1337 results about "Homomorphic encryption" patented technology

An AI-enhanced distributed system for neural network-based data compression leverages reinforcement learning optimization and privacy-preserving computation across edge and central computing devices to autonomously optimize efficiency and quality. The system includes a lightweight compression subsystem at edge devices that applies privacy-preserving preprocessing and partially compresses input data before securely transmitting it to central computing devices. A reinforcement learning agent continuously monitors system performance and automatically optimizes compression parameters, model selection, and task allocation based on multi-objective rewards. The central compression subsystem processes data using AI-optimized parameters and temporal modeling components. The system incorporates hardware detection capabilities that automatically select optimal compression models based on available processing resources and implements homomorphic encryption for computation on encrypted data while coordinating federated learning across distributed devices. This AI-enhanced distributed approach improves bandwidth efficiency, energy consumption, and adaptability while ensuring data privacy and security.

The invention belongs to the field of intelligent edge systems and privacy enhancement computing, and particularly relates to a privacy protection-oriented robot large model cloud edge end collaborative reasoning and federated learning system, which comprises a cloud server layer used for deploying a large-scale pre-training model and executing complex reasoning and global federated learning coordination; the edge calculation layer is used for deploying an intermediate layer model and executing local data aggregation, privacy protection processing and intermediate feature calculation; the terminal equipment layer is used for deploying a lightweight model and executing data acquisition, primary processing and lightweight reasoning; the federated learning framework is used for optimizing the model; the privacy protection module is used for integrating data localization, differential privacy, homomorphic encryption, secure multi-party computing and a block chain verification mechanism; the adaptive allocation module is used for dynamically adjusting computing resources. According to the method, the problems of privacy leakage risk, computing resource limitation, network delay, insufficient data isolation and the like of the traditional AI service in a robot scene are solved, and efficient privacy protection and data security isolation are realized.

The invention discloses an industrial equipment fault prediction system based on federated learning and a privacy protection method, and relates to the field of industrial equipment fault prediction. The data acquisition preprocessing module extracts fault features through compressed sensing downsampling, screens and uploads the fault features; the federal learning training module adopts a layered architecture and a dynamic algorithm to schedule a learning rate; the fault prediction and diagnosis module constructs a space-time diagram neural network and fuses a physical model to improve generalization; the privacy protection security communication module performs homomorphic encryption storage and zero-knowledge proof verification update; the knowledge graph construction reasoning module constructs a dynamic graph, locates a fault root cause through causal reasoning, and supports cross-device knowledge migration. By adopting the quantum and federated learning technology, the industrial equipment fault diagnosis accuracy is high, the attack resistance is high, the encryption efficiency is greatly improved, the model training time is shortened, cross-equipment knowledge migration is realized, the operation and maintenance cost is reduced, and the intelligent operation and maintenance development of the industrial equipment is promoted.

The invention discloses an intelligent fusion analysis system for multi-source heterogeneous data, and the system comprises a dynamic data collection module which is used for carrying out the data collection, and carrying out the processing of a collected mixed data flow; the semantic alignment module is used for constructing a domain ontology knowledge graph according to a preset scene target and carrying out semantic alignment and coordinate alignment on the collected data; the self-adaptive fusion engine module is used for fusing the collected multi-source heterogeneous data; the trusted computing module integrates a secure multi-party computing protocol and a homomorphic encryption algorithm to realize that data is available and invisible; and the intelligent decision-making module constructs a state action reward model based on reinforcement learning according to a preset scene target, and performs analysis and decision-making by using historical data and data acquired in real time. According to the invention, the capability and effect of data processing and decision support are improved.

The invention relates to a federated learning system based on multi-key homomorphic encryption and adaptive differential privacy, and belongs to the technical field of privacy computing. According to the system, on the premise that no trusted third party exists, a multi-client collaborative key generation and threshold decryption mechanism is achieved, it is ensured that model parameters are always in an encrypted state in the aggregation process, and leakage of a single node is prevented. By introducing a parameter sensitivity analysis and selective encryption strategy, the system only encrypts high-risk parameters, and the encryption burden is effectively reduced. Meanwhile, in combination with an adaptive privacy budget allocation mechanism, the system dynamically adjusts noise intensity according to a model training state, and model performance and convergence speed are maintained while privacy protection capability is improved. According to the method, high robustness and collusion resistance are realized, stable operation under the condition that part of clients are offline is supported, and the method is suitable for application scenes such as medical treatment and finance with high data sensitivity and strict performance requirements.

The present invention discloses a system and method for secure artificial intelligence-based financial technology governance and risk management, designed to provide real-time, autonomous, and verifiable compliance assurance within digital financial ecosystems. The invention integrates a secure artificial intelligence processing unit, a governance control processor, a cryptographically anchored storage unit, a federated learning coordination processor, and a quantum-resistant communication interface enclosed within a tamper-proof hardware structure. The system performs encrypted machine learning computations on financial transaction data using homomorphic encryption and trusted execution environments to preserve confidentiality during analysis. It computes a governance risk index based on probabilistic inference and anomaly detection to identify regulatory deviations, applies adaptive compliance reasoning across multi-jurisdictional frameworks, and automatically enforces governance actions through secure decision logic.

The invention discloses a big data privacy protection modeling method and system based on federated learning and a block chain, and relates to the technical field of privacy protection and joint modeling. According to the method, homomorphic encryption, differential privacy, federated learning, secure multi-party computing and block chain technologies are fused, big data privacy protection and joint modeling are realized, encryption and dimensionality reduction are performed on original data through homomorphic encryption and differential privacy, an encrypted training sample of secure privacy is generated, a local model is trained on an encrypted data set through federated learning, and a big data privacy protection result is obtained. The method comprises the following steps: calculating aggregation parameters by using security multiple parties, constructing a verification network in combination with a block chain, ensuring credibility and integrity of model training, and finally, adding noise optimization performance for a global model by using differential privacy, testing generalization ability through cross validation, and determining a deployable privacy protection joint learning model, thereby breaking traditional data islands, promoting cross-mechanism data cooperation, and improving the privacy protection performance. Big data values are released, and data protection regulations and privacy requirements are met.

Systems, apparatus, and methods of managing access to private data via tokens are disclosed. The disclosed techniques provide for constructing digital tokens that represent a right-to-access private data where the tokens can be managed as individual objects. Further, such digital access tokens (DATs) can be recorded on a notarized ledger, a blockchain for example. Additionally, the rights-to-access can be embodied as a non-fungible token (NFT), which could further include support for establishing homomorphic encryption workspaces in which private data can be accessed or manipulated without exposing the private data.

The invention relates to the technical field of cloud computing financial security, and provides a financial data encryption transmission and storage method. The method is characterized by comprising the following steps of: executing sensitivity-driven data grading fragmentation on a user terminal; dynamic elliptic curve encryption is carried out on transmission data through a two-channel encryption engine, and fully homomorphic encryption is carried out on storage data; dynamically distributing the fragments to heterogeneous cloud nodes by the multi-cloud routing based on reinforcement learning; a distributed key management matrix is constructed, key fragments are dispersed and stored in a block chain and a hardware security module, and reconstruction is activated through biological characteristics. The method has the advantages that full-link ciphertext operation is realized, and the plaintext exposure risk is eliminated; ciphertext state financial calculation is supported; single point failure is resisted; the APT attack is defended dynamically; and the quantum security evolution capability is realized. The method is suitable for mobile banks, cross-border payment and other scenes.

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.

Systems, apparatus, and methods of managing access to private data via tokens are disclosed. The disclosed techniques provide for constructing digital tokens that represent a right-to-access private data where the tokens can be managed as individual objects. Further, such digital access tokens (DATs) can be recorded on a notarized ledger, a blockchain for example. Additionally, the rights-to-access can be embodied as a non-fungible token (NFT), which could further include support for establishing homomorphic encryption workspaces in which private data can be accessed or manipulated without exposing the private data.

The invention discloses an OBD tamper-proof two-way verification method, which comprises the steps of obtaining OBD equipment data and vehicle sensor data, registering to a cloud end based on the OBD equipment data, obtaining registration data, obtaining key data and non-key data according to the vehicle sensor data, symmetrically encrypting the key data by using a session key in the registration data, and verifying the key data and the non-key data. Performing approximate homomorphic encryption on the non-key data, obtaining a double-entropy source dynamic salt value according to the time entropy of the ciphertext and the OBD equipment operation entropy, reversely reconstructing the dynamic salt value by the cloud according to the timestamp and the registration data, verifying the ciphertext structure by using the dynamic salt value and generating a random challenge containing a salt value check code, and sending the random challenge to the cloud; the equipment verification result and the challenge value are signed and then transmitted back, the OBD equipment carries out reverse verification on the salt value according to the signature and the equipment verification result, and the cloud generates an authority token based on the reverse verification result. According to the method, the data authenticity and integrity of the diagnostic equipment are guaranteed through packet encryption, dynamic salt value generation and bidirectional identity verification of transmission data.

The invention discloses a financial data processing method and system based on machine learning, and a storage medium. The method comprises the following steps: encrypting customer credit data of a financial institution through Paillier homomorphic encryption and extracting a risk feature vector; performing secret sharing segmentation on the local gradient parameters and then safely aggregating the local gradient parameters into global gradient parameters through a BGW protocol; on the basis of the privacy level, global gradient parameter differential privacy noise is injected and subjected to federal training to obtain a cross-institution credit evaluation result; calculating risk characteristics and evaluation results through homomorphic encryption to obtain an encrypted credit score, and performing secure multi-party calculation and decryption to generate a customer credit report; and block chain supervision compliance verification is carried out to generate an audit record, and a privacy budget optimization scheme is dynamically adjusted according to the business emergency degree. The technical problems that in an existing financial data processing method, information is incomplete due to data islands, data leakage risks are caused by insufficient privacy protection mechanisms, and an effective supervision compliance verification mechanism is lacked are solved.

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 discloses a medical insurance cost prediction and optimization method based on big data analysis, and belongs to the technical field of medical data processing. The method comprises the following steps: constructing a multi-source data acquisition module, and integrating data of an HIS system, a medical insurance platform and wearable equipment by using an FHIR interface and a block chain; a BERT-BiLSTM-CRF model is adopted to fuse multi-modal data, a dynamic model group containing Transform anomaly detection and LSTM-ARIMA time sequence prediction is established, and the prediction error rate is reduced to 12% (reduced by 28% compared with that of a traditional method); a multi-objective optimization system is designed, medical quality and cost control are balanced based on an improved NSGA-II algorithm, the cost of a single disease is reduced by 18%-25%, and the quality standard reaching rate exceeds 95%; a hybrid cloud and homomorphic encryption module is deployed, and the data leakage risk is reduced by 90%; a policy sandbox system is constructed, medical insurance policy simulation deduction is supported, and the response time is shortened to 72 hours. According to the invention, the problems of data islands, low prediction precision and privacy risks are solved, and the whole-process intelligent management and control of medical insurance fees is realized.

The invention relates to the technical field of information security management, and discloses an information security management method and system based on digital medical treatment. The method comprises the following steps: verifying doctor professional qualification, patient authorization range, data sensitivity level and time permission validity in a multi-institution medical data access request, and generating a permission verification result; analyzing ontology semantic attributes and sensitivity characteristics of medical search keywords input by a user, and determining a corresponding hierarchical encryption strategy based on the permission verification result; performing homomorphic encryption transformation on the medical term synonym set according to the hierarchical encryption strategy, and constructing a medical ciphertext database; and receiving a medical query request, executing symptom disease association matching and ciphertext state logical reasoning operation in the medical ciphertext database, and outputting an encrypted state search result. According to the method, the privacy leakage risk caused by search behavior trajectory analysis is effectively prevented, and the security and availability of medical data are improved.

The invention discloses an Internet of Things data storage method and device based on a block chain and a medium, and relates to the technical field of data storage, and the method comprises the steps: collecting an Internet of Things terminal data set, carrying out the noise disturbance and differential aggregation, and generating privacy protection data; performing hash coding on the privacy protection data, and outputting a tamper-proof to-be-stored data packet; performing security level encryption on the tamper-proof to-be-stored data packet by using a homomorphic encryption algorithm to form an anti-attack ciphertext vector; and carrying out data fragmentation and storage node mapping on the attack-resisting ciphertext vector to obtain a fragmentation storage index. According to the invention, through a dual privacy protection mechanism of homomorphic encryption and differential privacy, the privacy protection strength in the data storage process is improved. And meanwhile, safe and efficient multi-chain collaborative storage in a high-concurrency scene is realized through the constructed hierarchical cross-chain collaborative model.

An electronic apparatus includes: a memory for storing an instruction; and a processor configured to execute the instruction to thus transform a first homomorphic ciphertext homomorphically encrypted using a first scheme into a second homomorphic ciphertext encrypted using a second scheme, wherein each of the first homomorphic ciphertext and the second homomorphic ciphertext includes an a-part and a b-part, the first scheme is a homomorphic ciphertext format in which a plurality of homomorphic ciphertexts have different a-parts and b-parts, the second scheme is a homomorphic ciphertext format in which a plurality of homomorphic ciphertexts have the same a-part and only different b-parts, and the processor is configured to transform the first homomorphic ciphertext into the second homomorphic ciphertext by iterating a partial transformation operation of gradually increasing a rank of a secret key multiple times.

The invention discloses an emergency patient information data matching method and system based on the Internet, and the method comprises the steps: obtaining multi-source heterogeneous data in real time according to an electronic medical record, image data and real-time vital sign data of an emergency patient, and generating a time-synchronized multi-source data set through a self-adaptive data format conversion algorithm; for the multi-source data set, using a feature extraction network to extract illness state features of patients, and generating patient priority scores; adjusting a resource allocation scheme in real time by adopting a medical resource matching algorithm based on a graph neural network according to the patient priority score and the illness state feature, and generating a preliminary medical resource matching result; and for a medical resource matching result, generating a final emergency patient information data matching scheme through a differential privacy protection mechanism by adopting a data security transmission protocol based on homomorphic encryption. According to the embodiment of the invention, the method can meet the requirements of quick, safe and effective processing of data in an emergency scene, and improves the quality and efficiency of emergency medical service.

The invention discloses a cross-department collaborative approval digital processing method based on a block chain and a smart contract, and relates to the technical field of block chains, and the method comprises the steps: submitting an approval material through a front-end application, converting the approval material into standardized JSON format data, generating a data fingerprint, writing the data fingerprint into a block chain, and generating an NFT in a to-be-pre-audited state; the intelligent contract retrieves a list of departments participating in examination and approval according to examination and approval type fields in the standardized JSON format data, verifies the validity of the examination and approval right NFT of each department, and generates an encrypted pre-examination task package; and encrypting the encrypted pre-auditing task package by using a fully homomorphic encryption algorithm. The risk analysis request packet is generated after the threshold signature is collected through the smart contract, real-time risk analysis is performed through the neural network model deployed in the smart contract, the risk disposal report or the plaintext approval document is output, the result is synchronized to the related service chain through the cross-chain protocol, and the verifiable certificate is generated.

The invention discloses a digital archive supervision system based on a block chain, and relates to the field of digital archive supervision. The system comprises an archive collection module, a block chain pre-processing module, a dynamic consensus module, an intelligent contract module and a supervision module. The archive collection module preprocesses heterogeneous data through a CRNN algorithm to generate a standard data set; the pre-processing module adopts the combination of SM4 and homomorphic encryption to protect data; the dynamic consensus module dynamically adjusts the right based on the node credit score, the main chain uses a PBFT method enhanced by a threshold signature to ensure the safety of core operation, and the side chain uses DPoS-RAFT to process high-frequency business; the intelligent contract module constructs a Merkle tree through block hash, and performs automatic hash verification and triggers exception handling during access; the supervision module realizes layered examination and approval of the unfreezing application by using a decision tree algorithm; according to the system, through cooperation of multiple modules, full-process block chaining of archives from collection, encryption, consensus to supervision is achieved, and the system is suitable for credible storage and supervision scenes of digital archives.

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.

Provided is an electronic apparatus including: a memory for storing an instruction; and a processor configured to execute the instruction, wherein the processor is configured to generate a first matrix ciphertext by disposing a plurality of homomorphic ciphertexts in a matrix form if a matrix operation command for the plurality of homomorphic ciphertexts is input, split the first matrix ciphertext into a second matrix ciphertext and a third matrix ciphertext that satisfy predetermined conditions, and acquire a matrix operation result by performing a matrix operation between each of the second matrix ciphertext and the third matrix ciphertext and a plaintext matrix corresponding to the matrix operation command.

The invention discloses a digital bond risk assessment method and system based on a block chain and privacy calculation, electronic equipment and a storage medium, and the method achieves the innovation of digital bond risk assessment, and has the core effect of enhancing the data security and privacy protection in the assessment process. And meanwhile, the integrity and non-tampering property of the data are ensured. According to the method, the homomorphic encryption technology is utilized, the encryption state of the data in the processing process can be ensured even if calculation is carried out at the cloud, and data leakage and unauthorized access are effectively prevented. Besides, the evaluation system provided by the invention greatly improves the transparency and credibility of evaluation, so that investors can more accurately grasp the risk condition of the digital bond, thereby promoting the healthy development of the digital financial market and the efficient configuration of capital. Through the intelligent evaluation process, the evaluation efficiency is remarkably improved, the decision-making period is shortened, and the capacity of quickly responding to market changes is provided for issuers and investors.

Disclosed are various embodiments for homomorphic encryption for embeddings. A prompt is tokenized to generate a plurality of prompt tokens. A respective prompt embedding is generated for each of the plurality of prompt tokens, the respective prompt embedding for each of the plurality of prompt tokens representing an encoding of each of the plurality of prompt tokens in a high-dimensional vector space. Then, the respective prompt embedding for each of the plurality of prompt tokens is encrypted by rotating the respective prompt embedding through the high-dimensional vector space to generate a respective encrypted prompt embedding for each of the plurality of prompt tokens.

The invention discloses a federal learning-based cross-device brain grain model training system. The system comprises a terminal device layer, an edge coordination layer and a central server layer, the terminal equipment layer is responsible for collecting original electroencephalogram data and executing local lightweight training; the edge coordination layer is used for aggregating model updates of a plurality of terminal devices in the jurisdiction to generate a regionalized sub-model; according to the central server layer, a central server performs secondary optimization on each regional model to generate a global unified model; the encrypted global model and strategy configuration are pushed to all edge coordination layers; and the edge coordination layer pushes the optimized model and strategy to terminal equipment. Through a three-layer federated architecture and multi-dimensional optimization, the system efficiency is remarkably improved: the terminal equipment only uploads an encrypted model increment, and source data protection is realized in combination with differential privacy and homomorphic encryption; federal transfer learning shortens the cold start time of new equipment.

A latent transformer architecture with latent attention mechanisms and expert processing systems for federated deep learning is disclosed. The system operates entirely within latent space, eliminating traditional embedding and positional encoding layers while maintaining full attention capabilities. Input data is compressed into latent vectors via variational autoencoder encoding, then processed by a latent attention module that computes query, key, and value matrices directly from latent representations. The architecture incorporates expert processing systems including gated latent expert networks for sparse computation and latent mixture of experts for collaborative processing. In the gated approach, a routing network selectively activates specialized expert modules based on latent vector characteristics. The mixture approach enables all experts to contribute through weighted combination, facilitating distributed computation and enhanced model expressiveness.

The invention discloses a venous thrombosis risk assessment method based on a large language model, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: collecting thoracic surgery diagnosis and treatment data of a patient, carrying out the space-time alignment, generating a standard diagnosis and treatment data flow, and carrying out the homomorphic encryption of the standard diagnosis and treatment data flow, and forming an encrypted patient data package; inputting the encrypted patient data packet into a multi-task large language model, performing feature extraction and semantic coding by a feature coding layer, performing time sequence modeling and risk probability calculation by a risk quantification layer, and outputting a venous thromboembolism risk level of a patient; and performing feature decoupling and potential space mapping on the encrypted patient data packet to obtain thrombus semantic potential features. Through the multi-task large language model, the dual machine learning algorithm and the homomorphic encryption, the accuracy of venous thrombosis risk early warning is improved, and the safety of the risk assessment process is enhanced.

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.