Fitness personal privacy data encryption protection method

Abstract

The invention relates to the technical field of data processing, in particular to a fitness personal privacy data encryption protection method, which comprises the following steps: acquiring fitness personal privacy data of a plurality of users; determining a plurality of user categories according to the fitness personal privacy data of the plurality of users; according to preset hash function processing strategies corresponding to the user types, the fitness personal privacy data corresponding to the user types are processed, hash codes corresponding to the fitness personal privacy data of the users are obtained, and the preset hash function processing strategies corresponding to the user types are different; and transmitting the hash code to the target terminal to realize encryption protection of the private data of the fitness individual. According to the method and the device, the fitness individual privacy data corresponding to different user categories can be processed according to different preset hash function processing strategies to obtain hash codes with different lengths, so that the probability of hash collision is reduced, and the accuracy and the reliability of the fitness individual privacy data subjected to encryption protection are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for encrypting and protecting personal privacy data of fitness. Background Art

[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 common. Fitness apps have become a part of many people's daily lives, not only helping users track their health, but also providing personalized exercise plans and diet recommendations.

[0003] Fitness apps usually collect users' personal fitness data, such as weight, heart rate, exercise habits, and diet records. These personal fitness data are highly personal, involving the user's health status and lifestyle, and are essential to helping people improve their health and fitness. When these personal fitness data are illegally collected, disseminated, or stolen, they may be used for fraud or identity theft, which will affect the individual's work and life, and even cause financial losses. Therefore, personal privacy must be fully respected and protected during data storage and transmission. Encryption technology can prevent personal fitness data from being tampered with or destroyed, thereby maintaining the accuracy and reliability of the data.

[0004] In the prior art, fitness personal privacy data is usually encrypted and protected by hash functions. Hash functions convert data into a fixed-length hash code to encrypt data. Any slight change to the input data will generate a different hash code. Using hash functions to encrypt the data transmission process helps to detect any tampering or damage to the data during storage or transmission, ensure the integrity of the data, and cannot directly restore the original data from the hash code, which can prevent unauthorized access and data leakage, and improve the protection of fitness personal privacy data. However, since the hash function maps data of any length to a fixed-length hash code, this means that 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 "pigeon nest principle", there must be a situation where different inputs produce the same output, which is called a hash collision. Although modern hash function designs will minimize the probability of hash collisions, collisions may still occur due to the limited length of the hash function algorithm and the hash code. Hash collisions threaten the integrity of the data and create security vulnerabilities, thereby reducing the accuracy and reliability of the encrypted and protected fitness personal privacy data.

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

[0006] In order to solve the technical problem of how to improve the accuracy and reliability of encrypted and protected fitness personal privacy data, the purpose of the present invention is to provide a fitness personal privacy data encryption protection method, and the technical solution adopted is as follows: The present application embodiment provides a method for encrypting and protecting fitness personal privacy data, the method comprising: Obtaining multiple users’ personal fitness data; Determining multiple user categories based on the multiple users' personal fitness privacy data; 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 a hash code corresponding to the fitness personal privacy data of each user, wherein the preset hash function processing strategy corresponding to each user category is different; The hash code is transmitted to the target terminal to implement encryption protection of the fitness personal privacy data.

[0007] In some embodiments, the fitness personal privacy data includes: Personal data, including age, gender, height, weight, and circumference; Fitness data, including fitness times, fitness duration, fitness frequency, fitness equipment used, and fitness course completion information; Consumption data, including member registration time, fitness course purchase information, and fitness equipment purchase information.

[0008] In some embodiments, determining multiple user categories based on the multiple users' personal fitness privacy data includes: According to preset classification indicators, the multiple users are divided into multiple user groups; Sorting the fitness personal privacy data of multiple users in each of the user groups to obtain a data sequence of each of the user groups; Traversing the data sequences of each of the user groups to obtain the similarity between the fitness personal privacy data of each user in each of the user groups; According to the similarities, the users in each of the user groups are classified to obtain the multiple user categories.

[0009] In some embodiments, the classifying the users in each of the user groups according to the similarities to obtain the multiple user categories includes: According to the similarity, the data distance between the fitness personal privacy data of each user and the fitness personal privacy data of other users is obtained; Acquire multiple preset numbers of clusters, and cluster the fitness personal privacy data of each user according to each number of clusters to obtain multiple clusters corresponding to each number of clusters; For each number of clusters, according to the data distance, obtain the weighted same-cluster distance and the weighted nearest inter-cluster distance of the user's fitness personal privacy data in each cluster, and according to the weighted same-cluster distance and the weighted nearest inter-cluster distance, obtain the weighted contour coefficient of the fitness personal privacy data of each user; According to the weighted silhouette coefficient of the fitness personal privacy data of each user, an average weighted silhouette coefficient of each cluster is obtained; An optimal number of clusters is determined according to average weighted silhouette coefficients of the clusters corresponding to the optimal number of clusters, and clustering is performed according to the optimal number of clusters to obtain a plurality of target clusters, each of which is used to indicate the user category.

[0010] In some embodiments, after determining the multiple user categories, the method further includes: Obtaining the number of users and data complexity corresponding to each of the user categories; Determine, according to the number of users, the initial hash code length of the fitness personal privacy data corresponding to each user category; According to 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; According to the target hash code length of the fitness personal privacy data corresponding to each of the user categories, a preset hash function processing strategy corresponding to each of the user categories is determined.

[0011] In some embodiments, after obtaining the hash code corresponding to the fitness personal privacy data of each user, the method further includes: Obtaining a first separation weight corresponding to personal data, a second separation weight corresponding to fitness data, and a third separation weight corresponding to consumption data in each of the user categories; According to the first separation weight, the second separation weight, and the third separation weight, a separation symbol is inserted into the hash code to obtain a new hash code corresponding to the fitness personal privacy data of each of the users, and the new hash code corresponding to the fitness personal privacy data of each of the users is used to be transmitted to the target terminal.

[0012] In some embodiments, the obtaining of the first separation weight corresponding to the personal data, the second separation weight corresponding to the fitness data, and the third separation weight corresponding to the consumption data in each of the user categories includes: Obtaining a first weighted contour coefficient corresponding to personal data, a second weighted contour coefficient corresponding to fitness data, and a third weighted contour coefficient corresponding to consumption data in each of the user categories; Comparing a first actual data quantity of the personal data with a first preset data quantity to obtain a first data integrity corresponding to the personal data, and obtaining a first separation weight corresponding to the personal data according to the first weighted silhouette coefficient and the first data integrity; Comparing a second actual data quantity of the fitness data with a second preset data quantity to obtain a second data integrity corresponding to the fitness data, and obtaining a second separation weight corresponding to the fitness data according to the second weighted silhouette 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, and the third separation weight corresponding to the consumption data is obtained based on the third weighted contour coefficient and the third data integrity.

[0013] In some embodiments, transmitting the hash code to a target terminal to implement encryption protection of the fitness personal privacy data includes: Encrypting the fitness personal privacy data of the multiple users through a preset asymmetric encryption algorithm to obtain encrypted data corresponding to the fitness personal privacy data of each user; By using a preset digital signature strategy, signature processing is performed on each of the users to obtain a data signature corresponding to each of the users; 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.

[0014] In some embodiments, the method further comprises: The target terminal performs signature verification on the received data packet; When the signature verification fails, the target terminal discards the data packet; When the signature verification passes, the target terminal processes the encrypted data using the preset hash function processing strategy 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.

[0015] In some embodiments, the processing of the encrypted data according to the comparison result includes: When the comparison result of the parsed hash code and the hash code in the data packet is the same, storing the encrypted data based on the identity information of the user; When the comparison result of 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.

[0016] The present invention has the following beneficial effects: First, the fitness personal privacy data of multiple users are obtained; then, multiple user categories are determined according to the fitness personal privacy data of the multiple users; then, according to the preset hash function processing strategy corresponding to each of the user categories, the fitness personal privacy data corresponding to each of the user categories is processed to obtain a hash code corresponding to the fitness personal privacy data of each of the users, wherein the preset hash function processing strategy corresponding to each of the user categories is different; finally, the hash code is transmitted to a target terminal to realize encryption protection of the fitness personal privacy data. In the present application, multiple user categories are determined based on the fitness personal privacy data of multiple users, and user groups with similar characteristics or behavior patterns can be identified, so that individual differences can be taken into account when processing data, thereby 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, which increases the distinctiveness of the hash code and improves the accuracy of the hash code as a data identifier. Compared with using the same hash function processing strategy for data processing, the probability of hash collision is reduced, and the one-to-one correspondence between the original fitness personal privacy data and its corresponding hash code is stronger. While ensuring data integrity, any unauthorized data modification behavior can be more reliably detected, thereby improving the accuracy and reliability of the encrypted fitness personal privacy data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of an implementation environment of a method for encrypting and protecting personal privacy data of fitness provided by an embodiment of the present invention; Figure 2 A flowchart of a method for encrypting and protecting personal privacy data of fitness provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a fitness personal privacy data encryption protection device provided by an embodiment of the present invention; Figure 4A schematic diagram of the structure of a computer system suitable for electronic equipment provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a fitness personal privacy data encryption protection method proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0020] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0021] Unless defined otherwise, 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 belongs.

[0022] The specific scheme of the method for encrypting and protecting personal privacy data of fitness provided by the present invention is described in detail below with reference to the accompanying drawings.

[0023] See also Figure 1 , Figure 1 The following is a schematic diagram of an implementation environment of a method for encrypting and protecting personal privacy data of a fitness program provided by an embodiment of the present invention. Figure 1As 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 a mobile device with local computing capabilities, a laptop computer, a tablet computer, a handheld computer, a PAD, a desktop computer, etc.; the data encryption protection device can be implemented in the form of a target client, and the target client can be a video client, an instant messaging client, a browser client, etc. that supports encryption protection of fitness personal privacy data; the data encryption protection terminal 101 can, but is not limited to, communicate with the first target terminal 102 through a network, and the above network can include but is not limited to: a wired network, a wireless network, wherein the wired network includes: a local area network, a metropolitan area network, and a wide area network, and the wireless network includes: Bluetooth, WIFI, and other networks that realize wireless communication. The above data encryption protection terminal 101 can, but is not limited to, include a human-computer interaction screen, a processor, and a memory. The above processor can, but is not limited to, be used to respond to human-computer interaction operations, perform corresponding operations, or generate corresponding instructions.

[0024] As an optional method, the data encryption protection terminal 101 can be a mobile phone, through which the fitness personal privacy data of multiple users can be collected 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 this embodiment does not make any limitation to this.

[0025] As an optional method, the following steps of the fitness personal privacy data encryption protection method can be performed on the data encryption protection terminal 101: Obtaining multiple users’ personal fitness data; Determining multiple user categories based on the multiple users' personal fitness privacy data; 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 a hash code corresponding to the fitness personal privacy data of each user, wherein the preset hash function processing strategy corresponding to each user category is different; The hash code is transmitted to the target terminal to implement encryption protection of the fitness personal privacy data.

[0026] Based on the above method, multiple user categories are determined according to the fitness personal privacy data of multiple users, and user groups with similar characteristics or behavior patterns can be identified, so that individual differences can be taken into account when processing data, thereby 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, which increases the distinctiveness of the hash code and improves the accuracy of the hash code as a data identifier. Compared with using the same hash function processing strategy for data processing, the probability of hash collision is reduced, and the one-to-one correspondence between the original fitness personal privacy data and its corresponding hash code is stronger. While ensuring data integrity, any unauthorized data modification behavior can be more reliably detected, thereby improving the accuracy and reliability of the encrypted fitness personal privacy data.

[0027] As an optional example, this embodiment does not limit the executor of the above-mentioned fitness personal privacy data encryption protection method. The above-mentioned fitness personal privacy data encryption protection method can be executed on the data encryption protection terminal 101. For example, when the data encryption protection terminal 101 is a mobile phone, some or all steps of the above-mentioned fitness personal privacy data encryption protection method can be executed on the mobile phone.

[0028] The above section introduces the contents of an exemplary implementation environment for applying the technical solution of the present application. Next, the present application's method for encrypting and protecting fitness personal privacy data will be introduced.

[0029] In order to solve the problem of how to improve the accuracy and reliability of encrypted and protected fitness personal privacy data in the prior art, the embodiments of the present application respectively propose a fitness personal privacy data encryption protection method, a fitness personal privacy data encryption protection device, an electronic device, a computer-readable storage medium and a computer program product. These embodiments will be described in detail below.

[0030] See also Figure 2 , Figure 2 A flowchart of a method for encrypting and protecting fitness personal privacy data provided by an embodiment of the present invention is provided. The method can be applied to Figure 1 It should be understood that the method can also be applied to other exemplary implementation environments and specifically executed by devices in other implementation environments, and this embodiment does not limit the implementation environment to which the method is applicable.

[0031] like Figure 2 As shown, in an exemplary embodiment, the method for encrypting and protecting fitness personal privacy data includes at least steps S210 to S240, which are described in detail as follows: In step S210, the personal fitness privacy data of multiple users are obtained.

[0032] Among them, 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 each user uses the fitness application installed on the data encryption protection terminal 101 to exercise and bodybuilding, the data encryption protection terminal 101 obtains each user's personal fitness privacy data in real time.

[0033] In step S220, a plurality of user categories are determined according to the fitness personal privacy data of the plurality of users.

[0034] Among them, the hash function is an algorithm that maps input data of arbitrary length to a hash value of fixed length. However, since the hash space is limited and the input data length is infinite, resulting in a fixed output length, there is a high probability in theory that different inputs will produce the same hash result. This hash collision occurs by chance. When the characteristics of the data set do not match the design of the hash function, the probability of hash collision will be greatly increased, thereby increasing query time and complexity, increasing memory usage, reducing storage efficiency, destroying the randomness of the hash function, and possibly causing performance degradation and data integrity damage. User data in fitness applications is complex and diverse. By analyzing the user's fitness personal privacy data, the similarities in different user data sequences can be obtained, and then users can be divided into different user categories based on similarities, so as to determine different hash function processing strategies according to different user categories.

[0035] Exemplarily, before determining multiple user categories based on the fitness personal privacy data of multiple users, the fitness personal privacy data can be preprocessed. The preprocessing may include removing duplicates, filling missing values, correcting erroneous data, etc., and may also include standardizing numerical data so that they are on a common scale.

[0036] Exemplarily, the fitness personal privacy data of multiple users can be analyzed through cluster analysis, user portraits, and classification models to determine multiple user categories. Among them, cluster analysis can include: K-means clustering, which is a commonly used unsupervised learning method that can divide users into several groups according to similarity, select a suitable K value (number of categories), and use Euclidean distance or other distance metrics to evaluate the similarity between data points; hierarchical clustering, this method can build a hierarchical structure between users, which helps to discover more complex category relationships; DBSCAN (Density-Based Spatial Clustering of Applications with Noise, a density-based clustering algorithm), which can identify clusters of any shape and find these clusters in data containing noise. Among them, user portraits can include: behavior pattern analysis, analyzing users' exercise habits, preferences, etc., to form users' behavior patterns; health status assessment, based on users' physiological indicators, such as BMI index (Body Mass Index index, body mass index), heart rate, etc., to assess the health status of users. Among them, the classification model can use supervised learning methods for classification, such as logistic regression, support vector machine, decision tree, etc.

[0037] Exemplarily, user categories may include: primary fitness enthusiasts, who exercise occasionally and have little knowledge of fitness; intermediate fitness enthusiasts, who exercise regularly and have a certain fitness experience and knowledge base; and advanced fitness enthusiasts, who exercise frequently and may have a professional training background.

[0038] 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 a hash code corresponding to the fitness personal privacy data of each user, wherein the preset hash function processing strategy corresponding to each user category is different.

[0039] For example, for each user category, a hash function processing strategy suitable for that category is designed. The design of these hash function processing strategies should take into account the characteristics and security requirements of the data. For example: for beginner fitness enthusiasts, a simple but effective hash function can be used to quickly process data; for professional athletes, a more complex and more secure hash function can be used to ensure data security.

[0040] In step S240, the hash code is transmitted to the target terminal to implement encryption protection of the fitness personal privacy data.

[0041] Exemplarily, the hash code can be encapsulated in a secure data packet, which can also include metadata (such as the sending time and the identity of the recipient, etc.); the hash code itself can also be encrypted, especially when the transmitted data contains sensitive information, for example, the hash code can be encrypted using symmetric encryption or asymmetric encryption. The target terminal can receive the encrypted data packet, then decrypt the data packet and extract the hash code, verify the data integrity, and store the hash code.

[0042] It can be seen from the above steps S210 to S240 that the solution proposed in this embodiment determines multiple user categories based on the fitness personal privacy data of multiple users, and can identify user groups with similar characteristics or behavior patterns, so that individual differences can be taken into account when processing data, thereby 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, which increases the discrimination of the hash code and improves the accuracy of the hash code as a data identifier. Compared with using the same hash function processing strategy for data processing, the probability of hash collision is reduced, and the one-to-one correspondence between the original fitness personal privacy data and its corresponding hash code is stronger, which ensures data integrity while more reliably detecting any unauthorized data modification behavior, thereby improving the accuracy and reliability of the encrypted fitness personal privacy data.

[0043] In one embodiment of the present application, the fitness personal privacy data includes: Personal data, including age, gender, height, weight, and circumference; Fitness data, including fitness times, fitness duration, fitness frequency, fitness equipment used, and fitness course completion information; Consumption data, including member registration time, fitness course purchase information, and fitness equipment purchase information.

[0044] 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.

[0045] In one embodiment of the present application, determining multiple user categories based on the fitness personal privacy data of the multiple users includes: According to preset classification indicators, the multiple users are divided into multiple user groups; Sorting the fitness personal privacy data of multiple users in each of the user groups to obtain a data sequence of each of the user groups; Traversing the data sequences of each of the user groups to obtain the similarity between the fitness personal privacy data of each user in each of the user groups; According to the similarities, the users in each of the user groups are classified to obtain the multiple user categories.

[0046] Among them, the preset classification indicators can be selected according to the characteristics of fitness personal privacy data, such as: age, gender, exercise frequency, exercise type, heart rate range, BMI index. Assuming that age, exercise frequency and BMI index are selected as classification indicators, users can be initially divided into different groups. For example: the young active group is a group with younger age, higher exercise frequency and normal BMI index; the middle-aged health care group is a group with moderate age, average exercise frequency and high BMI index; the elderly rehabilitation group is a group with older age, lower exercise frequency and higher or lower BMI index.

[0047] Exemplarily, for each user group, further segmentation can be performed by calculating similarity. Similarity can be calculated in a variety of ways, such as using Euclidean distance, Manhattan distance, cosine similarity, etc. For example, the numerical difference between two users in terms of exercise frequency, BMI index, etc. can be calculated to evaluate the degree of similarity. After obtaining the similarity, users can be further segmented into more specific categories, and clustering algorithms can be used to automatically discover subgroups within the user group. These subgroups can be divided based on the similarity of users, thereby forming more fine-grained user categories.

[0048] Exemplarily, users can first be divided into two major user groups based on age. For each user group, the fitness personal privacy data can be sorted as follows: personal data, fitness data, and consumption data, wherein personal data is arranged in the order of age, gender, height, weight, and circumference, and fitness data is arranged in the order of number of fitness exercises, fitness duration, fitness frequency, fitness equipment used, and fitness course completion information; after all data are normalized, a data sequence is formed, and when there is no data at the corresponding position, 0 is filled to fill the position to obtain the data sequence of each user group.

[0049] For example, the similarity between the personal fitness privacy data of each user in each user group may be expressed as follows: in, Indicates the similarity between fitness personal privacy data; Represents the value of the kth data in the data sequence of user 1; represents the value of the kth data in the data sequence of user 2; n represents the number of data in the data sequence.

[0050] in, It represents the difference between the corresponding position data in two data sequences. The smaller the difference, the higher the similarity between the data.

[0051] in, It indicates the average similarity between all the data in the data sequence. The smaller the average similarity, the more similar the users are, and the greater the possibility that they belong to the same category.

[0052] In this embodiment, by presetting the classification index to preliminarily divide users into different groups, it can be ensured that the users in each group have certain commonalities, so that in subsequent processing, the characteristics of a specific group can be analyzed more accurately, improving the accuracy and precision of data processing. By sorting the data in the user group and calculating the similarity between users, the internal connection between users can be discovered. Similarity calculation helps to reveal the structure and pattern within the user group, so as to better understand the behavior and needs of users.

[0053] In an embodiment of the present application, the users in each of the user groups are classified according to the similarity to obtain the multiple user categories, including: According to the similarity, the data distance between the fitness personal privacy data of each user and the fitness personal privacy data of other users is obtained; Acquire multiple preset numbers of clusters, and cluster the fitness personal privacy data of each user according to each number of clusters to obtain multiple clusters corresponding to each number of clusters; For each number of clusters, according to the data distance, obtain the weighted same-cluster distance and the weighted nearest inter-cluster distance of the user's fitness personal privacy data in each cluster, and according to the weighted same-cluster distance and the weighted nearest inter-cluster distance, obtain the weighted contour coefficient of the fitness personal privacy data of each user; According to the weighted silhouette coefficient of the fitness personal privacy data of each user, an average weighted silhouette coefficient of each cluster is obtained; An optimal number of clusters is determined according to average weighted silhouette coefficients of the clusters corresponding to the optimal number of clusters, and clustering is performed according to the optimal number of clusters to obtain a plurality of target clusters, each of which is used to indicate the user category.

[0054] Among them, the weighted same-cluster distance is used to measure the similarity of users in the same cluster, and the weighted nearest inter-cluster distance is used to measure the separation between different clusters. For each number of clusters, the average weighted silhouette coefficient of all users is calculated, which will give a quantitative indicator for evaluating the effect of clustering under different numbers of clusters. The number of clusters with the highest average weighted silhouette coefficient is selected as the optimal number of clusters. Under this number of clusters, the clustering result is optimal, that is, the position of users in their respective clusters is most reasonable.

[0055] For example, similarity is used as the data distance between fitness personal privacy data, and K-means clustering (a commonly used unsupervised learning algorithm) is used to obtain the target cluster. First, multiple preset cluster numbers are selected, and for each cluster number, the k-means clustering algorithm is used for clustering to obtain multiple clusters corresponding to each cluster number. For the sample points in each cluster, the weights are calculated according to the ratio of fitness data to all fitness data in the group, and the weighted same-cluster distance and the weighted closest inter-cluster distance are calculated.

[0056] For example, the weighted cluster distance can be expressed as: in, represents the weighted cluster distance of the fitness personal privacy data of the i-th user; represents the weight of the jth user data; represents the data distance between the fitness personal privacy data of the i-th user and the fitness personal privacy data of the j-th user; m represents the The number of users in a cluster; Indicates the cluster to which the i-th user belongs.

[0057] in, represents the weighted distance between a number of 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 weight representing the distance between a number of 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.

[0058] in, The smaller the ratio is, the smaller the distance between the fitness personal privacy data of the ith user and several data points in the same cluster is, the data points in the cluster are relatively close in the feature space, have higher similarity in the selected features, and the better the clustering effect is.

[0059] Exemplarily, the weighted nearest cluster distance may be expressed as: in, represents the weighted nearest cluster distance of the fitness personal privacy data of the i-th user; represents the weight of the jth user data; represents the data distance between the fitness personal privacy data of the i-th user and the fitness personal privacy data of the j-th user; M represents the The number of users in a cluster; is the kth cluster except the cluster where the fitness personal privacy data of the i-th user is located; min represents the minimum value calculation method in mathematical calculation.

[0060] in, represents the weighted distance between a number of 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; Represents the weight of the distance between a number of 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.

[0061] in, It represents the minimum value of the average weighted distance between all data points in any cluster outside the cluster of the fitness personal privacy data of the i-th user and the fitness personal privacy data of the i-th user. The smaller the weighted nearest inter-cluster distance is, the higher the similarity between the two clusters is, which means that their data are relatively close in the feature space.

[0062] Exemplarily, the weighted silhouette coefficient may be expressed as: in, Represents the weighted silhouette coefficient of the fitness personal privacy data of the i-th user; 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.

[0063] in, express and The maximum value of represents the similarity between the cluster to which the fitness personal privacy data of the i-th user belongs and other clusters.

[0064] in, It represents the difference between the weighted nearest cluster distance and the weighted same-cluster distance of the fitness personal privacy data of the i-th user. The larger the difference, the closer the cluster is to other clusters and the poorer the clustering effect.

[0065] Exemplarily, based on the weighted silhouette coefficient of each user's fitness personal privacy data, the average weighted silhouette coefficient of each cluster is obtained, and the average weighted silhouette coefficient graph corresponding to different numbers of clusters is drawn. The number of clusters corresponding to the highest average weighted silhouette coefficient is selected as the optimal number of clusters, and clustering is re-run using the determined optimal number of clusters to obtain multiple target clusters. According to the multiple target clusters, the fitness personal privacy data is divided into several categories to obtain several user categories, and the number of users in each user category is counted.

[0066] In this embodiment, by calculating the weighted silhouette coefficient of the user and determining the optimal number of clusters based on it, the quality of clustering can be significantly improved, avoiding the problem of poor clustering effect caused by improper human settings. The weighted silhouette coefficient comprehensively considers the compactness within the cluster and the separation between clusters, making the final clusters more reasonable and the distribution of users in their respective clusters more even.

[0067] In one embodiment of the present application, after determining the multiple user categories, the method further includes: Obtaining the number of users and data complexity corresponding to each of the user categories; Determine, according to the number of users, the initial hash code length of the fitness personal privacy data corresponding to each user category; According to 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; According to the target hash code length of the fitness personal privacy data corresponding to each of the user categories, a preset hash function processing strategy corresponding to each of the user categories is determined.

[0068] The output value of the hash function is usually a string of fixed length, and the output hash code is generally composed of 26 letters and 10 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, according to the number of users in different user categories, different output hash code lengths are determined, thereby increasing the complexity of the output hash code, reducing the probability of hash collisions, and improving the security of hash function processing, thereby improving the overall performance and security of the system.

[0069] For example, the information entropy of each user category is calculated using the information entropy Indicates the data complexity of the user category.

[0070] Exemplarily, the initial hash code length may be expressed as: in, Take an integer to represent the The length of the initial hash code of the fitness personal privacy data corresponding to each user category; Indicates The number of users corresponding to each user category; log is the logarithmic function in mathematical calculation, The base is 36 The logarithm of .

[0071] Exemplarily, the target hash code length may be expressed as: in, represents the target hash code length of the f-th user category; represents the data complexity of the fth user category; represents the The initial hash code length of the fitness personal privacy data corresponding to each user category.

[0072] In this embodiment, the initial hash code length is determined according to the number of users, and the target hash code length is adjusted according to the complexity of the data, so that the use of resources can be optimized under the premise of ensuring data security. For example, for categories with a small number of users and simpler data, a shorter hash code can be used to save storage space and computing resources; while for categories with a large number of users and more complex data, a longer hash code can be used to ensure sufficient security.

[0073] In one embodiment of the present application, after obtaining the hash code corresponding to the fitness personal privacy data of each user, the method further includes: Obtaining a first separation weight corresponding to personal data, a second separation weight corresponding to fitness data, and a third separation weight corresponding to consumption data in each of the user categories; According to the first separation weight, the second separation weight, and the third separation weight, a separation symbol is inserted into the hash code to obtain a new hash code corresponding to the fitness personal privacy data of each of the users, and the new hash code corresponding to the fitness personal privacy data of each of the users is used to be transmitted to the target terminal.

[0074] Among them, user data includes personal data, fitness data, and consumption data. According to the importance of the data in the three data categories, the weight of the hash code length occupied by each data category is calculated.

[0075] In this embodiment, due to the different complexity of the data, the length of the hash code corresponding to each category of data is also inconsistent. The user data is segmented to obtain the hash code, so that the distribution of the hash code is more uniform. Since the hash code segment positions of the fitness personal privacy data of different users are different, the possibility of hash collision can be effectively reduced, and it is easier to identify errors during the verification process.

[0076] In one embodiment of the present application, the obtaining of the first separation weight corresponding to the personal data, the second separation weight corresponding to the fitness data, and the third separation weight corresponding to the consumption data in each of the user categories includes: Obtaining a first weighted contour coefficient corresponding to personal data, a second weighted contour coefficient corresponding to fitness data, and a third weighted contour coefficient corresponding to consumption data in each of the user categories; Comparing a first actual data quantity of the personal data with a first preset data quantity to obtain a first data integrity corresponding to the personal data, and obtaining a first separation weight corresponding to the personal data according to the first weighted silhouette coefficient and the first data integrity; Comparing a second actual data quantity of the fitness data with a second preset data quantity to obtain a second data integrity corresponding to the fitness data, and obtaining a second separation weight corresponding to the fitness data according to the second weighted silhouette 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, and the third separation weight corresponding to the consumption data is obtained based on the third weighted contour coefficient and the third data integrity.

[0077] Among them, in the same user category, the degree of difference of data is similar, and the first weighted contour coefficient corresponding to the personal data of the users, the second weighted contour coefficient corresponding to the fitness data, and the third weighted contour coefficient corresponding to the consumption data can be calculated respectively; Exemplarily, the data integrity may be expressed as follows: in, Indicates the data integrity corresponding to the dth data category; Indicates the actual number of data in the dth data category; Indicates the preset data quantity of the dth data category.

[0078] Among them, when the preset data quantity of the dth data category is constant, the larger the actual data quantity of the dth data category is, the higher the data integrity is.

[0079] For example, the length of the hash code corresponding to each new user's personal fitness privacy data can be expressed as: in, represents the target hash code length of the f-th user category; represents the length of the hash code corresponding to the personal data in the f-th user category; represents the length of the hash code corresponding to the fitness data in the f-th user category; Indicates the length of the hash code corresponding to the consumption data in the f-th user category.

[0080] For example, The representation can be: in, Indicates the first data integrity corresponding to the personal data in the f-th user category; represents the first separation weight corresponding to personal data in the f-th user category; d represents the d-th data category, when d=1, the first data category is personal data, when d=2, the second data category is fitness data, and when d=3, the third data category is consumption data.

[0081] in, It represents the sum of the products of the data completeness and weighted silhouette coefficient of the three data categories in the f-th user category.

[0082] in, It represents the first separation weight. The larger the first separation weight is, the more important the personal data is, and the longer the hash code length corresponding to the personal data should be.

[0083] In this embodiment, by calculating the first weighted silhouette coefficient, the second weighted silhouette coefficient and the third weighted silhouette coefficient of different data types, the importance and representativeness of each data type in the user category can be evaluated more finely. By comparing the actual data quantity with the preset data quantity, the integrity of the data can be evaluated, which helps to ensure that the integrity of the data is guaranteed when processing and transmitting data, and reduces misjudgment or analysis bias caused by missing data. The weighted silhouette coefficient reflects the suitability of the data in the cluster, while the data integrity reflects the actual availability of the data. By combining the two to determine the separation weight, the distinction between different data types in the user category can be more accurately reflected, thereby improving the accuracy of data processing.

[0084] In one embodiment of the present application, the transmitting of the hash code to the target terminal to implement encryption protection of the fitness personal privacy data includes: Encrypting the fitness personal privacy data of the multiple users through a preset asymmetric encryption algorithm to obtain encrypted data corresponding to the fitness personal privacy data of each user; By using a preset digital signature strategy, signature processing is performed on each of the users to obtain a data signature corresponding to each of the users; 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.

[0085] In this embodiment, an asymmetric encryption algorithm is used to encrypt the user's fitness personal privacy data, which can ensure that even if the encrypted data is intercepted, the attacker cannot easily decrypt the data. The asymmetric encryption algorithm uses a public key to encrypt data, and only the person holding the private key can decrypt it, thereby improving the security of the data. The user data is signed by a preset digital signature strategy to verify the authenticity and integrity of the data. The digital signature is usually generated using the user's private key, and the recipient uses the public key to verify the validity of the signature to ensure that the data has not been tampered with.

[0086] In one embodiment of the present application, the method further includes: The target terminal performs signature verification on the received data packet; When the signature verification fails, the target terminal discards the data packet; When the signature verification passes, the target terminal processes the encrypted data using the preset hash function processing strategy 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.

[0087] In this embodiment, signature verification is used to ensure that the source of the data packet is credible. If the signature verification fails, the target terminal will discard the data packet, which helps prevent the intrusion of malicious data and potential security threats. When the signature verification passes, the target terminal will use a preset hash function processing strategy to process the encrypted data to obtain a parsed hash code. This process helps to verify whether the data remains intact and has not been tampered with during transmission. By comparing the parsed hash code with the hash code in the data packet, the reliability of the data can be further ensured.

[0088] In one embodiment of the present application, the processing of the encrypted data according to the comparison result includes: When the comparison result of the parsed hash code and the hash code in the data packet is the same, storing the encrypted data based on the identity information of the user; When the comparison result of 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.

[0089] In this embodiment, by comparing the parsed hash code with the hash code in the data packet, it is possible to verify whether the data maintains integrity during transmission, thereby improving the reliability of data integrity verification and ensuring that only data that has not been tampered with is stored. If the parsed hash code is inconsistent with the hash code in the data packet, it means that the data may have been tampered with during transmission. At this time, discarding the data and requesting a new data packet can prevent the damaged data from being stored, thereby improving the security of the data.

[0090] Figure 3 This is a schematic diagram of a fitness personal privacy data encryption protection device provided by an embodiment of the present invention. The device can be applied to Figure 1 The device may also be applicable to other exemplary implementation environments and specifically configured in other devices, and this embodiment does not limit the implementation environment to which the device is applicable.

[0091] like Figure 3 As shown, the exemplary fitness personal privacy data encryption protection device includes: The data acquisition module 301 is used to acquire the fitness personal privacy data of multiple users; A user classification module 302, configured to determine a plurality of user categories according to the fitness personal privacy data of the plurality of users; 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 to obtain the hash code corresponding to the fitness personal privacy data of each user, wherein the preset hash function processing strategy corresponding to each user category is different; The transmission module 304 is used to transmit the hash code to the target terminal to implement encryption protection of the fitness personal privacy data.

[0092] 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, and user groups with similar characteristics or behavior patterns can be identified so that individual differences can be taken into account when processing data, thereby 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, which increases the distinctiveness of the hash code and improves the accuracy of the hash code as a data identifier. Compared with using the same hash function processing strategy for data processing, the probability of hash collision is reduced, and the one-to-one correspondence between the original fitness personal privacy data and its corresponding hash code is stronger. While ensuring data integrity, any unauthorized data modification behavior can be more reliably detected, thereby improving the accuracy and reliability of the encrypted and protected fitness personal privacy data.

[0093] It should be noted that the fitness personal privacy data encryption protection device provided in the above embodiment and the fitness personal privacy data encryption protection method provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment, and will not be repeated here. In actual application, the fitness personal privacy data encryption protection device provided in the above embodiment can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0094] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the electronic device implements the fitness personal privacy data encryption protection method provided in the above-mentioned embodiments.

[0095] Figure 4 This is a schematic diagram of the structure of a computer system suitable for electronic equipment provided by one embodiment of the present invention. It should be noted that: Figure 4 The computer system 400 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0096] like Figure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, the ROM 402 and the RAM 403 are connected to each other through the bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0097] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, 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, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0098] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the device of the present application are executed.

[0099] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier, which carries a computer-readable computer program. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0100] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the apparatus, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0101] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0102] Another aspect of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for a processor to load to execute the steps in any one of the fitness personal privacy data encryption protection methods provided in the embodiments of the present application. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist independently without being assembled into the electronic device.

[0103] It should be noted that the sequence of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0104] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for encrypting and protecting personal privacy data of fitness, characterized in that: The method comprises: Obtaining multiple users’ personal fitness data; Determining multiple user categories based on the multiple users' personal fitness privacy data; 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 a hash code corresponding to the fitness personal privacy data of each user, wherein the preset hash function processing strategy corresponding to each user category is different; The hash code is transmitted to the target terminal to implement encryption protection of the fitness personal privacy data.

2. The method for encrypting and protecting fitness personal privacy data according to claim 1, characterized in that: The fitness personal privacy data includes: Personal data, including age, gender, height, weight, and circumference; Fitness data, including fitness times, fitness duration, fitness frequency, fitness equipment used, and fitness course completion information; Consumption data, including member registration time, fitness course purchase information, and fitness equipment purchase information.

3. The method for encrypting and protecting fitness personal privacy data according to claim 1, characterized in that: Determining a plurality of user categories according to the fitness personal privacy data of the plurality of users comprises: According to preset classification indicators, the multiple users are divided into multiple user groups; Sorting the fitness personal privacy data of multiple users in each of the user groups to obtain a data sequence of each of the user groups; Traversing the data sequences of each of the user groups to obtain the similarity between the fitness personal privacy data of each user in each of the user groups; According to the similarities, the users in each of the user groups are classified to obtain the multiple user categories.

4. The method for encrypting and protecting fitness personal privacy data as claimed in claim 3, characterized in that: The user groups are classified according to the similarities to obtain the multiple user categories, including: According to the similarity, the data distance between the fitness personal privacy data of each user and the fitness personal privacy data of other users is obtained; Acquire multiple preset numbers of clusters, and cluster the fitness personal privacy data of each user according to each number of clusters to obtain multiple clusters corresponding to each number of clusters; For each number of clusters, according to the data distance, obtain the weighted same-cluster distance and the weighted nearest inter-cluster distance of the user's fitness personal privacy data in each cluster, and according to the weighted same-cluster distance and the weighted nearest inter-cluster distance, obtain the weighted contour coefficient of the fitness personal privacy data of each user; According to the weighted silhouette coefficient of the fitness personal privacy data of each user, an average weighted silhouette coefficient of each cluster is obtained; An optimal number of clusters is determined according to average weighted silhouette coefficients of the clusters corresponding to the optimal number of clusters, and clustering is performed according to the optimal number of clusters to obtain a plurality of target clusters, each of which is used to indicate the user category.

5. The method for encrypting and protecting fitness personal privacy data as claimed in claim 2, characterized in that: After determining the multiple user categories, the method further includes: Obtaining the number of users and data complexity corresponding to each of the user categories; Determine, according to the number of users, the initial hash code length of the fitness personal privacy data corresponding to each user category; According to 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; According to the target hash code length of the fitness personal privacy data corresponding to each of the user categories, a preset hash function processing strategy corresponding to each of the user categories is determined.

6. The method for encrypting and protecting fitness personal privacy data as claimed in claim 2, characterized in that: After obtaining the hash code corresponding to the fitness personal privacy data of each user, the method further includes: Obtaining a first separation weight corresponding to personal data, a second separation weight corresponding to fitness data, and a third separation weight corresponding to consumption data in each of the user categories; According to the first separation weight, the second separation weight, and the third separation weight, a separation symbol is inserted into the hash code to obtain a new hash code corresponding to the fitness personal privacy data of each of the users, and the new hash code corresponding to the fitness personal privacy data of each of the users is used to be transmitted to the target terminal.

7. The method for encrypting and protecting fitness personal privacy data according to claim 6, characterized in that: The obtaining of the first separation weight corresponding to the personal data, the second separation weight corresponding to the fitness data, and the third separation weight corresponding to the consumption data in each of the user categories includes: Obtaining a first weighted contour coefficient corresponding to personal data, a second weighted contour coefficient corresponding to fitness data, and a third weighted contour coefficient corresponding to consumption data in each of the user categories; Comparing a first actual data quantity of the personal data with a first preset data quantity to obtain a first data integrity corresponding to the personal data, and obtaining a first separation weight corresponding to the personal data according to the first weighted silhouette coefficient and the first data integrity; Comparing a second actual data quantity of the fitness data with a second preset data quantity to obtain a second data integrity corresponding to the fitness data, and obtaining a second separation weight corresponding to the fitness data according to the second weighted silhouette 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, and the third separation weight corresponding to the consumption data is obtained based on the third weighted contour coefficient and the third data integrity.

8. The method for encrypting and protecting fitness personal privacy data according to claim 1, characterized in that: The transmitting the hash code to the target terminal to implement encryption protection of the fitness personal privacy data includes: Encrypting the fitness personal privacy data of the multiple users through a preset asymmetric encryption algorithm to obtain encrypted data corresponding to the fitness personal privacy data of each user; By using a preset digital signature strategy, signature processing is performed on each of the users to obtain a data signature corresponding to each of the users; 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.

9. The method for encrypting and protecting fitness personal privacy data according to claim 1, characterized in that: The method further comprises: The target terminal performs signature verification on the received data packet; When the signature verification fails, the target terminal discards the data packet; When the signature verification passes, the target terminal processes the encrypted data using the preset hash function processing strategy 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.

10. The method for encrypting and protecting fitness personal privacy data according to claim 9, characterized in that: The processing of the encrypted data according to the comparison result includes: When the comparison result of the parsed hash code and the hash code in the data packet is the same, storing the encrypted data based on the identity information of the user; When the comparison result of 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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