276 results about "Random seed" patented technology

The invention discloses an anti-counterfeiting traceability system and method based on Beidou satellite positioning and a block chain. The method comprises the following steps: generating a current positioning data tuple; performing validity judgment on the satellite signal quality index; generating current event summary information, performing encryption calculation on the event summary information by using a private key embedded in a commodity and improving a verifiable random function, and outputting a random seed and a verification proof; constructing an event fingerprint and generating an event signature by using a private key; a challenge request is generated when the commodity request state is migrated, the commodity end device responds to the challenge request and collects new Beidou positioning information, and a challenge response data packet is constructed; and performing consistency verification on the challenge response data packet according to the virtual state resume smart contract, writing the verified data into a geographic sub-chain, recording an event abstract hash path by a main chain, and updating a commodity state transfer path. According to the invention, based on Beidou positioning and an improved verifiable random function, the credible verification of the commodity position and the traceable management of the state are realized.

The invention discloses a fine tuning system of a large-scale pre-training model in a federated learning environment and application thereof. The system comprises a local disturbance gradient estimation module, a differential privacy protection module and a global model aggregation and update module. The local disturbance gradient estimation module is used for calculating a global model loss value by combining forward propagation with a zero-order optimization method so as to estimate a gradient and realize all-parameter fine tuning; the differential privacy protection module performs differential privacy protection processing on the estimated disturbance gradient to prevent gradient information from leaking user sensitive data; and the global model aggregation and update module reconstructs a disturbance vector and completes global model update based on a random seed and a scalar gradient uploaded by a client. Compared with the prior art, on the premise of not depending on back propagation, all-parameter fine tuning of a large-scale pre-training model is achieved, data privacy is guaranteed, meanwhile, calculation and memory expenses are remarkably reduced, and the method is suitable for a resource-limited distributed calculation environment.

The invention relates to a master-slave communication technology, and discloses an automatic slave address allocation method of a master-slave communication system and the master-slave communication system, and the method comprises the following steps: when all slave addresses need to be reallocated, a host sends a first broadcast frame containing a first instruction identifier and a random seed; and after receiving and analyzing, each slave generates random delay and a slave address in combination with the unique identifier of the slave and the random seed, and sends a response frame containing the slave address and equipment information to the host according to the random delay. And the host receives the response frame, updates the slave equipment information table and establishes a special communication link for the slave. And when a new slave is accessed, the host sends a second broadcast frame containing a second instruction identifier, a random seed and an allocated address list. And the slave with the allocated address does not respond, the new slave generates an address sending response frame according to a rule, and the host establishes a link for the new slave. The invention further discloses a computer readable storage medium. The invention aims to improve the efficiency of allocating addresses to the slave by the master-slave communication system and facilitate the expansion and access of the new slave.

The invention discloses a small sample modeling stability evaluation method and system based on Bootstrap resampling, and particularly relates to the technical field of computers and data intellectualization, the method comprises the following steps: completing data preprocessing and stable stage identification under a unified time base, and forming a segment set; an average block length is used as a decision quantity, an SBB is optimized to construct a sample space, and an optimal block length is determined in combination with variance consistency and nominal coverage rate consistency criteria; carrying out re-sampling training around short / medium / long scales, and collecting layered indexes such as prediction, parameters and features; stability calculation and empirical coverage rate calibration are completed based on intra-scale statistics and inter-scale weighted mixing, and a pseudo-stationary diagnosis score is output; and finally, engineering judgment and backspacing optimization are carried out according to the coverage rate, the correlation maintenance and the risk threshold. The system records random seeds, block metadata and model version generation evidence pointers, has the characteristics of traceability and reverifiability, and is suitable for scenes of production lines, network traffic, finance and the like.

The invention discloses a digital archive multi-protection-based data security transmission method, and relates to the field of data security transmission, and the method comprises the steps: generating a random seed in real time based on a quantum state, carrying out the iterative blocking of the quantum seed through combining with Logistic chaotic mapping, outputting variable-length secret key fragmentation, carrying out the symmetric encryption of archive data through employing a lightweight SM4 algorithm, and carrying out the encryption of the archive data. Obtaining encrypted data of the fragments; a behavior anomaly probability is calculated through a hidden Markov model, a risk score is calculated through a safety interval, and when a score value exceeds a safety threshold value, a grading response is triggered; key fragments are generated by adopting n nodes, at least k fragments can reconstruct signatures to monitor the node state of the block chain, and when the node anomaly rate exceeds a node threshold value, a three-layer architecture is automatically switched. The method has the advantages that an active multi-protection system is constructed through a quantum dynamic key, risk-driven hierarchical response and block chain disaster recovery switching, and quantum attack resistance and dynamic adaptation to transmission of network risks are realized.

The invention discloses a mobile payment encryption system based on a data random algorithm, and the system comprises a collection module which is used for collecting multi-modal data, and constructing a cross-modal entropy source pool; the random seed generation module is used for extracting feature vectors from the cross-modal entropy source pool and generating initial random seeds; the time sequence self-adaptive random chain module is used for generating a random factor sequence based on the initial random seed recursion and forming a random chain structure; the encryption module is used for jointly encoding the random factor sequence and transaction data to be encrypted, generating a dynamic encryption parameter set and encrypting the transaction data; and the chain type updating module is used for collecting network environment parameters, updating the time sequence self-adaptive random chain and generating a new dynamic encryption parameter set so as to form a chain type evolved encrypted data sequence. According to the method, the cross-modal entropy source and the time sequence random chain are utilized to realize dynamic encryption of the mobile payment data, and the method has the advantages of high entropy randomness, strong attack resistance and high transmission security.

The invention discloses a jewelry transaction data security and privacy protection method based on a 5G fusion application. The method comprises the following steps: establishing a distributed database; generating a first transaction public key and a first transaction private key of two transaction parties through a pre-established first asymmetric encryption algorithm; carrying out random multivariate function calculation on the first transaction public key and the first transaction private key of the two transaction parties, and desensitizing the random multivariate function through an encryption algorithm and storing the random multivariate function in a distributed database; solving a random seed of the random multivariate function as a second transaction private key of the two transaction parties through a key derivation function of a second Hash algorithm; in the encryption period, the transaction data of the two transaction parties are encrypted through an encryption algorithm, and meanwhile, the encrypted data are desensitized and stored; in the decryption period, the two transaction parties obtain plaintext data through the second transaction private key; on the basis of an existing encryption algorithm, the neural network algorithm is introduced to generate the purchaser key, and meanwhile, desensitization processing is performed on the ciphertext data, so that jewelry transaction data is safer.

The invention provides a parallel multi-target random parameter optimization method and device.The parallel multi-target random parameter optimization method comprises the steps that the number of nodes of a parallel computing cluster is configured, and a random seed sequence is generated; carrying out structure loading and compiling on the to-be-optimized model, and obtaining global configuration of the to-be-optimized model; sending the global configuration to all processes, generating an initial parameter population meeting the constraint of the global configuration by each process based on the random seed, and performing serial evaluation to obtain a target function value; independently iteratively executing shuffling complex evolution by each process until a convergence condition is met; each process outputs the respective optimal parameter vector and the corresponding objective function value to a root process, and a global optimal solution is determined through the root process. By means of the method and device, the accuracy and efficiency of parameter optimization can be remarkably improved, and the problem that the model optimization effect is poor in the prior art is solved.

A system and methods are provided for encrypting and decrypting data payloads, receiving an unencrypted data payload; generating a random seed value; generating in FPGA firmware an encryption hash key from seed parameters including the seed value, XORing the encryption hash key with the unencrypted data payload to generate an encrypted data payload; transmitting the encrypted data packet with the seed value and the encrypted data payload to a second FPGA that regenerates the hash key from the see parameters and XORing the hash key with the encrypted data payload to regenerate an unencrypted data payload.

The invention discloses a secondary power modulus-based post-error-correction-free quantum lattice signature method, which comprises a counter, and comprises the following steps of: S1, sampling a random seed, and expanding the seed through a hash function to obtain a byte stream element I, a byte stream element II and a byte stream element III; s2, calculating and generating a public key and a private key based on the throttling element I, the byte stream element II and the byte stream element III; s3, assigning initial values to the counter and the signature, and calculating and generating a hash value I and a hash value II based on the private key and the signature message; s4, performing signature loop calculation based on the hash value I, the hash value II, the private key and the initial value of the counter to generate a legal signature final value, and returning the legal signature final value; s5, a signature verification variable is generated through calculation based on the public key and the signature final value, if the signature verification variable meets a verification condition, signature verification is passed, otherwise verification is not passed, the post-quantum lattice signature calculation efficiency, bandwidth optimization and stability are greatly improved, and a more efficient solution is provided for practical application.

The invention discloses a dynamic encryption and security level adaptive adjustment method for flight data, particularly relates to the field of data security, and is used for solving the security and integrity problems of the flight data in an unstable network environment. According to the method, the mass of random seeds is quantified in real time in the mixing process, incremental seeds are added once the dispersion is reduced, and weak random source transmission is blocked; the historical track continuously provides threshold correction, so that the secret key updating rhythm and the link fluctuation are kept at the same frequency; a new secret key with sufficient safety margin is output by depending on a structured spin mixing and real-time source supplementing mechanism, and a secret key material keeps double characteristics of high discretization and high independence, so that randomness attenuation caused by packet missing, retransmission or node out-of-step is avoided; meanwhile, security level adjustment logic is deeply coupled with a key generation process, so that adaptive balance of encryption strength and operation load is realized, and the flight data is enabled to maintain collaborative excellent performance of confidentiality, integrity and real-time performance in a complex environment.

The embodiment of the invention provides a large language model optimization method based on variance reduction and momentum acceleration. The method comprises the following steps: a gradient estimation stage of a large language model: initializing a seed list and a projection list; executing multiple independent query iterations, calling a disturbance subprogram, generating a random seed for the large language model, and storing a table; in the disturbance subprogram, the determined gradient projection value is stored in a projection list, and iteration is carried out for next query; after executing multiple independent query iterations, storing a plurality of random seeds and a plurality of gradient projection values corresponding to the random seeds; in the weight updating stage of the large language model, a gradient norm subprogram is called for each layer of the large language model, and a random seed is obtained to reset a random number generator; and determining variance-reduced gradient estimation according to the gradient projection value extracted from the projection list and the reproduced disturbance vector. According to the embodiment of the invention, gradient information queried for multiple times is aggregated to generate low-noise gradient estimation, and fine tuning of a large language model is completed.

The invention discloses a security encryption method, device, equipment and medium for a vehicle-gauge-level HSM, and relates to the technical field of vehicle network security, and the method comprises the steps: obtaining a target algorithm parameter corresponding to a target encryption task in an algorithm parameter register after receiving the target encryption task, and determining a corresponding encryption algorithm type; if the target algorithm type is a post quantum cryptography algorithm type, a true random number generation component of a vehicle gauge level HSM is called through a post quantum cryptography coprocessor to generate a true random seed, and a target encryption task is executed based on the target algorithm parameter and the true random seed; in the process of executing the target encryption task, second-order mask operation is carried out on the fast number theory transformation operation, whether error injection attacks exist or not is continuously detected in the sampling process, and operation results of the key steps are stored through a preset composite register set.

The invention discloses a security measurement system for data transmission encryption, and particularly relates to the field of data disaster tolerance, and the system comprises an identification module, an acquisition module, a seed generation module, an AST construction module, a variation module, a verification module and a packaging module. The authentication module verifies whether the equipment or the software is in an unauthorized state or not by constructing a preposed feature vector; and the acquisition module is used for acquiring the internal environment characteristics and the external environment characteristics of the equipment or the software when the judgment result of the identification module is in an unauthorized state, splicing the internal environment characteristics and the external environment characteristics into character strings, calculating a hash value for the splicing result of the character strings through a hash function, and outputting an environment entropy character string. Through AST variation based on environment entropy and double random seed drive, dynamic randomization of backup and recovery scripts is realized, supply chain attack is prevented, even if an attacker tampers a configuration file, the script security is relatively ensured by a dynamic generation and key protection mechanism depended in the recovery process, and thus the integrity of data disaster tolerance is ensured.

The invention relates to the technical field of data encryption and decryption, in particular to a solid state disk data encryption and rapid decryption co-processing system which comprises a key management module, a task scheduling distribution module, a cache optimization module, an identity verification module and a controller monitoring module. According to the method, the unique random seed is generated by combining the user identity characteristic parameters and the SSD controller state, the key attribute is dynamically adjusted by periodically disturbing the data, the key complexity and safety are improved, cross matching is carried out according to the concurrent task priority and the data safety level in the task scheduling link, and the complexity and safety of the key are improved. The method comprises the following steps: distributing special channels and buffer resources, ensuring that a key task has a higher execution priority, performing cache optimization, dynamically dividing a multi-level cache region, realizing priority scheduling and processing of a high-frequency data block according to the access frequency of the data block, fusing authentication parameters and equipment and environment information in an identity authentication link, realizing multi-identity verification, and realizing high-efficiency and high-efficiency scheduling of the high-frequency data block according to the access frequency of the high-frequency data block. And the data decryption authority is strictly controlled.

The invention discloses a fine tuning system of a large-scale pre-training model in a federated learning environment and application thereof. The system comprises a local disturbance gradient estimation module, a differential privacy protection module and a global model aggregation and update module. The local disturbance gradient estimation module is used for calculating a global model loss value by combining forward propagation with a zero-order optimization method so as to estimate a gradient and realize all-parameter fine tuning; the differential privacy protection module performs differential privacy protection processing on the estimated disturbance gradient to prevent gradient information from leaking user sensitive data; and the global model aggregation and update module reconstructs a disturbance vector and completes global model update based on a random seed and a scalar gradient uploaded by a client. Compared with the prior art, on the premise of not depending on back propagation, all-parameter fine tuning of a large-scale pre-training model is achieved, data privacy is guaranteed, meanwhile, calculation and memory expenses are remarkably reduced, and the method is suitable for a resource-limited distributed calculation environment.

The invention relates to a physical unclonable function modeling attack resisting method based on a neural network. The method comprises the following steps: generating a multi-bit random bit stream by using a random seed generated by an LFSR module, and simultaneously acting PUF excitation on a plurality of physical unclonable functions to generate a multi-bit physical unclonable function response; the method comprises the following steps: constructing an initial feature matrix by utilizing a Decoder module, and equally dividing position decoding excitation into a plurality of binary position decoding sub-excitation with a certain length; sequentially guiding multi-bit physical unclonable function responses to storage units of corresponding addresses to form a physical unclonable function response feature matrix, and inputting the physical unclonable function response feature matrix into the neural network model to obtain one-bit confusion response; and the other bit of confusion response of the random seed is reutilized until a final confusion response sequence with a preset bit is generated, and the server compares the final confusion response sequence with an expected confusion response to complete identity authentication. By adopting the method, the hardware security can be improved.

A data processing method performed on a verification node and including obtaining task event information and a signature for the task event information from a scheduling node. The task event information includes a first computing node set participating in execution of a computing task and a random seed provided by a task submission end of the computing task. The method further includes performing verification on the signature, deriving a random number set based on the random seed in response to the verification of the signature succeeds, selecting, for the computing task, a second computing node set associated with the random number set, verifying validity of the first computing node set by using the second computing node set, to obtain a verification result, and adding a block including the verification result to a blockchain of a verification node cluster including the verification node.

The invention provides a sequence decision strategy acquisition method and system for high-neuroplasm crowds. The method comprises the following steps: establishing a full-screen drawing canvas, and loading a task set comprising a decision map type, an emotion titer ratio, a reward parameter, a random seed and a sequence control parameter; collecting user gender selection and binding a corresponding emotion picture library; generating a decision map based on a Markov decision principle under the control of the parameter set, defining nodes and connecting edges, randomly scheduling and laying emotion pictures according to a titer ratio, and assigning rewards to form an emotion and reward coupling environment; based on the environment, recording a mouse position and a mouse path in an execution exploration stage, recording key pressing time, an action direction and an award in an execution stage, and establishing step sequence association according to a timestamp to form sequence decision data; a Latin square balance control task sequence is adopted, and the process is propelled under the trigger of a user; and finally, filing the data in a binary system and a text file for subsequent analysis. According to the invention, the authenticity and repeatability of data are improved.

The invention provides a Python bytecode obfuscation method and system based on dynamic screening and random replacement, and the method comprises the steps: calculating a plurality of safety indexes and a comprehensive safety index SDI of bytecode files, and screening out the bytecode files which have high safety requirements and need to be obfuscated; a linear congruence generator is constructed, a pseudo-random number sequence is provided for obfuscation operation of byte codes, and operation code dynamic replacement obfuscation operation is performed in byte code instructions by generating random seeds, constructing random dynamic operation code mapping tables and applying the independent operation code mapping tables to different byte code files needing to be obfuscated; and performing obfuscation state detection on the obfuscated bytecode file, if the detection result is the obfuscated bytecode file, modifying bytecode analysis and instruction scheduling logic, increasing a runtime de-obfuscation process, embedding an Opcode reflection mechanism, re-compiling to generate a customized Python interpreter supporting the execution of the obfuscated bytecode, and executing the obfuscated bytecode file. According to the invention, byte codes can be confused.

The invention provides an automatic test and quality evaluation system for game development. The system improves the diversity of test coverage and the precision of physical logic error detection. Firstly, a collision recovery coefficient and an initial random seed are collected through a physical engine bottom layer interface, and a standard physical parameter set is constructed; secondly, generating a test case set by adopting disturbance offset and a random trigger mechanism, and realizing scene disturbance simulation; and finally, performing difference analysis on a test result by calculating time sequence data such as a period, event response time and a frame rendering interval, and automatically correcting physical parameters and a trigger sequence based on feedback to realize closed-loop optimization. According to the method, three types of data including a periodic sequence, an event response timestamp sequence and a frame rendering interval sequence are introduced, a frame-level physical logic consistency criterion is established, and the problems of abnormal performance fluctuation, event delay and frame imbalance are finely captured through a combined judgment mechanism of a difference index and a tolerance threshold.

The invention discloses an automobile ECU and UDS security diagnosis method and system based on a dynamic session key, and the method comprises the steps: in the diagnosis process, the ECU generates challenge information containing a random seed, a unique session identifier and an initial counter, and calculates an expected key in combination with a hardware identifier and a main key; and the diagnostic instrument calculates a client key and returns the client key by using the same hardware identifier, the master key and the received challenge parameters, and the ECU completes authentication by checking the session identifier, the counter and the key. And after the authentication is passed, the two parties independently derive a temporary session key by taking the master key as a root key and combining the dynamic factor of the session. When the diagnostic instrument sends a request, the counter is increased progressively, an authentication code is generated in combination with the session key, and the request, the authentication code and the counter are sent to the ECU; and the ECU checks the continuity of the counter and the consistency of the authentication code to verify the legality of the request, and also generates the authentication code to be verified by the diagnostic apparatus when sending a response, thereby completing bidirectional verification. And the diagnosis safety level of vehicle-mounted electronic equipment and the like is improved.

The invention provides a traceable notarization lottery system and method based on a national secret algorithm, and the method comprises the steps: collecting and preprocessing user behavior data, and constructing user behavior features; dividing a pre-constructed numbering pool into a plurality of sections of disturbance blocks, and generating disturbance seeds of each section according to the high-entropy hash random seeds in combination with user behavior characteristics; constructing a structured receipt according to the chain flow structure and the number distribution list, and combining and constructing a verification instruction structure according to the structured receipt; and when the user generates questions, after the questions of the user are received, reconstructing a lottery environment based on the verification instruction structure, and generating a final number sequence obtained by local replay reconstruction of the user. The method solves the core problems that an existing lottery system cannot be audited, verified and reconstructed, and the like, has obvious systematic innovation characteristics, and is suitable for core resource allocation scenes with strong supervision requirements.

The invention provides a watermark detection method and device based on Bayesian detection. The method comprises the steps of obtaining a target text to be subjected to large model watermark detection and a cue word corresponding to the target text; processing a token sequence spliced by the cue word and the target text through a language model to obtain probability distribution output by the language model for each token position; for the jth token in the T tokens of the target text, selecting the first k tokens before the jth token to be input into the hash function, and obtaining a random seed of the jth token; dividing a word list of the large model into a preferential selection set and a non-preferential selection set based on random seeds; on the basis of preset watermark bias, preferentially selecting the set and the probability distribution, and generating probability distribution after disturbance of the jth token; calculating a log-likelihood ratio by using the probability distribution before and after disturbance, and accumulating the log-likelihood ratio to a detection score of the current text; and if the detection score is higher than the threshold value, judging that the target text has the watermark of the large model.

The invention relates to the technical field of electronic security, and discloses a vehicle-mounted ECU identity authentication method and system based on a multi-factor random seed and multiple security levels, and the method comprises the steps: synthesizing a random seed at an ECU end through multiple factors such as CRC32, a random number, a timestamp and a global seed, and returning the random seed to a client; the client calculates a key and returns the key by using a corresponding key algorithm according to the target security level; the ECU calculates an expected key through the same algorithm and compares the expected key with a client key, if the expected key is consistent with the client key, the session of the level is opened and the delay timer is closed, if the session fails, the number of failure times of the level is accumulated, and if a threshold value is reached, the delay timer of the corresponding security level is started and the access of the level is locked until the timing is finished; and meanwhile, the authentication result, the security level, the timestamp, the failure count and the seed state are written into a security log. According to the method, it is guaranteed that each time of seed is unpredictable and is strongly correlated with the historical state, a non-zero recalculation mechanism is matched, hidden dangers of fixed seeds, replay attacks and prediction attacks are thoroughly eliminated, and attacks can be protected, and events can be traced.

A method and arrangement for converting a digital file into a cryptographic challenge-response-pair mechanism is disclosed. An encrypted digital file is concatenated with a random nonce and subject to one-way cryptographic functions and / or extended output functions such that a result C* is obtained having a known bit length. This result is organized into a series of addressable segments. A random seed is generated and is used to derive a random bit stream that is parsed into segments, each of which is read as an address in C*. These segments are applied as challenges to C*, and the corresponding responses may be used as or to generate or store an encryption key.

The invention discloses a post-processing device of a true random number generator, which comprises a random seed simulation module used for generating random seeds; the first processing module is used for performing accelerated multiplication on each random seed group formed by the random seeds and each original random number sub-vector formed by the original random number vector output by the true random number generator based on fast Fourier transform to obtain a first result corresponding to each random seed group; or extracting bits in the original random number vector through a single-bit extractor according to each random seed group formed by the random seeds to obtain a first result corresponding to each random seed group; splicing all the first results to obtain an intermediate result; and the second processing module obtains an actual random number vector according to the intermediate result. According to the method, bit correlation defects can be processed, original random numbers from a plurality of correlative entropy sources can be processed, and the method has unconditional security independent of any calculation hypothesis or random model.

The invention discloses an industrial park weather typing method and device integrating a self-organizing mapping neural network and a k-means algorithm, and the method comprises the steps: firstly, constructing an initial data set through air quality monitoring data and ERA5 meteorological reanalysis data, converting the data into a numerical value type, and carrying out the standardization, so as to guarantee that the feature data has the distribution with the mean value being 0 and the standard deviation being 1; thirdly, initializing a self-organizing mapping (SOM) model, and performing dimension reduction on the data by setting parameters such as grid size, sigma and learning rate so as to obtain two-dimensional feature representation; then, initializing a k-means clustering model, setting a clustering number and a random seed, and performing clustering analysis on the feature data after dimension reduction by using a fitpredict () method so as to obtain a clustering label of each sample; in the evaluation and visualization stage, evaluation indexes such as SSE, MSE, RMSE, a contour coefficient and a Clinski-Harabasz index are calculated so as to evaluate the clustering effect and the separation degree. And finally, adding a clustering label into the original data, and visually analyzing the distribution of each feature under different clusters by using a violin chart, thereby providing a visual basis for further analysis. According to the method, through integration of the SOM and the k-means model, the precision of ozone concentration weather typing of the small-scale industrial park is remarkably improved, and the high-concentration ozone weather is divided more finely and more accurately.

This application provides a method and apparatus for generating random instruction test cases, relating to the field of automated testing technology. First, target instructions or instruction sequences are encapsulated into C inline functions using inline assembly or macro definition, forming a unified function library. Next, a function parameter constraint system is constructed, defining parameter types, address ranges, instruction combination methods, and value range constraints for each encapsulated C inline function. Then, a multi-level nested structure is built based on the C inline functions to organize the instructions, where each level node is associated with a corresponding C inline function name and constraints. Finally, based on a YAML configuration file, the target C inline functions are determined from the multi-level nested structure and combined to generate random instruction test cases. The YAML configuration file includes at least the number of iterations, a random seed, a random strength factor, and test content configuration. The solution provided in this application has the advantages of improved verification efficiency and applicability to different scenarios.

The present invention belongs to the field of data processing technology, and discloses a data transmission method, system, computer equipment, and medium based on security verification. In the data transmission method based on security verification, the client calculates a signature for the request; the server encrypts, decrypts, and verifies the signature at the time of the request, determines whether it has access rights, and makes a service response. The data encryption of this solution can prevent most acts of espionage, increase the cost of espionage, and thus improve the integrity of the data; it is more suitable for mobile applications: Cookies do not support access from mobile phones; during network transmission, the performance is better; the encryption and decryption processes are all on the server side, avoiding the exposure of the encryption and decryption process. This solution utilizes random seeds, dynamic salt values, and AES encryption, which greatly improves the security of the secret key. The encrypted transmission method of this solution can be called by a third-party interface, and the dynamic secret key can be used independently for different services.