 403, such as performing the methods described in the above embodiments. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0194] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0195] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the various functions defined in the apparatus of this application.

[0196] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0197] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0198] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0199] Another aspect of this application provides a computer-readable storage medium storing a plurality of instructions adapted for loading by a processor to perform steps in any of the fitness personal privacy data encryption and protection methods provided in the embodiments of this application. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0200] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0201] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for encrypting and protecting personal privacy data related to fitness, characterized in that, The method includes: Obtaining personal fitness data from multiple users; Based on the fitness personal privacy data of the multiple users, multiple user categories are determined; after determining the multiple user categories, the method further includes: obtaining the number of users and data complexity corresponding to each user category; determining the initial hash code length of the fitness personal privacy data corresponding to each user category based on the number of users; adjusting the initial hash code length based on the data complexity to obtain the target hash code length of the fitness personal privacy data corresponding to each user category; and determining a preset hash function processing strategy corresponding to each user category based on the target hash code length of the fitness personal privacy data corresponding to each user category. According to the preset hash function processing strategy corresponding to each user category, the fitness personal privacy data corresponding to each user category is processed to obtain the hash code corresponding to the fitness personal privacy data of each user. The preset hash function processing strategies corresponding to each user category are different, so as to obtain hash codes of different lengths for the fitness personal privacy data corresponding to different user categories. Obtain the first separating weight corresponding to personal data, the second separating weight corresponding to fitness data, and the third separating weight corresponding to consumption data in each user category; based on the first separating weight, the second separating weight, and the third separating weight, insert a separator symbol into the hash code to obtain a new hash code corresponding to the fitness personal privacy data of each user, and the new hash code corresponding to the fitness personal privacy data of each user is used to transmit to the target terminal. The process of transmitting the hash code to the target terminal to achieve encrypted protection of the fitness personal privacy data includes: encrypting the fitness personal privacy data of the multiple users using a preset asymmetric encryption algorithm to obtain encrypted data corresponding to the fitness personal privacy data of each user; performing signature processing on each user using a preset digital signature strategy to obtain a data signature corresponding to each user; packaging the encrypted data corresponding to the fitness personal privacy data of each user, the hash code, and the data signature corresponding to each user into a data packet, and transmitting the data packet to the target terminal.

2. The method for encrypting and protecting personal privacy data related to fitness as described in claim 1, characterized in that, The fitness-related personal privacy data includes: Personal data, including age, gender, height, weight, and measurements; Fitness data, including the number of workouts, workout duration, workout frequency, fitness equipment used, and fitness course completion information; Consumer data, including member registration time, fitness class purchase information, and fitness equipment purchase information.

3. The method for encrypting and protecting personal privacy data related to fitness as described in claim 1, characterized in that, The process of determining multiple user categories based on the fitness personal privacy data of the multiple users includes: Based on preset classification indicators, the multiple users are divided into multiple user groups; The fitness personal privacy data of multiple users within each user group are sorted to obtain the data sequence of each user group. By traversing the data sequences of each user group, the similarity between the fitness personal privacy data of each user in each user group is obtained; Based on the similarity, users in each user group are classified to obtain the multiple user categories.

4. The method for encrypting and protecting personal privacy data related to fitness as described in claim 3, characterized in that, The step of classifying users within each user group based on the similarity to obtain the multiple user categories includes: Based on the similarity, the data distance between each user's fitness personal privacy data and other users' fitness personal privacy data is obtained; Obtain multiple preset cluster numbers, and cluster each user's fitness personal privacy data according to each cluster number to obtain multiple clusters corresponding to each cluster number; For each cluster number, based on the data distance, the weighted intra-cluster distance and weighted nearest inter-cluster distance of the user's fitness personal privacy data in each cluster are obtained, and the weighted profile coefficient of the user's fitness personal privacy data is obtained based on the weighted intra-cluster distance and the weighted nearest inter-cluster distance. The average weighted profile coefficient of each cluster is obtained based on the weighted profile coefficient of each user's fitness personal privacy data. The optimal number of clusters is determined based on the average weighted profile coefficient of each cluster corresponding to the number of clusters, and clustering is performed based on the optimal number of clusters to obtain multiple target clusters, each of which is used to indicate the user category.

5. The method for encrypting and protecting personal privacy data related to fitness as described in claim 1, characterized in that, The step of obtaining the first separating weight corresponding to personal data, the second separating weight corresponding to fitness data, and the third separating weight corresponding to consumption data in each of the user categories includes: Obtain the first weighted profile coefficient corresponding to personal data, the second weighted profile coefficient corresponding to fitness data, and the third weighted profile coefficient corresponding to consumption data in each user category; The first actual data quantity of the personal data is compared with the first preset data quantity to obtain the first data integrity corresponding to the personal data, and the first separation weight corresponding to the personal data is obtained based on the first weighted contour coefficient and the first data integrity. The second actual data quantity of the fitness data is compared with the second preset data quantity to obtain the second data integrity corresponding to the fitness data, and the second separation weight corresponding to the fitness data is obtained based on the second weighted contour coefficient and the second data integrity. The third actual data quantity of the consumption data is compared with the third preset data quantity to obtain the third data integrity corresponding to the consumption data. Based on the third weighted contour coefficient and the third data integrity, the third separation weight degree corresponding to the consumption data is obtained.

6. The method for encrypting and protecting personal privacy data related to fitness as described in claim 1, characterized in that, The method further includes: The target terminal performs signature verification on the received data packet; If the signature verification fails, the target terminal discards the data packet; When the signature verification is successful, the target terminal uses the preset hash function processing strategy to process the encrypted data to obtain a parsed hash code, compares the parsed hash code with the hash code in the data packet, and processes the encrypted data according to the comparison result.

7. The method for encrypting and protecting personal privacy data related to fitness as described in claim 6, characterized in that, The process of processing the encrypted data based on the comparison result includes: When the comparison result between the parsed hash code and the hash code in the data packet is the same, the encrypted data is stored based on the user's identity information; If the comparison result between the parsed hash code and the hash code in the data packet is different, the encrypted data is discarded and a new data packet is requested.

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