1222results about "Communication with homomorphic encryption" patented technology

A system and method for adaptive real-time multi-modal compression with dynamic resource allocation provides intelligent compression optimization based on continuously monitored device conditions. The system monitors battery level, CPU utilization, and memory availability while classifying incoming multi-modal data streams comprising image, audio, text, and sensor data to determine processing priorities. Multi-objective optimization balances compression efficiency, reconstruction quality, and energy consumption using evolutionary algorithms that generate optimal parameters for an adaptive variational autoencoder. The autoencoder features dynamically selectable processing complexity, adjustable latent space dimensionality, and modality-specific processing layers. The system automatically switches between operational modes including emergency mode triggered by resource constraints, which applies maximum compression settings and intelligent data triage. Continuous learning adapts compression parameters based on observed performance outcomes, improving future optimization decisions. The system enables homomorphic operations on compressed data and provides enhanced compression performance under varying resource constraints across diverse edge computing applications.

A spatiotemporal modeling system for Personal Health Database (PHDB) platforms integrates diverse health data types into a comprehensive 4D model of an individual's health status. By combining genomic, imaging, clinical, and real-time health data, the system creates a dynamic, time-based representation of the user's anatomy and physiology. This model enables real-time analysis, pattern recognition, and predictive forecasting of health outcomes. The system preprocesses and aligns data from various sources, constructs a detailed spatial framework, and continuously updates the model with new inputs. Through interactive visualizations, it provides users and healthcare providers with intuitive, personalized insights for improved health management and decision-making.

The invention discloses a medical data privacy protection method and device, and relates to the field of medical data security. The method comprises the following steps: acquiring medical data feature information of a plurality of medical institutions to form a medical data feature map; constructing a hierarchical federal learning framework based on the medical data characteristic spectrum; performing parameter aggregation optimization through a convolution weighting method of a recursive context guide network; a dynamic differential privacy budget allocation strategy is combined with a multi-first-choice Lambda weighted list DPO technology to optimize a noise injection process, and adaptive differential privacy protection is realized; and in combination with a homomorphic encryption technology, performing multi-party calculation through a security aggregation protocol to obtain a medical data privacy protection analysis system. According to the method, efficient cooperative analysis is carried out while the privacy of the medical data is protected, the problems that in the prior art, simple parameter exchange may cause model inversion attack, special optimization for medical scenes is lacked, and a supervision and auditing mechanism is imperfect are solved, and the utilization efficiency and safety of the medical data are improved.

The invention discloses an intelligent factory data intelligent analysis and management system, and relates to the technical field of data analysis. Multi-source data are uniformly accessed through an industrial gateway, and a knowledge graph is constructed after cleaning and standardization; an edge node deploys a lightweight AI model to realize real-time analysis such as equipment anomaly detection; intelligent distribution of cloud side tasks is realized based on a decision model; optimizing production scheduling and quality control by using an algorithm; a zero-trust architecture is adopted to guarantee safety, and efficient energy management is realized in combination with reinforcement learning; all the modules work cooperatively, and the intelligent level of a factory is improved. The operation efficiency and quality of the intelligent factory are effectively improved. Efficient data fusion processing is realized, and equipment anomaly detection is more accurate; the order delivery period is shortened, and the product reject ratio is reduced; network security protection is enhanced, and energy waste is reduced; the decision response speed is accelerated, the decision accuracy is improved, cost reduction and efficiency improvement of enterprises are comprehensively assisted, and the competitiveness is enhanced.

The invention provides a cross-park enterprise data collaborative analysis method based on federated learning, and relates to the technical field of distributed machine learning and data security, and the method comprises the steps that a central server distributes an initial global model and configuration parameters to each park node; the nodes execute local data feature alignment to generate standardized feature vectors; calculating dynamic collaborative factors of local data and global distribution; adjusting a training strategy based on the collaborative factors and updating model parameters; collecting model updating through an encrypted channel, and screening effective updating by adopting a dynamic aggregation offset threshold value; performing weighted aggregation to generate a new global model; and terminating the process when the cross-park convergence condition is met or the maximum round is reached. According to the method, heterogeneous data differences are eliminated through a dynamic feature alignment mechanism, dual-channel collaborative evaluation and adaptive security protection are combined, multi-park collaborative modeling efficiency and robustness are remarkably improved on the premise of guaranteeing data sovereignty, and the problems of feature space splitting, weak attack protection and node contribution imbalance are solved.

The invention discloses an efficient processing method and system for online troubleshooting of the information department, and relates to the technical field of automatic operation and maintenance, and the system comprises a multi-modal data acquisition module, a dynamic knowledge graph construction module, a reinforcement learning driven self-healing module, an edge co-processing module and a cross-domain privacy calculation module. According to the method, the limitation of traditional single-dimensional monitoring is broken through, multi-source heterogeneous data such as server performance, container state and network flow are fused, a high-timeliness and high-consistency decision information base is constructed through unified time sequence alignment and semantic standardization, and a panoramic view is provided for dynamic analysis; based on an incremental learning mechanism and a causal inference engine, a service topology dependency relationship is updated in real time, a deep root cause is accurately positioned, and a cross-system and cross-level complex fault combination is solved.

The invention provides a multi-party collaborative anti-quantum signature method and system based on homomorphic Hash, and relates to the technical field of digital signatures, the system comprises a plurality of participants and a verification party, and the method is specifically as follows: each participant generates a pair of public and private keys, the public key is composed of a total public key, a total signature matrix and a total Hash matrix of all participants; based on the public and private keys, all participants perform multi-party signature on the target message through a homomorphic hash function, and a generated signature is composed of total hash challenge values of all participants, total response vectors of all participants and commitment value high-order difference values of all participants; according to the signature, the verifier recovers the high order of the total commitment value, further calculates the total hash challenge value, and verifies the validity of the signature through comparison of the total hash challenge value; according to the method, multi-user collaborative signature is realized, and the security of resisting malicious opponents under a quantum computer is obtained.

The invention discloses an urban multi-mode digital twin data asset security management method, and relates to the technical field of urban digital twin and data security, and the method comprises the steps: firstly building a unified access gateway, converging multi-department and multi-type data, and generating sensitivity annotation and privacy level metadata; performing differential privacy or desensitization and dynamically calibrating parameters at the local part of the data provider according to the labels; bookkeeping access, authorization and training on a permission alliance chain, and performing fine-grained permission control; through on-chain authorization, each node only uploads disturbed and encrypted model update, and a global model is obtained after secure aggregation; the governance layer continuously monitors logs, budget and model indexes and triggers strategy closed-loop regulation and control; and multi-agent reinforcement learning of a privacy agent and a performance agent is further introduced, a unified multi-target optimizer is formed through distillation, privacy, federation and permission strategies are refreshed online, and collaborative analysis and compliance sharing operation without data out of a domain are realized.

The invention provides a multi-institution medical data collaborative modeling and intelligent generation method based on privacy protection, which relates to the technical field of privacy protection, and comprises the following steps: solving the problem of feature heterogeneity through a cross-institution feature semantic mapping graph, constructing a hybrid coding data structure to carry out hierarchical privacy protection, and carrying out multi-institution medical data collaborative modeling and intelligent generation. A homomorphic encryption mechanism is utilized to safely fuse multi-party local feature representations to generate a global representation, and differential privacy budget is allocated based on feature sensitivity. According to the method, multi-mechanism data security collaborative modeling is realized, and the model performance is improved while the data privacy security is guaranteed.

The invention provides a data security method and system fusing an MIN and a unified AI theory, and the method comprises the steps: S1, completing the multi-dimensional identification registration and synchronization of a data space in a multi-identification network MIN based on a data space identity identification DSID and an ontology semantic mapping frame; s2, acquiring a data acquisition record from a multi-identifier network MIN, and constructing an access control atlas in combination with a semantic vector of a data subject identifier DSID-Item; s3, analyzing the access request and verifying the authority, identifying the sensitivity level of the access request, and triggering a privacy enhancement mechanism when the sensitivity level is marked as medium-high-level privacy data; and S4, judging whether the data object reaches a value end point in the life cycle of the data object or not by taking a data space identity (DSID) registered in the multi-identifier network (MIN) as a retrieval anchor point, and if so, executing a verifiable multi-stage destruction operation. According to the invention, a full-life-cycle trusted data security technical scheme can be realized, and intelligent, explainable and efficient security management of data streams in a network space is realized.

A private identity system performs on-device embedding and homomorphic tokenization (HT) to map plaintext inputs to non-invertible HT tokens. During enrollment, HT tokens are stored and a centroid can be computed for each user. During prediction, a new HT token shortlists nearest centroids, followed by 1:1 distance verification against stored embeddings. On success, the system returns a UUID bound to the user. Because passkeys can be shared via device or account keychains, the UUID binds the ceremony to the same enrolled individual without exposing biometrics.

Backup and recovery of multi-party computation (MPC) security data utilized to secure digital assets and transactions thereof can enhance user confidence and user experience in digital asset transactions. Example MPC security data can include cryptographic keys and key shares utilized with a N×M MPC signature and validation framework. A computing device participating in generation of MPC secure data can retain a segment of the MPC secure data and can encrypt and store an encrypted segment at a second device. A recovery service or recovery application at the second device can facilitate recovery of the MPC secure data segment at the computing device, or at an additional device not involved in generation of the MPC secure data. The recovery service or application can facilitate recovery of the MPC secure data segment even in the event the computing device is lost.

The invention relates to the technical field of data information retrieval, and discloses an efficient batch privacy information retrieval method and system based on OCD and GPU optimizing.The efficient batch privacy information retrieval method comprises the steps that firstly, a server is initialized, a database is subjected to bucket division through a PBC encoding algorithm based on the client query number, the database subjected to bucket division is obtained, and when a user conducts information retrieval, the user can retrieve privacy information in a batch mode; the method comprises the following steps of: finding a sub-bucket position of a retrieval request in a server at a client by using a PBC scheduling algorithm, carrying out homomorphic encryption on the retrieval request in each sub-bucket, compressing all the retrieval requests by using an OCD to obtain a retrieval ciphertext, sending the retrieval ciphertext to the server, and sending the retrieval ciphertext to the server according to the retrieval ciphertext provided by the client. The retrieval result in the ciphertext form is obtained and returned to the client side, the client side decrypts the retrieval result in the ciphertext form, and the required retrieval result in the plaintext form is obtained. According to the method, the GPU is used for parallel processing, computing resources in a real scene can be fully utilized, and the computing efficiency of privacy information retrieval is remarkably improved.

A system and method for optimizing data compression and encryption using reinforcement learning. The system analyzes incoming data streams to extract statistical features and data characteristics, which are processed by a reinforcement learning engine to automatically configure a multi-stage compression pipeline. Each compression stage transforms data into optimized distributions, applies Huffman coding, and maintains full encryption using homomorphic operations. A performance monitor tracks compression efficiency, processing speed, and output quality in real-time, providing feedback to continuously improve the reinforcement learning model's decisions. The system can dynamically adjust between one to five compression stages and select appropriate compression methods, including traditional algorithms or neural network-based approaches, based on data characteristics and performance requirements. All processing occurs on encrypted data without requiring decryption, ensuring complete data security throughout the pipeline. The adaptive nature of the system enables optimal compression performance across diverse data types while maintaining encryption integrity.

The invention discloses a security knowledge graph construction method and system based on behavior tracks, and relates to the technical field of network security. The method comprises the following steps: carrying out operation link aggregation according to user operation records to obtain a standardized behavior track chain; calling and accessing heterogeneous behavior trajectory data by taking the standardized behavior trajectory chain as a constraint to obtain multi-source behavior trajectory data; decomposing the standardized behavior track chain to obtain a link node time sequence operation relation and M link nodes; performing time sequence dependence modeling based on the graph convolutional network to generate a security knowledge graph; decomposing the multi-source behavior track data according to the M link nodes to obtain M pieces of multi-source behavior node data; and taking the M multi-source behavior node data as training data, and executing local parameter adaptive optimization of the security knowledge graph through a meta-learning framework. The technical problem of insufficient adaptability of the knowledge security graph in the prior art is solved, and the technical effect of dynamic adaptive optimization of the security knowledge graph is realized.

A collaborative transformation matrix learning system extends adaptive compression and encryption architectures through federated, privacy-preserving optimization. Each node analyzes local data distributions to generate anonymized distribution profiles using differential-privacy mechanisms, securely exchanging profiles and validated transformation matrices across a collaborative network. A trust and validation engine verifies mathematical properties and evaluates claimed performance metrics. Validated matrices are integrated into local optimization when trust and performance thresholds are satisfied. The system employs secure multi-party computation, homomorphic encryption, and conflict-resolution logic to ensure integrity of shared insights while preventing exposure of sensitive information. By combining collective learning with local adaptation, the invention accelerates convergence to optimal matrix configurations, mitigates cold-start inefficiencies, and improves compression-encryption efficiency and cryptographic strength across distributed deployments.

The invention discloses a real-time cleaning and aligning method for multi-source heterogeneous data, and particularly relates to the technical field of source heterogeneous data, a self-adaptive interface adapter is compatible with multiple types of data, and a semantic index atlas is generated through four-dimensional classification labeling; constructing a cloud-edge collaborative streaming processing framework, preprocessing edge nodes, and performing accurate cloud alignment; a dynamic rule cleaning and semantic-dimension-entity three-layer progressive alignment mechanism is adopted, and a four-dimensional quality evaluation system is combined for real-time monitoring; and through closed-loop optimization, homomorphic encryption, a block chain and other security mechanisms, data security and traceability are ensured. According to the real-time cleaning and aligning method for the multi-source heterogeneous data, the real-time performance and accuracy of data processing are effectively improved, the method is adaptive to multiple service scenes, and high-quality data support is provided for data value mining.

The invention discloses a health monitoring system of an old-age care robot, which relates to the technical field of health monitoring, and comprises the steps of constructing an indoor multi-modal data pool, generating an encrypted feature vector, establishing a steady-state physiological behavior baseline according to a historical encrypted feature vector, and outputting a health risk level and an intervention instruction set. According to the invention, all-time and all-scene health monitoring is realized by integrating a multi-mode sensor, old people do not need to wear equipment, feature compression and homomorphic encryption technologies are adopted, data security is ensured, a threshold value can be updated in real time, and the method is suitable for being applied to the health monitoring of the old people. The health state of the old people is accurately monitored, health risk prediction and personalized intervention are carried out in combination with analysis of behaviors such as falling and static abnormity, the prompt mode is dynamically adjusted according to the ability of the old people, the data processing efficiency is optimized through the edge cloud collaborative architecture, the data privacy and stability are guaranteed, and the life quality and safety are improved.

The invention provides a multi-tenant multi-user login method based on enhanced OAuth2, which relates to the technical field of network security, and comprises the following steps: obtaining a key threshold through a tenant identifier, and executing OAuth2 authentication to obtain an access token; tenant fingerprint information is generated through zero-knowledge proof and homomorphic encryption, and a tenant verification token is generated through secure multi-party calculation; constructing a reinforcement learning access controller in combination with the historical access data, and generating and verifying an authority list; and finally forming a session identifier and storing the session identifier in a distributed cache. According to the invention, the authentication security is improved, cross-tenant fine-grained authority management is realized, and unauthorized access is effectively prevented.

The present invention relates to a privacy-preserving EV charging authorization and billing system, and a method for the same. The proposed system is configured to integrate permissioned blockchain with fully homomorphic encryption. The present invention aims to eliminate plaintext exposure mitigates single point of failure, by performing all authorization and billing computations on encrypted data and recoding transactions immutably, wherein an EV user securely generates encrypted authorization and billing requests using FHE-based public keys. The charging station routes these encrypted requests to the blockchain network, which records immutable encrypted transactions and verifies them via consensus. The FHE computation layer performs secure operations on the encrypted data for authorization and billing, while smart contracts execute automated verification and billing computations, ensuring transparency and auditability.

Systems, methods, and apparatuses for securing a communication network are provided. Transmitting a first signal including encoding a first timestamp, a first identifier, and a message authentication component. Obtaining a message signal comprising one or more service variables associated and an encrypted unique identifier. Authenticating the encrypted unique identifier based on a comparison of the encrypted unique identifier against a plurality of stored unique identifiers in a lookup table. Generating a second signal comprising an encoding of a subset of the service variables, the encrypted unique identifier, and a second timestamp. Transmitting the second signal to each remote receiver in a subset of the plurality of remote receivers.

The invention relates to the technical field of security calculation and privacy protection, in particular to a large model privacy reasoning method and device based on trusted hardware and electronic device.The method comprises the steps that before a large model reasoning task is executed, a client and a server initialize respective pseudo-random number generators based on information interaction so as to synchronize initial seeds, and the pseudo-random number generators are initialized; generating a random number based on the seed; in the reasoning execution process, homomorphic encryption is used for completing word embedding of large model reasoning, a reasoning instruction set is used for converting all operators into instruction combinations, privacy reasoning is carried out based on a privacy reasoning protocol and a random number generated by a pseudo-random number, and the random number is used for masking privacy data; and after the task is completed, obtaining an execution result and proof information provided by the server, performing verification, and refusing to receive the result if verification fails. Therefore, the problems of data leakage risk, difficulty in considering safety and efficiency and the like existing in related technologies are solved.

According to the energy storage system battery full life cycle state perception and value circulation method and system, digital twin bodies of all battery modules are generated according to battery static information, and life cycle stage identifiers are set in the digital twin bodies; training is carried out based on a federated task scheduling contract, an edge calculation unit of each energy storage power station enters a corresponding federated collaborative clustering group through dynamic matching according to the life cycle stage identifier, and a corresponding stage global sensing model and model parameters thereof are obtained; updating the corresponding stage global perception model parameters by using the global model parameter update quantity obtained by decryption; the under-chain service network writes a state evaluation result and an evaluation timestamp into a digital twinborn body of each battery module according to the updated parameters of the stage global perception model and the real-time operation data of each battery module; and monitoring the SOH of each battery module in real time to update the life cycle stage identifier, performing training based on a federated task scheduling contract, and constructing a full life cycle data file.

A data processing method and device, a computer device, and a readable storage medium are disclosed. The method may include: acquiring a service algorithm of an application service, and performing format conversion on the service algorithm to obtain an algebraic intermediate representation; processing the algebraic intermediate representation according to a preset Scalable Transparent ARgument of Knowledge (STARK) conversion rule to obtain a prover and a verifier; sending the prover to a computing power processing device; receiving an operation result sent by the computing power processing device; and performing a verification on the proof information using the verifier, and sending the ciphertext result to the data source device in response to the verification of the proof information being successful, such that the data source device decrypts the ciphertext result using the homomorphic encryption algorithm and returns a plaintext result.

The invention relates to the technical field of computers, discloses an astronomical sensitive data security processing method, device and system based on privacy calculation, and a storage medium, and aims to solve the problems that in astronomical data cross-mechanism joint analysis, original data is easy to leak, the cooperation efficiency is low, and a traditional desensitization or encryption method is difficult to consider security and availability at the same time. The method specifically comprises the following steps: each participant deploys a private computing agent node locally, and original data is not out of a domain; the task coordination center issues an analysis task; the proxy node extracts and preprocesses local data, and loads a corresponding secure multi-party computing protocol template; according to the method, share segmentation is carried out on multi-modal data such as images, spectrums and star catalogues by adopting addition homomorphic secret sharing, and distribution is carried out through a national secret SM4 encryption channel; and multiple parties cooperatively execute task-oriented security calculation in an encrypted state, and the result is aggregated and returned with the minimum information amount. The method has the effect of realizing high-precision safe collaborative analysis which is available and invisible.

The invention provides a digital medical record management method and system. Belongs to the technical field of digital management. The method comprises the following steps: acquiring node information of a medical health institution and medical record management demand data, and constructing a medical alliance chain infrastructure based on the node information; performing hierarchical encryption mechanism deployment on the medical alliance chain infrastructure, and forming a distributed medical record storage block chain network through node identity authentication and consensus mechanism configuration; through the distributed block chain network and the Hash index mechanism, the non-tampering evidence storage of the medical record data is realized, and the data is effectively prevented from being maliciously modified or deleted. Differentiated encryption processing is carried out on the medical record field by adopting a hierarchical encryption mechanism, and a higher encryption level can be set for sensitive information, so that the privacy of a patient is guaranteed. A double-chain evidence storage mechanism (a main chain and an evidence storage side chain) stores data and operation logs in a separated manner but performs linkage verification, so that the overall anti-attack capability of the system and data consistency verification are enhanced.

The invention discloses an automatic driving scene library dynamic generation method based on multi-modal data fusion, belongs to the field of automatic driving testing, and solves the problems of insufficient coverage of extreme working conditions, data splitting and single evaluation of a static scene library. According to the technical scheme, the method comprises the following steps: establishing a distributed data acquisition layer, and accessing a vehicle-mounted terminal and a V2X roadside equipment data stream through a federated learning framework; constructing a multi-modal semantic coding layer, fusing data extraction features, converting laser radar point cloud data into a space-time constraint matrix, and outputting a scene DNA coding sequence; deploying a dynamic evolution engine, mapping the DNA coding sequence into a traffic flow state parameter, and calculating an entropy gradient field; and starting a virtual-real co-occurrence scene generator, receiving the coordinates of the high-entropy region and the physical interference intensity parameters, performing simulation to generate a controlled physical interference test scene, injecting a group interaction rule based on behavior uncertainty, and finally outputting a dynamically updated scene library and a risk report for automatic driving system test evaluation.

Provided are a computer program product, system, and method for processing a subset of a feature set to determine where to process a query. One of the following is performed: 1) in response to the determining to process the query at a first machine learning model, forwarding non-sensitive input data of the query to the first machine learning model to produce a first query result to return to an initiator of the query; and 2) in response to determining to process the query at a second machine learning model, forwarding sensitive input data and the non-sensitive input data to an encryption engine to encrypt to send to the second machine learning model; and receiving an encrypted second query result from the second machine learning model to decrypt to produce a second query result to return to the initiator of the query.

The invention discloses a medical big data management system and method based on cloud edge collaboration, and relates to the technical field of medical data management. Efficient data management is achieved through cooperation of multiple modules, and the heterogeneous data fusion module collects multi-source data in a unified mode and constructs a knowledge graph; the privacy protection calculation module guarantees data security by using homomorphic encryption and differential privacy; the edge intelligent analysis module deploys a lightweight model to realize real-time analysis; the cloud edge collaborative decision-making module dynamically allocates tasks according to the data and the equipment state; in addition, data quality evaluation, knowledge graph updating and other mechanisms are included, and the data reliability and timeliness are improved. Efficient and safe management of medical data is realized, the data processing efficiency is greatly improved, and the cloud cost and energy consumption are reduced; the data privacy is guaranteed, and the leakage risk is almost zero; the model diagnosis accuracy is improved, and clinical decision making is assisted; the diagnosis time is shortened, the error rate is reduced, and the medical service quality is remarkably optimized.

The invention belongs to the technical field of network security, and discloses a federated learning-based distributed network security threat prediction system, which is characterized in that adaptive iteration is driven by threat entropy through a dynamic federated learning scheduling module, emergency update is triggered when a threat feature entropy value exceeds a threshold value, and a model iteration period is greatly compressed; a hierarchical sub-model architecture is combined, so that different domain nodes can pertinently update feature layer parameters, the recognition speed of novel variation threats is remarkably improved, the problem that traditional model recognition is lagged is solved, and it is guaranteed that a system follows up threat changes in time; a privacy enhanced aggregation module is adopted, and parameter security aggregation is realized on the premise of not leaking original data through differential privacy and homomorphic encryption double-layer protection; and meanwhile, heterogeneous data is mapped to a unified feature space by virtue of a federal transfer learning engine, so that the cross-scene model adaptability is improved, the problem of difficulty in data fusion under a centralized architecture is solved, the collaborative advantage of multi-source data is fully played, and the global model precision is improved.