123results about How to "Reduce communication overhead" patented technology

The invention relates to the technical field of data distributed storage, and discloses a distributed storage method and system for clinical test data, and the method comprises the steps: carrying out the ring signature encryption processing of the patient test data of a plurality of data centers, and obtaining a patient anonymization identifier and standardized clinical data; performing two-stage random storage node configuration to obtain an optimal storage node configuration scheme; constructing an LPPMM-DA data aggregation model based on the optimal storage node configuration scheme, and performing fragmentation processing on the patient test data to obtain a plurality of target data fragments; performing dynamic operation envelope analysis and alliance graph game calculation on the multiple target data fragments to obtain a storage resource scheduling scheme; according to the storage resource scheduling scheme and the anonymization identifier, distributed data block storage and access authentication are executed on the multiple target data fragments, data block storage distribution information is generated, a complete data access control system is constructed, and safe and reliable storage of clinical test data is ensured.

The invention provides a privacy protection federated learning method and device based on multi-key fully homomorphic encryption, and the method comprises the steps: obtaining a public key matrix and a private key matrix corresponding to each client, and enabling the public key matrix and the private key matrix to be determined based on the public parameters of a global model in a server; carrying out aggregation processing on the public key matrixes of different clients to obtain an aggregated public key; acquiring a local model parameter of each client after encryption processing based on the aggregated public key; performing homomorphic encryption evaluation on local model parameters of different clients to obtain an aggregation ciphertext; partial decryption results of different clients are obtained, and the partial decryption results are obtained after the clients decrypt the aggregated ciphertext based on the private key matrix; and carrying out aggregation processing on the partial decryption results of different clients to obtain target model parameters, and updating the global model based on the target model parameters. Therefore, the communication overhead can be effectively reduced while the model precision is improved.

The invention provides a 5G intelligent networking method and system based on edge computing. The method comprises the following steps: deploying edge computing nodes, and designing a hierarchical network architecture and a dynamic routing mechanism; deploying an edge model on the edge computing node, and carrying out real-time processing and anomaly detection; dynamically distributing network resources, and carrying out network switching and load balancing by utilizing an edge model; decomposing the complex task into a plurality of sub-tasks, and performing distribution and parallel processing of the sub-tasks; updating and optimizing the edge model, and dynamically adjusting a networking strategy; and data security protection is realized, and network data and user feedback are monitored. The system comprises an edge deployment module, a data analysis module, a networking strategy module, a collaborative computing module, a collaborative optimization module and a security protection module. According to the invention, network resources can be efficiently utilized, intelligent networking and network switching are carried out, load balancing is realized, the response speed, the processing capability and the adaptive capability of the network are greatly improved, and the experience feeling of a user is improved.

An efficient low-altitude route authentication method for privacy protection comprises the following steps: in a system initialization stage, a trusted authority center configures system security parameters, initializes a Bloom filter, and sets a whistle station for data processing; in the air route verification information generation stage, the credible authority center defines a legal air route of the unmanned aerial vehicle for each whistle station and generates a Bloom filter. And the credible authority center generates elements by using the filter and sends the elements to the whistle station to prepare for subsequent verification. In the air route verification stage, the whistle station receives real-time flight data of the unmanned aerial vehicle, illegal air route elements are screened by using a Bloom filter, and the illegal air route elements are sent to the credible authority center after being processed. And the credible authority center verifies the received element, and confirms that the route is legal if the verification succeeds. The method ensures that only the unmanned aerial vehicle on a legal air route passes verification, protects the privacy of air route information, reduces calculation and communication overhead, adapts to an unmanned aerial vehicle environment with limited resources, and provides safe and efficient air route authentication.

The invention relates to the field of heterogeneous federal basic model adjustment, in particular to a clustering-based heterogeneous federal basic model adaptive fine adjustment method and a computer device. According to the scheme, a representative client is selected for each cluster through a multi-factor heterogeneous sensing clustering module, and the representative client selects a corresponding model as a cluster model according to own computing power limitation; searching an optimal sub-model based on the cluster model for all the clients in each cluster through a knowledge perception model architecture search algorithm, and deploying the optimal sub-model at the client; the method comprises the following steps of: uploading parameters to representative clients, aggregating corresponding parameters on the representative clients, issuing the aggregated parameters to clients in clusters, transmitting knowledge of each cluster to a server model through a knowledge transfer module perceived by the clusters, and returning the knowledge of the server model and updating the representative clients of each cluster through reverse knowledge distillation. The method is suitable for self-adaptive fine tuning of the heterogeneous federal basic model.

The invention discloses a lightweight vehicle intranet identity authentication and key negotiation method based on a PUF (Physical Unclonable Function). The method comprises a system initialization stage, a PUF registration stage, an identity authentication and key negotiation stage and a block chain synchronization stage. The method is oriented to an automobile domain architecture, direct low-cost identity authentication is carried out on the electronic control unit by utilizing the PUF (namely a physical unclonable function), the necessity of installing safety hardware on the electronic control unit can be eliminated, and secret key leakage is prevented; meanwhile, symmetric cryptography is adopted, a hash function, a pseudo-random function and a Lagrange interpolation formula are combined to balance the speed and safety of the key negotiation process of the electronic control unit, and compared with an existing vehicle intranet identity authentication and key negotiation method, the method has remarkable performance advantages.

The invention provides a fuzzy privacy set intersection method and system, a medium and electronic equipment, and the method comprises the steps: carrying out the encryption processing of an initial data set X through a homomorphic encryption algorithm, obtaining first data, and obtaining a processed data structure D through a defined space Hash algorithm; the receiver performs homomorphic addition or scalar multiplication operation processing on the first data by using own data Y, and performs confusion processing by using a random number R to obtain second data; after the sender carries out Augment algorithm processing on the random number, a Hash algorithm is carried out to obtain a corresponding Hash value, and the Hash value is recorded as third data to be sent to the sender; and the sender decrypts the second data, processes the second data through an Augment algorithm, executes a PSI protocol with the receiver to obtain fourth data, and compares the third data with the fourth data to obtain intersection data. According to the method, an addition homomorphic technology is adopted for a novel application scene of intersection of the fuzzy privacy set, and compared with a full homomorphic technology in the prior art, the time consumption is less, and the communication is small.

The invention relates to the technical field of distributed learning and wireless communication, in particular to an efficient communication federated learning model training method and system based on residual adaptive compression. The client obtains and updates the global model; obtaining a local model residual error by utilizing the local model memory set and the updated model weight; carrying out adaptive sparse compression on the residual error and transmitting the residual error to a server; the server selects a reliable client to carry out model weight prediction and aggregation; and broadcasting the updated global model to the client, and repeating the steps until convergence. Through adaptive compression and reliable client selection, the communication overhead is remarkably reduced, and the model convergence speed and the final performance are improved. The method is particularly suitable for edge computing scenes with limited resources, effectively solves the key problems of communication efficiency, model performance, privacy protection and the like in federated learning, and provides new possibility for wide application of federated learning.

The invention discloses a quantum federal collaborative distillation method, which comprises the following steps of: coordinating and serially connecting a whole federal learning process through a federal server, selecting clients participating in training in each round through a non-put-back sub-sampling method to realize first privacy amplification of privacy, providing a public non-label data set and finishing aggregation of soft labels; the obfuscator anonymously obfuscates the received information through a shuffling method, so that the federated server cannot associate any piece of information with a sender of the federated server, and second privacy amplification of differential privacy is realized under the condition that collusion with the federated server is avoided; and a client performs quantum model training on the local data set, predicts the public label-free data set, adds noise, encrypts and uploads the public label-free data set, so that knowledge distillation is realized, and third privacy amplification of differential privacy is realized by smoothing the same cluster sample. The invention also provides a quantum federal collaborative distillation system, electronic equipment and a storage medium.

The invention discloses a characteristic factor-based network dynamic planning method for edge Internet of Things equipment. The method comprises the following steps of: S1, extracting characteristic factors and matching weights; s2, constructing a dynamic planning feature engineering model; s3, training a dynamic planning feature engineering model; and S4, optimizing the dynamic programming feature engineering model. According to the invention, by extracting and analyzing the feature factors composed of the service scene features, the distribution strategy of the network resources is adjusted in real time according to different scene requirements, and the service requirements in the scene are closely combined with network resource management, so that the edge equipment can flexibly adjust the network strategy according to the scene change, and the network resource utilization rate is improved. The utilization efficiency of network resources is improved, the security of data transmission is enhanced, and the communication delay is reduced.

The invention discloses a rapid batch switching authentication method for predictability and aggregation promise proof, which realizes batch authentication of a plurality of user terminals by generating system parameters and secret keys through a trust mechanism and combining predictability promise and aggregation signature technologies. In the authentication process, the edge base station to be switched exchanges with the current communication edge base station through an encrypted message, verification information such as a pre-authentication certificate and a signature is generated through the user terminal, and identity authentication is completed step by step. The method comprises the following specific steps: a trust mechanism generates a private key, a public key, a pseudo identity and an expiration timestamp for each device; through multiple rounds of message exchange, the edge base station and the user terminal perform identity verification by using a shared key and a hash function; and finally carrying out efficient verification on a plurality of users through batch signature and aggregation commitment. According to the method, on the basis of ensuring the security, the speed of switching the authentication process is greatly improved, and the method is suitable for a large-scale network environment, especially in edge computing and mobile communication networks.

The invention provides an NTRU-based batch on-loop multiplication triple generation method, which comprises the following steps of: firstly, designing a homomorphic encryption scheme based on a decision type NTRU difficulty problem, then providing a batch on-loop multiplication triple generation protocol of two parties, randomly selecting secret share vectors on integer rings by participants P0 and P1 of the protocol, and selecting lt by P0; gt; 0, lt; bgt; 0, P1 is selected as lt; gt; 1, lt; bgt; the method comprises the following steps: 1, generating an instantiated homomorphic encryption scheme by P0, generating two ciphertexts through packaging coding and encryption, sending a public parameter, a public key and the ciphertexts to another participant P1, selecting an n-dimensional random number vector r as a mask on a ring with a larger modulus by the P1, and calculating lt in an encryption state; gt; 0. Lt; bgt; 1 + 1t; gt; 1. Lt; bgt; the method comprises the steps that firstly, a ciphertext ctd of a vector a and a vector b is obtained, the ctd is sent to a participant P0, and after the participant P1 and the participant P0 execute homomorphic calculation and decryption operation through the packaging decoding and SIMD technology, the participant P0 obtains the share lt of the product of the elements of the vector a and the vector b; cgt; 0, the participant P1 obtains the other share lt of the product; cgt; 1. The scheme of the invention has higher calculation and communication efficiency, and has anti-quantum security.

The invention discloses a Byzantine-robust federal learning method for realizing privacy protection based on a block chain, and the method comprises the steps: distributing secret keys to a calculation server and participants based on a secret key generation center, initializing a global model through the calculation server, and uploading the initialized global model and a guarantee submitted by the participants to a block chain system; each federal learning participant carries out current model training locally, carries out normalization, homomorphic encryption and signature on the gradient obtained by training, uploads the signature to the block chain system and sends the encrypted gradient to the computing server; verifying the gradient and the signature submitted by the participant through the computing server; carrying out gradient aggregation by adopting a weighted average method based on the computing server, then updating the computing server to initialize the global model, and uploading the updated global model to the block chain; and repeating the steps until the model converges. According to the invention, privacy protection is realized, the communication overhead of the computing server is reduced, and the overall security is improved.

The invention discloses a federal recommendation privacy protection method based on ring signature and selective aggregation, which realizes anonymization privacy protection and selective aggregation in a federal learning process through an innovative double-server architecture and a hierarchical security mechanism. The method comprises the following steps: 1) deploying an aggregation server and a security server in a system initialization stage, and completing parameter initialization and key distribution; 2) the client executes local model training, and anonymization privacy protection uploading is realized by adopting a ring signature technology; 3) the security server performs selective aggregation of credibility verification, and completes grouping and parameter fusion based on user similarity; and 4) ensuring the completeness and source credibility of model updating through a hierarchical security parameter distribution mechanism. While the recommendation precision is ensured, the user identity leakage and privacy tracking are effectively prevented, the comprehensive security guarantee is provided for the federal recommendation system, and the method has the advantages of low calculation overhead and high communication efficiency.

The present invention relates to the technical field of electronic digital data processing, and specifically discloses a hierarchical federated learning method of adaptive compression coding, the method comprising: hierarchical federated learning architecture construction, edge server matching, model generation performance judgment, compression rate matching and hierarchical federated learning architecture judgment, firstly, a hierarchical federated learning architecture is constructed so that each layer can make full use of its computing resources, an edge server corresponding to a terminal device is obtained by matching, each terminal device is used to generate an environmental data training model, a corresponding compression rate is obtained according to the network transmission performance of the edge server, the edge server receives the environmental data training model of each terminal device and performs adaptive compression coding and edge aggregation in sequence according to the corresponding compression rate, a cloud server receives the edge aggregation result and performs global aggregation, and finally judges whether the cloud server to which the hierarchical federated learning architecture belongs uploads the corresponding result of the global aggregation to an environmental resource scheduling cloud platform.

The invention relates to a Halton sequence optimization method and system based on RISC-V vector extension and application. The Halton sequence optimization method comprises the following steps: 1) identifying mathematical characteristics generated by a Halton sequence; a Halton sequence is generated; after continuous index values are partitioned and grouped, simultaneous processing of the multiple index values is achieved through a parallelized grouping calculation method in combination with vectorization operation; 2) carrying out vectorization optimization by using an RISC-V vector instruction; in a Halton sequence generation process, carrying out parallel processing on the grouped index values by using an RISC-V vector extension instruction; 3) packaging into a dynamic link library; and 4) integration and calling with a Hadoop framework. According to the Halton sequence generation method, the number of loop iterations is remarkably reduced, and the Halton sequence generation efficiency is greatly improved. According to the method, resource waste in traditional serial computing is avoided, meanwhile, branch jump and condition judgment expenses are reduced, and the utilization rate of hardware computing resources is increased.

The invention relates to the technical field of data security and privacy protection, in particular to a block chain and edge computing-based medical data sharing and access control platform, which comprises a multi-layer data storage system, a block chain network, an intelligent contract module, a plurality of data consumers, a plurality of data providers and a plurality of hospital servers. According to the invention, the calculation and communication overhead in data transmission of the medical internet of things is reduced, and the sharing efficiency and security requirements of the medical data between a provider and a consumer can be met.

The invention discloses a phased-array antenna calibration test method and system and a storage medium, and belongs to the technical field of phased-array antennas. When the phased-array antenna is calibrated, an upper computer, a wave controller and a vector network are converted into instantaneous pulses of a hardware circuit through information interaction of a specific protocol and a complex process. A Flash storage chip and an FPGA chip are arranged in the wave control box, before a phased-array antenna is prepared for calibration testing, an upper computer writes a calibration instruction set into the Flash of the wave control box, and a vector network is set to be in an external trigger mode. In the calibration process, the condition that the upper computer sends a calibration instruction to the wave controller and the wave control box analyzes the calibration instruction and then issues the calibration instruction is avoided, but the wave control box directly sends the calibration instruction and triggers the vector network, and the vector network collects and caches data. And after receiving a calibration completion command sent back by the wave control box, the upper computer reads the cached data at one time through the vector network FIFO function and then analyzes and displays the cached data. According to the method, the communication overhead between the upper computer and the wave control and vector network during calibration is saved, the calibration speed and accuracy are improved, and the labor cost is saved.

The invention relates to an asynchronous hierarchical federal learning method and system for resisting data heterogeneity, and belongs to the technical field of artificial intelligence. The method comprises the following steps: S1, constructing a hierarchical federal learning framework in the Internet of Things; s2, constructing an asynchronous hierarchical federated learning algorithm for resisting data isomerism, system isomerism and accidental lagging behind in the Internet of Things, and a communication and calculation model in an unreliable communication network; s3, constructing a virtual homogeneous learning method for resisting client drift in federated learning; and S4, considering data distribution, heterogeneous delay and accidental lagging-behind conditions among different clients, and constructing learning utility quantitative data distribution and a client selection method based on learning utility and delay. The method provided by the invention can effectively improve the performance of the hierarchical federal learning algorithm model, and has a wide application prospect.

The invention discloses a privacy protection federal learning method oriented to natural language processing, which comprises a three-layer network structure, and is characterized in that each client compresses the gradient of the client and uploads the compressed gradient to a corresponding edge server layer; each edge server aggregates the compression gradients uploaded by the clients in the communication range of the edge server, and sends the aggregated compression gradients to the cloud server layer; the cloud server layer aggregates the received compression gradients sent by the edge servers again, and then decompresses the gradients subjected to secondary aggregation; the gradient is observed and compressed on the client, so that the possibility of gradient leakage is reduced; gradient perturbation based on compressed sensing further makes an attacker difficult to reconstruct related words from an embedded layer, an original text sequence is effectively prevented from being reconstructed, the privacy of a client side is effectively protected, meanwhile, the communication overhead is reduced, and only slight influences are generated on the performance of the model.

The invention discloses a network traffic anomaly detection and classification method based on LSTM-EWC. The method comprises the following steps: S1, data preprocessing; s2, feature selection; s3, model building and training; and S4, evaluating the performance of the model. According to the invention, the EWC algorithm is utilized to solidify LSTM model parameters within a certain range, an efficient LSTM-EWC model capable of continuously learning network traffic features is developed, and efficient detection and classification of network data streams with dynamically changed features are realized.

The invention provides an image rendering method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: for a target parent display object corresponding to a target frame image, in response to a starting operation of a preset batch hybrid switch for determining the target parent display object corresponding to a plurality of target display objects, obtaining a first hybrid mode parameter of the target parent display object; determining a first mixed mode parameter of the target parent display object as a target mixed mode parameter of each target display object, and setting the first mixed mode parameter to be invalid for the target parent display object; and performing rendering display on the target frame image according to the target mixed mode parameter of each target display object, thereby realizing rendering on the main screen according to the target mixed mode parameter of the target display object without creating a rendering target for the target parent display object, realizing batch rendering, reducing the number of times of state switching, and improving the display efficiency. And rendering performance is improved.

The embodiments of this specification provide a method and device for fusion processing of truncation and ring-raising in secure multi-party computing. The method includes: the first party uses the first ring arithmetic slice of its own party to perform a first multi-party joint calculation with the second party to obtain the Boolean slice of its own party with an overflow indication bit; the overflow indication bit is used to indicate whether the sum of the two first ring arithmetic slices of the target private data overflows the first ring; the Boolean slice of its own party with the overflow indication bit is used to perform a second multi-party joint calculation with the second party to obtain the overflow bit arithmetic slice of the overflow indication bit secretly shared on the second ring; the number of bits of the second ring is determined according to the number of bits of the first ring, the number of bits of the target ring of the ring-raising, and the number of bits of the truncation; based on the first ring arithmetic slice of its own party and the overflow bit arithmetic slice, a local operation is performed to obtain the target ring arithmetic slice of the target private data that is secretly shared on the target ring after truncation. It can significantly reduce the number of communication rounds and communication volume.

The application provides an XGBoost training method and system based on multi-party privacy protection, and belongs to the technical field of machine learning; a key pair is generated for each user by encoding feature values in a one-hot encoding mode; data is encrypted by using a single instruction multiple data method and uploaded to the cloud; for a partition scene corresponding to a data type of the encrypted data, the encrypted data of each user is aggregated to construct an encrypted data set; based on the encrypted training set, a secure training algorithm is used to generate an XGBoost model in a ciphertext state for encrypted prediction services; wherein the secure training algorithm comprises secure gain calculation, secure division calculation, secure sigmoid calculation and secure tree building calculation. The private data of multiple participants can be encrypted respectively, privacy leakage caused by direct use of plaintext data is avoided, and the encrypted data can be used for offline training after being uploaded, so that practicability and deployment flexibility are considered.

The invention discloses an adaptive dynamic fragmentation block chain network construction method and system. A verifiable random function is used to quickly elect a directory committee and a consensus committee. And the directory committee generates a committee state table for recording the state of the consensus committee, and assists the consensus committee in consensus. The invention designs a consensus committee number adjustment algorithm to adjust the number of committees according to transaction processing requirements. According to the method, a weighting mechanism is designed for the consensus committee, so that the efficient consensus committee calling sequence is realized, and the consensus efficiency is improved. The invention also provides a new consensus method for reducing repeated consensus through threshold signature. The fragment number of the fragment block chain can be dynamically adjusted, the consensus efficiency is improved, and the expenditure of repeated consensus is reduced.

The invention relates to the technical field of machine learning, in particular to a federal learning acceleration method and system for industry user water consumption prediction. According to the federal learning acceleration method for industry user water consumption prediction, in the global training process, clustering is performed according to the local training speed of edge servers at intervals of multiple global training times, and multiple edge server clusters are formed; the edge servers in the edge server cluster are globally aggregated to update the local model. According to the method, the edge servers are clustered, and intra-cluster model aggregation is carried out by taking the edge server cluster as a unit, so that the combination of synchronous training and asynchronous training is realized. In the global training process, dynamic combination of edge server clusters is realized according to local training time interval clustering, so that the correlation of all edge servers in the global training process is ensured, and the model training performance is ensured; the clusters are mutually independent, and the aggregation in the clusters greatly saves the model training time compared with the aggregation of all edge servers.

The invention discloses an agile development method and system for a software-defined chip, and the method comprises the steps: obtaining a predefined design space which comprises a predefined hardware design space and a predefined software design space, the hardware design space comprises a design space of a calculation unit, and the software design space comprises a design space of a calculation unit; all the computing units use an ordered data stream scheme and asynchronous data stream transmission, and the computing units comprise computing units specially used for supporting cyclic variable control and generation; obtaining a target application program, a software design requirement and a hardware design requirement of a target field input by a user; determining actual design space parameters corresponding to the target application program from a predefined design space through demand analysis; and performing design space exploration in the actual design space parameters to obtain optimal actual design space parameters, and generating software codes and hardware codes of the target application program. According to the invention, low power consumption and high energy efficiency can be realized, and various complex calculation modes can be flexibly supported.

The invention discloses a continuous authentication method based on a trusted connection architecture in an Internet of Vehicles environment, which comprises the following steps of: firstly, designing an initial authentication stage, and completing bidirectional identity authentication of a vehicle and a road side unit in the stage under the assistance of a cloud service provider; then, after the vehicle drives into the coverage area of other road side units, the vehicle or the road side units can initiate continuous identity verification on the vehicle, the vehicle sends latest identity information to the road side units and the cloud service provider, and the cloud service provider completes continuous verification on the vehicle identity and completes switching of the vehicle among different road side units at the same time. According to the method, initial authentication of the road vehicle is realized, continuous authentication and switching in the subsequent driving process are also realized, key negotiation, forward / backward confidentiality and identity privacy protection can be realized, common attacks such as collusion attack, man-in-the-middle attack, replay attack, counterfeit attack and modification attack can be effectively resisted, and the safety of the vehicle is improved. And the safety of the Internet of Vehicles environment is enhanced.

The invention relates to a federated learning system and method applied to Internet of Vehicles, the system comprises a plurality of CAVs, RSUs and a cloud server, the plurality of CAVs are respectively in communication connection with the RSUs, the RSUs are in communication connection with the cloud server, and the CAVs are used for compressing and encrypting collected data and then transmitting the data to the RSUs; the RSU is used for training a local model, compressing and encrypting the trained model and then transmitting the model to the cloud server; an aggregation model issued by the cloud server is received, and an available global model is obtained through decryption and reconstruction operation and used for the next round of model training; and the cloud server is used for receiving the models uploaded by the RSUs and performing aggregation processing to obtain an aggregation model. Compared with the prior art, the method can solve the defects of insufficient vehicle computing power and insufficient privacy protection of the federated learning scheme in the existing Internet of Vehicles, and realizes an efficient and privacy-protected federated learning scheme.

This invention discloses a method and system for protecting privacy in wearable devices for human activity recognition, aiming to solve the problems of existing methods struggling to balance privacy and utility, and high resource consumption on the edge. The method first acquires multi-channel time-series signals from the wearable device and constructs fixed-length feature vectors. Based on a sensitive attribute inference model and the number of high-risk features, it divides the device into high-risk and low-risk feature sets. Only the high-risk feature sub-vectors are subjected to perturbation noise to generate perturbation latent representations, which are then uploaded to an edge server along with the low-risk feature set. The edge server generates the final feature vectors and assesses the risk of privacy leakage. An offline evaluation is used to construct a privacy-utility trade-off, dynamically adjusting the perturbation dimension and noise amplitude. This invention achieves targeted protection of high-risk features, maximizing the preservation of key recognition information, reducing edge computing power and communication overhead, adapting to the differentiated needs of different scenarios, and balancing privacy security and activity recognition performance.