A cloud photo frame data end-to-end secure encryption transmission method based on security requirements

Abstract

The present invention discloses a cloud photo frame data end-to-end secure encryption transmission method based on security requirements, which relates to the technical field of data encryption transmission, including the user performing identity authentication at the transmission end to generate an identity fingerprint, and generating a device fingerprint according to the transmission end device parameters, and binding the identity fingerprint with the device fingerprint; the transmission end collects environmental data and user behavior data to generate a user behavior fingerprint; based on the user identity fingerprint, device fingerprint and behavior fingerprint, a key is generated, and the cloud photo frame data is disrupted for encryption; the user identity change is detected to perform identity metabolism and key update, and the key is distributedly stored; the frequency band is selected by stealth communication for encrypted data transmission, and the receiving end receives the cloud photo frame data and performs decryption verification and storage. The present invention greatly improves the security and flexibility of the encryption key and improves the stability of cloud photo frame data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of data encryption transmission, and in particular to a cloud photo frame data end-to-end secure encryption transmission method based on security requirements. Background Art

[0002] With the continuous development and popularization of digital technology, cloud computing technology has become one of the important means of information storage and sharing. In this context, cloud photo frame, as an emerging smart device, allows users to easily display and share multimedia content such as photos and videos through interaction with cloud data. However, with the widespread application of cloud services, the security issues of data transmission have gradually emerged. Especially when it comes to personal privacy and important data, how to ensure the confidentiality, integrity and anti-theft of data during transmission has become an important issue that needs to be solved in cloud photo frame applications. Current research and development mainly focuses on data encryption transmission, identity authentication and secure storage, in order to provide users with a more secure and reliable cloud service experience. However, there are still many deficiencies in the existing technology. The existing technology is relatively simple in key generation, cannot comprehensively utilize multiple data, and the generated key security is insufficient. In addition, when dealing with dynamic changes in user identity, there is often a lack of efficient response measures, which easily leads to key failure or security vulnerabilities. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing cloud photo frame data end-to-end secure encryption transmission method based on security requirements, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that the existing technology is relatively simple in key generation, cannot comprehensively utilize multiple data, the generated keys are not secure enough, and when dealing with dynamic changes in user identities, there is often a lack of efficient response measures, which can easily lead to key failure or security vulnerabilities.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a method for end-to-end secure encryption transmission of cloud photo frame data based on security requirements, which includes: a user performs identity authentication at the transmission end to generate an identity fingerprint, and generates a device fingerprint according to the device parameters of the transmission end, and binds the identity fingerprint with the device fingerprint; the transmission end collects environmental data and user behavior data to generate a user behavior fingerprint; a key is generated based on the user identity fingerprint, the device fingerprint and the behavior fingerprint, and the cloud photo frame data is scrambled for encryption; the user identity change is detected to perform identity metabolism and key update, and the key is distributedly stored; stealth communication is used to select a frequency band for encrypted data transmission, and the receiving end receives the cloud photo frame data and decrypts and verifies it before storing it.

[0006] As a preferred solution of the end-to-end secure encryption transmission method of cloud photo frame data based on security requirements described in the present invention, wherein: the user performs identity authentication at the transmission end to generate an identity fingerprint, and generates a device fingerprint according to the device parameters of the transmission end, and binding the identity fingerprint with the device fingerprint means that the user enters the user identity information for identity registration when using the transmission end, and the transmission end uses a hash algorithm to generate a user identity hash value as the user identity fingerprint S according to the user identity information, and collects the transmission end device hardware information and system parameters to use the hash algorithm to generate a device fingerprint R, and binds the user identity fingerprint S and the device fingerprint R, and stores them in a secure database.

[0007] As a preferred solution of the end-to-end secure encryption transmission method of cloud photo frame data based on security requirements described in the present invention, wherein: the transmission end collects environmental data and user behavior data to generate user behavior fingerprints, which means that the transmission end collects device environmental data through device sensors, collects user behavior log data and multimedia data on the transmission end device, and performs noise cleaning and standardization on the device environment data, multimedia data and behavior log data through a master-slave adaptive notch filter;

[0008] The dual-tree complex discrete wavelet transform is used to extract the time-frequency domain features of device environment data, behavior log data and multimedia data respectively. , as well as ;

[0009] The Lorentz system is used to perform initial chaotic mapping on the time-frequency domain features:

[0010]

[0011] in , as well as is the initial value of the chaotic sequence;

[0012] The initial value update step of the chaotic sequence is calculated by the Lorentz equation, and the time period T is discretized and divided into time periods , use the Runge-Kutta method to calculate the , as well as , and generate the chaotic sequence C of time period T:

[0013]

[0014] The time-frequency domain features , as well as Splicing to form comprehensive time-frequency domain features , use the chaotic sequence C to encrypt the comprehensive time-frequency domain features:

[0015]

[0016] Where B is the chaotic encryption feature vector;

[0017] Set the initial distance threshold , map the chaotic encrypted feature vector B to the high-dimensional space, take each element in the chaotic encrypted vector B as a point in the high-dimensional space, and connect the points whose distance in the high-dimensional space is less than the initial distance threshold The points form a simple complex;

[0018] Gradually increase the distance threshold , and at each distance threshold The following computes the homology group of a simple complex, with each simple complex as a data set, where computing the homology group includes:

[0019] Zero-order homology group: the number of connected branches in the response data set;

[0020] First-order homology group: one-dimensional ring hole structure in the reaction data set;

[0021] Second-order homology group: reflects the high-dimensional hole structure in the data set;

[0022] Record the appearance and disappearance time of the simple complex homology group at different distance thresholds and form a persistence bar graph, identify the simple complex homology group represented by the longest bar in the persistence bar graph, and extract the simple complex homology group as the topological feature P;

[0023] Construct a relational two-layer aggregation graph convolutional network, set the relational two-layer aggregation graph convolutional network input as topological feature P, and output as behavioral feature X, use the training set to train the relational two-layer aggregation graph convolutional network, and input the topological feature P into the relational two-layer aggregation graph convolutional network to obtain the behavioral feature X;

[0024] Use the topological simplicity algorithm to denoise the behavior feature X to generate a simple topological feature ;

[0025] Simple topological features Apply SHA-256 hashing to generate user behavior fingerprint D.

[0026] As a preferred solution of the cloud photo frame data end-to-end secure encryption transmission method based on security requirements of the present invention, wherein: the key generation based on the user identity fingerprint, device fingerprint and behavior fingerprint refers to obtaining the user identity fingerprint S, device fingerprint R and behavior fingerprint D and combining them to generate the encryption key K using the SHA-256 hash algorithm:

[0027] .

[0028] As a preferred solution of the end-to-end secure encryption transmission method of cloud photo frame data based on security requirements of the present invention, wherein: the scrambling of cloud photo frame data for encryption refers to the transmission end obtaining the cloud photo frame data to be encrypted and transmitting, and extracting the video data, picture data, audio data and text data in the cloud photo frame data respectively;

[0029] Divide the video data into video data blocks of the same size according to the video frame height, width and number of frames, divide the picture data into picture data blocks of the same size according to the pixel size, divide the audio data into audio data blocks of the same length according to the time period, divide the text data into text data blocks according to the phrases, and record the initial arrangement order Q of the data blocks;

[0030] The transmission end generates a random sequence Reorder the data blocks of video data, image data, audio data, and text data respectively, and generate a random order Reorder all data blocks into disordered cloud photo frame data;

[0031] The random cloud photo frame data is encrypted using the encryption key K.

[0032] As a preferred solution of the end-to-end secure encryption transmission method of cloud photo frame data based on security requirements of the present invention, wherein: the detection of user identity changes to perform identity metabolism and key update, and the distributed storage of keys means that before each data encryption transmission, the transmission end collects device environment data, user behavior log data and multimedia data for denoising and standardization, and extracts detection time-frequency domain features. , as well as Forming comprehensive time-frequency domain features for detection , calculate and detect the comprehensive time-frequency domain features The hash value of the transmission end is combined with the comprehensive time-frequency domain features collected during the last data transmission The hash value is compared and the comparison threshold is set. When the comprehensive time-frequency domain features are detected and integrated time-frequency domain features When the hash value difference is higher than the comparison threshold, the identity metabolism operation is automatically triggered;

[0033] The transmission end detects time-frequency domain features , as well as Generate a new user behavior fingerprint and update the encryption key K based on the new user behavior fingerprint;

[0034] The encryption key K is split into key fragments using a key sharing algorithm, and the key fragments are stored in the distributed cloud server nodes.

[0035] As a preferred solution of the end-to-end secure encryption transmission method of cloud photo frame data based on security requirements of the present invention, wherein: the use of stealth communication to select a frequency band for encrypted data transmission refers to encrypting the disordered cloud photo frame data and then using the Tor network to generate a Tor routing path to hide the source and destination address of the data packet to transmit the encrypted disordered cloud photo frame data;

[0036] The transmission segment monitors the spectrum channel occupancy in real time during data transmission, selects idle spectrum channels for encrypted cloud photo frame data transmission, and implements key exchange between the transmitter and receiver through elliptic curves based on the TLS 1.3 protocol.

[0037] As a preferred solution of the end-to-end secure encryption transmission method of cloud photo frame data based on security requirements of the present invention, wherein: the receiving end decrypts and verifies the cloud photo frame data after receiving it, and then stores it, which means that the receiving end uses the encryption key K to decrypt the encrypted random cloud photo frame data after receiving it, and stores the random cloud photo frame data according to the random order. and The disordered cloud photo frame data is reordered to generate cloud photo frame data, and the hash value of the generated cloud photo frame data is calculated and compared with the hash value of the original cloud photo frame data to determine the integrity of the cloud photo frame data. If the receiving end determines that the integrity is passed, the cloud photo frame data is stored in the database.

[0038] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-mentioned cloud photo frame data end-to-end secure encryption transmission method based on security requirements when executing the computer program.

[0039] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned cloud photo frame data end-to-end secure encryption transmission method based on security requirements.

[0040] The beneficial effects of the present invention are as follows: the present invention generates identity fingerprints and device fingerprints by collecting user identity authentication data and device data, and generates user behavior fingerprints by collecting environmental data and user behavior data through the device to combine them to obtain encryption keys, thereby greatly improving the security and flexibility of encryption keys, and simultaneously scrambling cloud photo frame data for encrypted transmission and identity metabolism key update, thereby improving the stability of cloud photo frame data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0042] Figure 1 The figure is a flowchart of a method for end-to-end secure encryption transmission of cloud photo frame data based on security requirements.

[0043] Figure 2 Schematic diagram of the structure generated for encryption keys. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0047] Example 1

[0048] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and which provides a cloud photo frame data end-to-end secure encryption transmission method based on security requirements. The cloud photo frame data end-to-end secure encryption transmission method based on security requirements includes:

[0049] S1. The user performs identity authentication at the transmission end to generate an identity fingerprint, and generates a device fingerprint based on the transmission end device parameters, and binds the identity fingerprint to the device fingerprint;

[0050] Specifically, the user performs identity authentication at the transmission end to generate an identity fingerprint, and generates a device fingerprint based on the transmission end device parameters. Binding the identity fingerprint with the device fingerprint means that the user enters the user identity information for identity registration when using the transmission end. The transmission end uses a hash algorithm to generate a user identity hash value as the user identity fingerprint S based on the user identity information, and collects the transmission end device hardware information and system parameters to generate a device fingerprint R using a hash algorithm. The user identity fingerprint S and the device fingerprint R are bound and stored in a secure database.

[0051] By generating an identity fingerprint, a highly unique identifier can be provided for the user's identity. This identifier plays a key role in the subsequent identity authentication and data encryption process. The identity fingerprint generated by the hash algorithm is irreversible and can effectively prevent identity information leakage or reverse cracking. In addition, the generation process of the identity fingerprint does not need to store the original identity information, only the fingerprint value needs to be stored, which further enhances the privacy protection of the user's identity information. The generation of the device fingerprint ensures that each device can be uniquely identified. This feature is particularly important when preventing the device from being impersonated or forged. By combining the hardware information and system parameters of the device, the generated device fingerprint is not only unique, but also reflects the true status of the device. Even if the device hardware or system parameters change partially, the generation process of the device fingerprint can still remain consistent, ensuring that the device's identity authentication is accurate and reliable. By binding the identity fingerprint with the device fingerprint, the system can achieve dual authentication of the user's identity and the device, further enhancing security. Even if the identity information is stolen, unauthorized devices cannot pass the identity authentication, and vice versa. The binding process also ensures that data transmission or access operations can only be completed under the correct user and device combination, greatly reducing potential security risks.

[0052] S2, the transmission end collects environmental data and user behavior data to generate user behavior fingerprints;

[0053] Specifically, the transmission end collects environment data and user behavior data to generate user behavior fingerprints, which means that the transmission end collects device environment data through device sensors, collects user behavior log data and multimedia data on the transmission end device, and performs noise cleaning and standardization on the device environment data, multimedia data and behavior log data through master-slave adaptive notch filters;

[0054] The dual-tree complex discrete wavelet transform is used to extract the time-frequency domain features of device environment data, behavior log data and multimedia data respectively. , as well as ;

[0055] The Lorentz system is used to perform initial chaotic mapping on the time-frequency domain features:

[0056]

[0057] in , as well as is the initial value of the chaotic sequence;

[0058] The initial value update step of the chaotic sequence is calculated by the Lorentz equation, and the equation is as follows:

[0059]

[0060] in is the Prandtl number, which represents the ratio of the viscosity and thermal conductivity of the fluid, and is generally taken as 10. is the Rayleigh number, which indicates the ratio between the temperature difference and the viscous force, and is generally taken as 28. is the geometric factor, which reflects the physical geometric characteristics of the system and is generally taken as 8 / 3;

[0061] The time period T is discretized and divided into time periods , use the Runge-Kutta method to calculate the , as well as , and generate the chaotic sequence C of time period T:

[0062]

[0063] The time-frequency domain features , as well as Splicing to form comprehensive time-frequency domain features , use the chaotic sequence C to encrypt the comprehensive time-frequency domain features:

[0064]

[0065] Where B is the chaotic encryption feature vector;

[0066] Set the initial distance threshold , map the chaotic encrypted feature vector B to the high-dimensional space, take each element in the chaotic encrypted vector B as a point in the high-dimensional space, and connect the points whose distance in the high-dimensional space is less than the initial distance threshold The points form a simple complex;

[0067] Gradually increase the distance threshold , and at each distance threshold The following computes the homology group of a simple complex, with each simple complex as a data set, where computing the homology group includes:

[0068] Zero-order homology group: reflects the number of connected branches in a data set, such as independent point clusters;

[0069] First-order homology group: reflects the one-dimensional loop structure in the data set, such as a closed loop;

[0070] Second-order homology group: reflects the high-dimensional hole structure in the data set, such as cavities;

[0071] Record the appearance and disappearance time of the simple complex homology group at different distance thresholds and form a persistence bar graph, identify the simple complex homology group represented by the longest bar in the persistence bar graph, and extract the simple complex homology group as the topological feature P;

[0072] Construct a relational two-layer aggregation graph convolutional network, set the relational two-layer aggregation graph convolutional network input as topological feature P, and output as behavioral feature X, use the training set to train the relational two-layer aggregation graph convolutional network, and input the topological feature P into the relational two-layer aggregation graph convolutional network to obtain the behavioral feature X;

[0073] Use the topological simplicity algorithm to denoise the behavior feature X to generate a simple topological feature ;

[0074] Simple topological features Apply SHA-256 hashing to generate user behavior fingerprint D.

[0075] Environmental data and user behavior data are collected through device sensors, and noise is cleaned using master-slave adaptive notch filters to ensure high data quality and reliability. Standardization processing further eliminates scale differences between different data sources and provides a unified basis for subsequent feature extraction. This step greatly improves the accuracy and stability of subsequent data analysis. DTCDWT can capture important information in the data, such as directionality and frequency distribution characteristics, by extracting time-frequency domain features of device environmental data, behavior log data, and multimedia data. These features provide rich input for subsequent chaotic mapping and encryption, ensuring the multi-dimensional expression and complexity of the data. The initial chaotic mapping of time-frequency domain features is performed through the Lorentz system, and the generated chaotic sequence has extremely high unpredictability and complexity. This chaotic sequence is used to encrypt time-frequency domain features, enhancing the security and anti-attack of the data. By continuously performing chaotic encryption on the chaotic encrypted feature vectors, the chaotic sequence is generated. Long-term coherence analysis can extract key topological features in the data set, especially those that persist at different scales. The generation of persistent bar graphs provides an intuitive tool for identifying these key features. The topological feature P finally extracted is global and stable, and can accurately represent the topological structure of user behavior data. By constructing the RBAGCN model, the topological feature P is deeply analyzed and extracted, which can capture the global relationship and local features in the complex graph structure. Through training, the model can efficiently convert topological features into behavioral features X, ensuring the accuracy and representativeness of the behavioral features. The behavioral features X are denoised by the topological simplicity algorithm. The generated simplified topological features remove redundancy and noise while retaining the key behavioral features, ensuring the accuracy of the final fingerprint. The user behavior fingerprint D generated by the SHA-256 hash algorithm is highly unique and irreversible, greatly enhancing the security of the system.

[0076] S3, generate a key based on the user identity fingerprint, device fingerprint and behavior fingerprint, and scramble the cloud photo frame data for encryption;

[0077] Specifically, generating a key based on the user identity fingerprint, device fingerprint, and behavior fingerprint means obtaining the user identity fingerprint S, device fingerprint R, and behavior fingerprint D, and then combining them to generate an encryption key K using the SHA-256 hash algorithm:

[0078] .

[0079] By generating user identity fingerprints, device fingerprints and behavioral fingerprints respectively, the system can accurately identify users and devices in multiple dimensions. The identity fingerprint ensures the uniqueness of the user identity, the device fingerprint provides a unique identification of the device, and the behavioral fingerprint further improves the accuracy and security of identification by analyzing the user's operating behavior. Combining these three fingerprints allows the system to verify the user's identity from multiple levels, greatly improving the security of the system and preventing the risks of forgery and impersonation of a single fingerprint. After obtaining the user's identity fingerprint, device fingerprint and behavioral fingerprint, the system generates an encryption key through the SHA-256 hash algorithm. The SHA-256 algorithm, due to its strong one-way and anti-collision properties, can ensure that the generated key is highly secure and irreversible, which means that even if an attacker obtains the key value, it cannot obtain the user's identity, device or behavior information through reverse calculation. In addition, the key length generated by SHA-256 is 256 bits, which is sufficient to meet most current security needs and effectively prevent brute force attacks. By combining user identity fingerprints, device fingerprints and behavioral fingerprints, the system has established a multi-level security mechanism, which can effectively prevent security vulnerabilities caused by a single authentication method. For example, relying solely on identity fingerprints may be threatened by information leakage or identity theft, while device fingerprints may become invalid due to device forgery or cloning, and behavioral fingerprints may be misjudged due to the randomness and diversity of user behavior. However, by combining these three, the system can integrate all aspects of information for more comprehensive and accurate verification, effectively reducing the error rate and security risks. By combining the identity fingerprint, device fingerprint and behavioral fingerprint to generate the encryption key K, it can ensure that data encryption in different operations or transmission scenarios is consistent and secure. Since the key is generated based on fingerprint information in multiple dimensions, it has a high degree of anti-cracking. In addition, the dynamic generation feature of the key ensures that the encryption key for each operation is different, further enhancing the security of the data and preventing security vulnerabilities caused by the reuse of keys.

[0080] Further, scrambling the cloud frame data for encryption means that the transmission end obtains the cloud frame data that needs to be encrypted for transmission, and extracts the video data, the picture data, the audio data and the text data from the cloud frame data respectively;

[0081] Divide the video data into video data blocks of the same size according to the video frame height, width and number of frames, divide the picture data into picture data blocks of the same size according to the pixel size, divide the audio data into audio data blocks of the same length according to the time period, divide the text data into text data blocks according to the phrases, and record the initial arrangement order Q of the data blocks;

[0082] The transmission end generates a random sequence Reorder the data blocks of video data, image data, audio data, and text data respectively, and generate a random order Reorder all data blocks into disordered cloud photo frame data;

[0083] The random cloud photo frame data is encrypted using the encryption key K.

[0084] The video, picture, audio and text data are divided into independent data blocks of the same size, and their initial arrangement order Q is recorded, ensuring the uniform division of each data type. This division method not only helps to uniformly encrypt different types of data, but also makes the data more flexible and secure during encryption and transmission. The role of recording the initial arrangement order Q is to accurately restore the order of the original data during decryption and data recovery, ensuring the integrity and accuracy of the data. By generating a random order to reorder the data blocks, the obfuscation of the data is effectively increased, so that even if the attacker intercepts the data, the original data content cannot be easily restored. Random reordering disrupts the logical association between data blocks, further improving the data's anti-analysis ability and security. The data blocks of each data type are reordered independently, and then all data blocks are randomly reordered as a whole, further increasing the difficulty of data recovery and ensuring the high security of data during transmission. By reordering the data The blocks are combined into disordered cloud photo frame data, which can make the data present a highly complex and unpredictable arrangement form. This disordered data, as the final data form before encryption, further enhances the security of encryption and the irreversibility of data. The encryption key K is used to encrypt the disordered cloud photo frame data, ensuring that even if the data is intercepted during transmission, it cannot be cracked without the key. The security of this process depends on the strength and randomness of the encryption key. The encryption key K is generated based on the user identity fingerprint, device fingerprint and behavior fingerprint, and has high uniqueness and security. Through data block division, random sorting, disordered generation and encryption and other multi-level security measures, the high security of data during transmission is ensured. Different from the traditional single encryption method, the present invention greatly improves the anti-cracking and security of data through multi-level processing. In addition, during the decryption and data recovery process, since the initial arrangement order Q is recorded, the system can accurately restore the decrypted data to the original state, ensuring the integrity and correctness of the data.

[0085] S4, detect the change of user identity, perform identity metabolism and key update, and store the key in a distributed manner;

[0086] Specifically, the user identity change is detected to metabolize the identity and update the key, and the key is distributedly stored. Before each data encryption transmission, the transmission end collects device environment data, user behavior log data, and multimedia data for denoising and standardization, and extracts the detection time and frequency domain features. , as well as Forming comprehensive time-frequency domain features for detection , calculate and detect the comprehensive time-frequency domain features The hash value of the transmission end is combined with the comprehensive time-frequency domain features collected during the last data transmission The hash value is compared and the comparison threshold is set. When the comprehensive time-frequency domain features are detected and integrated time-frequency domain features When the hash value difference is higher than the comparison threshold, the identity metabolism operation is automatically triggered;

[0087] The transmission end detects time-frequency domain features , as well as Generate a new user behavior fingerprint and update the encryption key K based on the new user behavior fingerprint;

[0088] The encryption key K is split into key fragments using a key sharing algorithm (Shamirs Secret Sharing algorithm), and the key fragments are stored in distributed cloud server nodes.

[0089] By collecting device environment data, user behavior log data, and multimedia data, the system can obtain comprehensive user behavior and device usage information. De-noising and standardization processing ensure the high quality and consistency of the data and eliminate the interference of external noise on data analysis. The time-frequency domain features extracted on this basis can reflect the dynamic changes of user behavior and device environment, and provide accurate basic data for subsequent identity metabolism. By hashing the currently collected comprehensive time-frequency domain features, generating a hash value, and comparing it with the comprehensive time-frequency domain feature hash value at the time of the last data transmission, the system can quickly detect changes in user behavior and device environment. The purpose of setting the comparison threshold is to filter out tiny and irrelevant For important changes, the identity metabolism operation will only be triggered when the change amplitude exceeds the threshold. This mechanism can effectively prevent false alarms and ensure the system's timely response to significant behavioral changes. When the hash value comparison results show that the user behavior or device environment has changed significantly, the system automatically triggers the identity metabolism operation. This operation generates a new user behavior fingerprint based on the new time-frequency domain features, and updates the encryption key K based on this. This dynamic key update mechanism ensures that the security of data transmission can still be guaranteed even when user behavior changes. In addition, the dynamically updated encryption key is highly unique and random, which further enhances the anti-cracking ability of data encryption. The encryption key K is divided into several key fragments using the Shamirs Secret Sharing algorithm, and these fragments are distributed and stored in different cloud server nodes. This key management method greatly improves the security of the key, because even if a single node is attacked or leaked, the complete key cannot be restored.

[0090] S5. Use stealth communication to select a frequency band for encrypted data transmission. After receiving the cloud photo frame data, the receiving end decrypts and verifies it before storing it;

[0091] Specifically, using stealth communication to select a frequency band for encrypted data transmission means encrypting the disordered cloud photo frame data and then using the Tor network to generate a Tor routing path to hide the source and destination addresses of the data packets to transmit the encrypted disordered cloud photo frame data;

[0092] The transmission segment monitors the spectrum channel occupancy in real time during data transmission, selects idle spectrum channels for encrypted cloud photo frame data transmission, and implements key exchange between the transmitter and receiver through elliptic curves based on the TLS 1.3 protocol.

[0093] After encrypting the cloud photo frame data, the multi-layer routing path generated by the Tor network can effectively hide the source and destination address of the data packet. The Tor network uses multi-layer encryption technology, and each layer is encrypted and decrypted through different relay nodes, making the transmission path complex and difficult to track. This feature ensures the high privacy and security of the data during transmission. Even if the data packet is intercepted during transmission, the attacker cannot easily know the source or destination of the data. When the transmission end is transmitting data, it can dynamically select the optimal idle channel for data transmission by real-time monitoring of the occupancy of the spectrum channel. This process ensures the stability and efficiency of data transmission and avoids data transmission failure or delay caused by channel congestion or interference. Through intelligent channel selection, the system can flexibly adjust the transmission strategy in different network environments to ensure the continuity and high quality of data transmission. During the data transmission process, the TLS 1.3 protocol uses the elliptic curve Diffie-Hellman key exchange (ECDHE) to achieve secure key exchange between the transmission end and the receiving end. This process ensures that the transmission parties can securely negotiate a shared key without directly transmitting the key, thereby protecting the confidentiality of the data. TLS The improved handshake process of the 1.3 protocol not only improves communication efficiency, but also reduces the possible attack surface, making data transmission more secure and reliable.

[0094] Furthermore, the receiving end decrypts and verifies the cloud photo frame data after receiving it, and then stores it. The receiving end receives the encrypted random cloud photo frame data and uses the encryption key K to decrypt and obtain the random cloud photo frame data, and stores the random cloud photo frame data according to the random order. and The disordered cloud photo frame data is reordered to generate cloud photo frame data, and the hash value of the generated cloud photo frame data is calculated and compared with the hash value of the original cloud photo frame data to determine the integrity of the cloud photo frame data. If the receiving end determines that the integrity is passed, the cloud photo frame data is stored in the database.

[0095] After receiving the encrypted disordered cloud photo frame data at the receiving end, the encryption key K is first used for decryption. The decryption process restores the encrypted data to the original disordered cloud photo frame data. Since the encryption key K is generated by the user identity fingerprint, device fingerprint and behavior fingerprint, it is highly unique and complex. Therefore, even if the encrypted data is intercepted during transmission, unauthorized third parties cannot decrypt the data. This step ensures the confidentiality of the data. Even if the data is transmitted in an insecure network environment, a high level of security can still be maintained. After decrypting the disordered cloud photo frame data, the receiving end reorders the data according to the pre-generated random order. This process restores the data blocks to the original arrangement order and generates complete cloud photo frame data. Due to encryption and disorder, The reordering process increases the complexity and security of the data, so this reordering process ensures the readability and correctness of the data after transmission. Even if the attacker obtains the data during the transmission process, the data cannot be correctly restored due to the lack of random order and key. After reordering to generate the cloud photo frame data, the receiving end calculates the hash value of the generated data and compares it with the hash value of the original cloud photo frame data. In this way, the system can determine whether the data has been tampered with or damaged in any way during the transmission process. The hash value comparison process is efficient and accurate, and can quickly detect any minor changes, thereby ensuring the integrity and reliability of the data. If the verification is successful, it can be confirmed that the data has maintained its integrity during transmission and can be safely stored in the database.

[0096] Example 2

[0097] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0098] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0099] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0100] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0101] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A cloud photo frame data end-to-end secure encryption transmission method based on security requirements, characterized by: include, The user performs identity authentication at the transmission end to generate an identity fingerprint, and generates a device fingerprint based on the transmission end device parameters, and binds the identity fingerprint to the device fingerprint; The transmission end collects environmental data and user behavior data to generate user behavior fingerprints; Generate a key based on user identity fingerprint, device fingerprint, and behavior fingerprint, and scramble the cloud photo frame data for encryption; Detect changes in user identity, perform identity metabolism and key updates, and store keys in a distributed manner; Stealth communication is used to select the frequency band for encrypted data transmission. The receiving end receives the cloud photo frame data and decrypts and verifies it before storing it; The user performs identity authentication at the transmission end to generate an identity fingerprint, and generates a device fingerprint based on the transmission end device parameters. Binding the identity fingerprint with the device fingerprint means that the user enters the user identity information to register the identity when using the transmission end, and the transmission end uses a hash algorithm to generate a user identity hash value as the user identity fingerprint S based on the user identity information, and collects the transmission end device hardware information and system parameters to generate a device fingerprint R using a hash algorithm, binds the user identity fingerprint S and the device fingerprint R, and stores them in a secure database; The transmission end collects environment data and user behavior data to generate user behavior fingerprints, which means that the transmission end collects device environment data through device sensors, collects user behavior log data and multimedia data on the transmission end device, and performs noise cleaning and standardization on the device environment data, multimedia data and behavior log data through a master-slave adaptive notch filter; The dual-tree complex discrete wavelet transform is used to extract the time-frequency domain features of device environment data, behavior log data and multimedia data respectively. , as well as ; The Lorentz system is used to perform initial chaotic mapping on the time-frequency domain features: in , as well as is the initial value of the chaotic sequence; The initial value update step of the chaotic sequence is calculated by the Lorentz equation, and the time period T is discretized and divided into time periods , use the Runge-Kutta method to calculate the , as well as , and generate the chaotic sequence C of time period T: The time-frequency domain features , as well as Splicing to form comprehensive time-frequency domain features , use the chaotic sequence C to encrypt the comprehensive time-frequency domain features: Where B is the chaotic encryption feature vector; Set the initial distance threshold , map the chaotic encrypted feature vector B to the high-dimensional space, take each element in the chaotic encrypted vector B as a point in the high-dimensional space, and connect the points whose distance in the high-dimensional space is less than the initial distance threshold The points form a simple complex; Gradually increase the distance threshold , and at each distance threshold The following computes the homology group of a simple complex, with each simple complex as a data set, where computing the homology group includes: Zero-order homology group: the number of connected branches in the response data set; First-order homology group: one-dimensional ring hole structure in the reaction data set; Second-order homology group: reflects the high-dimensional hole structure in the data set; Record the appearance and disappearance time of the simple complex homology group at different distance thresholds and form a persistence bar graph, identify the simple complex homology group represented by the longest bar in the persistence bar graph, and extract the simple complex homology group as the topological feature P; Construct a relational two-layer aggregation graph convolutional network, set the relational two-layer aggregation graph convolutional network input as topological feature P, and output as behavioral feature X, use the training set to train the relational two-layer aggregation graph convolutional network, and input the topological feature P into the relational two-layer aggregation graph convolutional network to obtain the behavioral feature X; Use the topological simplicity algorithm to denoise the behavior feature X to generate a simple topological feature ; Simple topological features Apply SHA-256 hashing to generate user behavior fingerprint D.

2. The end-to-end secure encryption transmission method for cloud photo frame data based on security requirements as claimed in claim 1, characterized in that: Generating a key based on the user identity fingerprint, device fingerprint and behavior fingerprint refers to obtaining the user identity fingerprint S, device fingerprint R and behavior fingerprint D and combining them to generate an encryption key K using the SHA-256 hash algorithm: 。 3. The end-to-end secure encryption transmission method for cloud photo frame data based on security requirements as claimed in claim 2, characterized in that: The scrambling of the cloud frame data for encryption refers to the transmission end obtaining the cloud frame data that needs to be encrypted for transmission, and extracting the video data, picture data, audio data and text data from the cloud frame data respectively; Divide the video data into video data blocks of the same size according to the video frame height, width and number of frames, divide the picture data into picture data blocks of the same size according to the pixel size, divide the audio data into audio data blocks of the same length according to the time period, divide the text data into text data blocks according to the phrases, and record the initial arrangement order Q of the data blocks; The transmission end generates a random sequence Reorder the data blocks of video data, image data, audio data, and text data respectively, and generate a random order Reorder all data blocks into disordered cloud photo frame data; The random cloud photo frame data is encrypted using the encryption key K.

4. The end-to-end secure encryption transmission method for cloud photo frame data based on security requirements as claimed in claim 3, characterized in that: The detection of user identity changes to perform identity metabolism and key update, and the distributed storage of keys means that before each data encryption transmission, the transmission end collects device environment data, user behavior log data, and multimedia data for denoising and standardization, and extracts detection time-frequency domain features. , as well as Forming comprehensive time-frequency domain features for detection , calculate and detect the comprehensive time-frequency domain features The hash value of the transmission end is combined with the comprehensive time-frequency domain features collected during the last data transmission The hash value is compared and the comparison threshold is set. When the comprehensive time-frequency domain features are detected and integrated time-frequency domain features When the hash value difference is higher than the comparison threshold, the identity metabolism operation is automatically triggered; The transmission end detects time-frequency domain features , as well as Generate a new user behavior fingerprint and update the encryption key K based on the new user behavior fingerprint; The encryption key K is split into key fragments using a key sharing algorithm, and the key fragments are stored in the distributed cloud server nodes.

5. The end-to-end secure encryption transmission method for cloud photo frame data based on security requirements as claimed in claim 4, characterized in that: The use of stealth communication to select a frequency band for encrypted data transmission refers to encrypting the disordered cloud photo frame data and then using the Tor network to generate a Tor routing path to hide the source and destination address of the data packet to transmit the encrypted disordered cloud photo frame data; The transmission segment monitors the spectrum channel occupancy in real time during data transmission, selects idle spectrum channels for encrypted cloud photo frame data transmission, and implements key exchange between the transmitter and receiver through elliptic curves based on the TLS 1.3 protocol.

6. The end-to-end secure encryption transmission method for cloud photo frame data based on security requirements as claimed in claim 5, characterized in that: The receiving end receives the cloud photo frame data and then decrypts and verifies it before storing it. The receiving end receives the encrypted random cloud photo frame data and then uses the encryption key K to decrypt and obtain the random cloud photo frame data, and stores it according to the random order. and The disordered cloud photo frame data is reordered to generate cloud photo frame data, and the hash value of the generated cloud photo frame data is calculated and compared with the hash value of the original cloud photo frame data to determine the integrity of the cloud photo frame data. If the receiving end determines that the integrity is passed, the cloud photo frame data is stored in the database.

7. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the end-to-end secure encryption transmission method for cloud photo frame data based on security requirements described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the cloud photo frame data end-to-end secure encryption transmission method based on security requirements described in any one of claims 1 to 6 are implemented.

Images