976 results about "Information leakage" patented technology

The invention discloses a data safety protection method for a photovoltaic power station in an intelligent power grid environment, and relates to the technical field of intelligent water supply equipment inspection. The method comprises the following steps of: 1, identifying and evaluating a data security risk of a photovoltaic power station by utilizing an attack tree model and a threat model, and evaluating a risk level; and meanwhile, on the basis of a block chain technology, a unique digital identity label is generated for the photovoltaic power station equipment and a service scene thereof, and access control is implemented. According to the data security protection method for the photovoltaic power station in the intelligent power grid environment, a large amount of data can be efficiently processed, the security and integrity of the data in the transmission, storage and processing processes are ensured, information leakage or hostile attacks can be avoided, the data security of the photovoltaic power station is improved, and safe and stable operation of a power system is ensured.

To provide an information leakage prevention device capable of making information in a hard disk unusable and preventing information leakage from the hard disk. [Solution] The information leakage prevention device 1 comprises a base body on which the hard disk is placed on its inner upper surface, a holder that is placed in contact with the upper surface of the hard disk and has an explosives loading section formed to penetrate the inside of the holder from the bottom to the top, a lid that is provided to cover the top surface of the explosives loading section of the holder, explosives formed within the explosives loading section so that the central portion on the hard disk side is recessed, and an ignition device that ignites the explosives.

The invention discloses a machine learning method and system based on privacy protection enhancement, and relates to the technical field of data processing. The method comprises the steps of monitoring real-time data streams of a plurality of data sources based on real-time input data, analyzing relationships among various data, drawing a data association network diagram, evaluating information leakage risks and privacy sensitivity of a plurality of data points by analyzing interconnection density and path distribution of nodes in the network diagram, and generating sensitivity evaluation indexes. According to the method, the security and privacy protection level in the data processing process are improved by identifying the risk points in the data in real time and specifically adjusting the noise level of the data points, and the encryption processing efficiency and security are improved by comprehensively considering the data processing efficiency, security level requirements and computing resource consumption; the stability of encrypted data is tested by simulating network attack conditions, the capability of coping with network attacks is enhanced, input data is enabled to support various data analysis tasks, and the privacy and security of the data are ensured.

The invention discloses a water conservancy design archive retrieval system and method based on a local lightweight large model, and the method comprises the steps: S1, constructing a Python automatic preprocessing assembly line, extracting texts for PDF and Word multi-format archives, correcting metadata, and outputting standardized data; s2, constructing a full-text retrieval and semantic retrieval dual-mode cross-document retrieval service by relying on a Weavi ate local vector database and a lightweight text embedding model; s3, analyzing a user query intention through a local large model, synchronously triggering metadata accurate retrieval and content semantic retrieval, and generating a structured result; and S4, integrating the core module into a local area network Web platform, adopting Docker containerization deployment, and combining an RBAC permission model and JWT authentication to guarantee security. The system comprises a preprocessing module, a cross-document retrieval module, an intelligent agent module and a background management module, and collaboration is achieved through a standardized API. According to the method, the problem of archive fragmentation is solved, multi-mode retrieval breaks through keyword limitation, an intelligent agent reduces manual intervention, a localized architecture prevents secret-related leakage, background management adapts to an existing I T environment, and full-process intelligent archive service is provided for water conservancy design.

The invention provides a tunnel fire behavior inversion method based on multi-modal fusion and physical constraint, and aims to solve the problem that the position and power of a fire source are difficult to invert accurately in real time in traditional fire behavior monitoring. According to the method, data, temperature distribution images and physical parameters of distributed temperature sensors in a tunnel are collected, and a spatial-temporal feature fusion network is constructed; designing an information leakage prevention training mechanism, and adopting a modal random discarding and sensor shielding technology to avoid overfitting of the model to a specific input mode; a physical constraint loss function is introduced, and the temperature gradient, the maximum temperature rise and the longitudinal attenuation law are combined to ensure the physical rationality of an inversion result; a coarse-fine two-stage position prediction framework is adopted, and meter-scale precision positioning of the position of a fire source is achieved. According to the invention, the power and position of the fire source can be inversed accurately in real time, and reliable technical support is provided for intelligent monitoring and emergency rescue of tunnel fire.

The invention relates to a bidding document total-factor blind-state desensitization method fusing multi-modal artificial intelligence, and the method comprises the following steps: S1, obtaining an original bidding document package, carrying out the data preprocessing, and obtaining a standardized document set; s2, according to the standardized document set, performing multi-modal content identification and alignment to obtain a multi-modal content library; s3, constructing a sensitive information classification system based on domain expert knowledge and a historical case library; s4, constructing a dynamic rule base according to the sensitive information classification system; s5, according to the multi-modal content library and the dynamic rule library, in combination with deep learning, multi-modal sensitive information intelligent detection is carried out, and a sensitive area set is confirmed; and S6, based on the dynamic rule base, performing intelligent desensitization on the sensitive area set to obtain a desensitized document. According to the method, the risk of sensitive information leakage is effectively reduced while the bidding file expression effect is improved.

The invention provides a federated learning method combining acutance perception training and knowledge distillation, and belongs to the technical field of privacy protection. The method comprises the steps that a central server initializes global model parameters and distributes the initial global model parameters to participating clients; after each participating client receives the global model parameters, acutance perception training is executed based on the local data set, and a local updated global model is obtained and serves as a teacher model; through a knowledge distillation method, on the basis of a local training set and in combination with a soft label of a teacher model, obtaining student model parameter variation; performing privacy protection operation on the parameter variation of the student model, and uploading the parameter variation to a central server; the central server aggregates the parameter variation uploaded by each participating client after privacy protection, and updates a global model; the updated global model is used as an initial global model of the next round of communication. According to the method, privacy and efficiency are balanced, sensitive information leakage can be prevented, and the practicability of a federal learning global model is maintained.

The invention discloses an operation and maintenance data query analysis method of an internet data center, and relates to the technical field of operation and maintenance of the internet data center, and the method comprises the following steps: setting an initial detection threshold value for transverse flow detection in combination with existing service requirements, security policies and historical flow data, and distinguishing normal flow from suspicious flow; after an initial detection threshold value is set, comprehensive real-time data capture is carried out on all transverse flow in the IDC, and the obtained flow data is preprocessed. According to the method, through dynamic self-adaptive adjustment of the initial threshold, microscopic behavior characteristics are deeply analyzed, and the latent permeability coefficient is accurately calculated in combination with machine learning, so that intelligent identification of distributed small-flow abnormal transmission is realized. When a risk is detected, a threshold value is automatically regulated and controlled, and the perception capability for latent attacks is enhanced, so that the IDC can timely discover and respond to hidden threats, the sensitive information leakage risk is reduced, and the network security and stability are ensured.

The invention relates to the technical field of intelligent agriculture, in particular to a multi-source heterogeneous big data governance method and device.The multi-source heterogeneous big data governance method comprises the following steps that S1, multi-source heterogeneous data collection and semantic alignment are conducted, hidden semantic conflicts are eliminated; s2, performing dynamic data source adaptation and quality monitoring; s3, performing cross-domain permission penetration control; s4, building an industrial model driven by federal learning; and S5, double-chain block chain evidence storage and traceability are carried out. The method is used for disambiguating dialect term conflict, federated learning cross-domain privacy modeling and double-chain block chain evidence storage traceability through a dynamic semantic alignment neural network, and systematically solves the problems of dialect semantic segmentation, data islands and sensitive information leakage caused by small scattered distribution of peasant households in the field of traditional Chinese medicinal material planting. And a trust closed loop of government-enterprise-peasant household three-party cooperative treatment is constructed.

The invention discloses a non-intrusive load decomposition method and system based on an xLSTM-Transform model, and is applied to the technical field of non-intrusive load decomposition. XLSTM is constructed by combining sLSTM (scalar LSTM) and mLSTM (matrix LSTM), so that the processing capability of the model on time sequence data is enhanced; a causal convolution module is added behind an input layer, a sliding window in a fixed direction of the causal convolution module can more accurately extract a local load mode of an electric appliance while ensuring causality, and future information leakage is avoided; a self-attention mechanism in a Transform architecture is introduced to establish an xLSTM-Transform combination model, historical data is adopted to train the model, and finally, the model is used to perform electric appliance load decomposition. According to the method, a multi-scale electrical load mode can be effectively processed, and the problem of long-term dependence faced by a traditional time sequence model is solved.

The invention discloses an anonymous identity authentication method and device, electronic equipment and a storage medium, and relates to the technical field of anonymous identity authentication or other related fields, and the method comprises the steps: obtaining a public key set containing a trusted user public key, and extracting a local private key of a target anonymous user; a ring signature is generated based on the public key set and the private key, certification information is generated based on the private key and the ring signature, the ring signature is used for certifying that the target anonymous user belongs to one of trusted users, and the certification information is used for certifying that the ring signature is legal; packaging the ring signature, the certification information and the timestamp identifier into an authentication request, submitting the authentication request to an authentication system, and returning an authentication result; and obtaining an identity authentication token indicating that authentication passes in the authentication result, and logging in the authentication system based on the identity authentication token. According to the method and the device, the technical problem of identity authentication failure caused by information leakage and unreliable authentication due to the fact that security attacks are easily encountered in the identity authentication process in related technologies is solved.

The invention discloses a privacy information desensitization method and system for a voice generation type large model, and relates to the technical field of artificial intelligence. Input voice data is discretized, Gaussian noise disturbance sensitive features are injected, and a cross-modal voice generation model is constructed in combination with three-stage training; and meanwhile, a cross-modal privacy enhancement mechanism is applied to detect and fuzzify sensitive information in real time in an output stage. According to the invention, the adaptability of a large-scale voice generation model in a privacy protection scene is improved, and comprehensive protection of user privacy is realized. And moreover, the capability of extracting user privacy information by an adversarial attacker is effectively limited, and the risk of sensitive information leakage in the data transmission, storage and generation process of the voice generation model is reduced. On the premise that privacy is ensured, the voice generation model can still keep high-quality generation performance, generated voice output has high naturalness and accuracy, and actual application requirements are met.

The invention discloses a multi-scale point cloud pre-training method for sampling from top to bottom, equipment and a medium. According to the method, through a U-Net type asymmetric encoder-decoder architecture and in combination with a multi-scale mask, the extraction capability of local features and global information of point cloud data is enhanced. According to the method, firstly, input point cloud data is subjected to multi-scale division and mask processing, then multi-scale features are extracted through an encoder, a masked point cloud area is reconstructed through a decoder, and a masked local point cloud block is recovered through a linear projection layer. According to the invention, the problem of multi-scale information leakage in the prior art is effectively avoided, and the point cloud data processing precision is improved. After self-supervised pre-training, the model shows excellent performance in a plurality of downstream tasks, especially in applications such as 3D shape classification, object detection and partial segmentation. The method has the advantages of high robustness and wide application prospect.

The invention discloses a dynamic business data updating and querying method and system, and particularly relates to the technical field of information processing, and the method comprises the following steps: S1, real-time data acquisition and preprocessing; s2, updating the dynamic data; s3, intelligent query optimization; s4, result feedback and visualization are carried out; and S5, performing anomaly detection and self-repairing. According to the method, the real-time processing capability and query efficiency of massive heterogeneous data are remarkably improved, the distributed sensor network is combined with lightweight encryption and a differential privacy mechanism to realize localized cleaning and feature extraction of multi-source data, the load of a central server is effectively reduced, and the risk of sensitive information leakage is avoided; an incremental synchronization mechanism depends on a message queue and Hash chain log technology to ensure the atomicity and traceability of data updating in a high-concurrency scene, and by combining a dynamically generated space-time multi-dimensional index and an adaptive weight distribution algorithm, the system can intelligently analyze query semantics and optimize path selection, and the response time of complex query is greatly shortened.

The invention discloses a multi-encryption-based AI glasses security payment method and a related device, and relates to the field of security payment. In the method, multi-modal biological information of a user is collected, when the multi-modal biological information is consistent with preset biological information, a dynamic password request is sent to bound target equipment, and a dynamic password returned by the target equipment is received and displayed on a display interface; and when the user inputs the dynamic password on the payment interface, encrypting payment information by using a private key to obtain encrypted information, sending the encrypted information to a payment server to decrypt the payment information through the payment server and verify the decrypted data through a block chain network, and when the verification result is successful, sending the encrypted information to the user. Fund transfer is executed according to the payment information; and fund transfer information returned by the payment server is received. By implementing the technical scheme provided by the invention, the payment security and convenience are improved, and information leakage is effectively prevented through a multi-encryption technology.

The invention relates to the technical field of network security, and more specifically discloses a network security risk detection system and method based on a big data technology, the feature vector of intrusion data is extracted and analyzed by using a classifier, so that the network security risk level is evaluated, a basis is provided for real-time early warning and targeted defense, and the network security risk detection efficiency is improved. Defects of an existing security monitoring system are made up, network security threats are dealt with, and information leakage and economic loss are prevented.

The invention discloses a strong-isolation private domain agent data processing method and server, and relates to the field of data security, and the method comprises the steps: receiving a data processing request sent by a user through a user terminal, and obtaining a department identifier; according to the department identification, the data processing request is routed to a corresponding internal data interface module, the internal data interface module is used for analyzing the data processing request, obtaining a processing task and calling an intelligent agent engine, and the intelligent agent engine is used for obtaining a temporary access credential according to the department identification and sending the temporary access credential to the corresponding internal data interface module; accessing an internal data storage area of the department based on the temporary access credential to obtain to-be-processed data; and the agent engine is used for executing the processing task, analyzing and processing the to-be-processed data to obtain a processing result, and transmitting the processing result to the user terminal through the output interface module. According to the application, the risk of data leakage and the risk of unauthorized access and information leakage among different departments can be effectively reduced.

The invention belongs to the technical field of network information security, and discloses an information security management system based on network operation and maintenance. The method comprises the following steps: periodically acquiring multi-dimensional traffic data of a network node through a traffic acquisition module, and constructing and labeling a network security situation map by means of an asset labeling module; the authority distribution module automatically adjusts the authority of the abnormal access node by analyzing the node access behavior; the threat detection module analyzes the flow in real time, drives the self-adaptive isolation module to quickly isolate threat nodes according to the generated temporary strategy, and updates the joint defense model for cooperative defense; the attack evolution prediction module performs attack path prediction by extracting attack features and optimizes an isolation strategy accordingly; and information leakage is effectively prevented.

The invention discloses a lithium battery SOH and RUL prediction system and method based on multi-scale feature federation, and relates to the technical field of battery detection, the system comprises a data acquisition and feature extraction module, an encryption and uploading module, a model construction module, a recursion updating module and a work order generation module; through the multi-scale feature fusion formula, the time sequence features and the statistical features of the lithium battery are organically combined, the fusion feature vector is generated, degradation information of the lithium battery under different time scales and operation conditions is comprehensively captured, the health state of the lithium battery and the accuracy of prediction of the remaining service life of the lithium battery are improved, and the prediction accuracy of the remaining service life of the lithium battery is improved. And meanwhile, through dynamic Top-K sparse compression, 8-bit linear quantization and AES-256-GCM encryption technologies and in combination with a differential privacy protection mechanism, the data transmission quantity is reduced, the data security is enhanced, sensitive information leakage is prevented, and a reliable guarantee is provided for remote monitoring and predictive maintenance of the lithium battery.

The invention provides a communication method and device, which are used for desensitizing a sensitive area and / or a sensitive target in a sensing service to avoid sensitive information leakage. The method comprises: a first network element receiving a first message from a second network element, the first message being used for requesting perception information, the first message comprising a first region and / or a first target, the first region comprising a region that does not allow perception, and the first target comprising a target that does not allow perception; and the first network element sends a second message to an access network device, the second message being used for requesting the access network device to perceive, the second message comprising a second region and / or a second target, the second region being a region permitting perception, and the second target being a target permitting perception. Based on the method, the sensitive information in the sensing request can be desensitized.

The invention discloses a financial transaction data intelligent protection method and system based on a block chain, and belongs to the technical field of block chains, and the method comprises the steps: collecting financial transaction data from a data source of a financial transaction system, and carrying out the cleaning, format unification and abnormal value detection processing; dynamic encryption strength adjustment is carried out through the smart contract according to the sensitivity and the purpose of the transaction data, compared with a traditional fixed encryption mode, data can be protected more accurately, identity authentication is carried out in combination with a zero-knowledge proof mechanism, specific identity information does not need to be exposed in the identity authentication process, identity information leakage is effectively prevented, and user experience is improved. Encrypted financial transaction data are recorded in a block chain, it is ensured that the data are not maliciously tampered in the storage and transmission process through the non-tampering characteristic of the block chain, each transaction generates a unique hash value as an identity identifier, and decentralized propagation and consensus verification are performed in a block chain network, so that the security of the transaction is improved. And the integrity and credibility of the data are further enhanced.

The invention discloses a computer big data information security risk behavior identification method and system, and aims to solve the problem of sensitive information leakage existing in image information transmission in chat software. According to the method, when a user sends a picture to a target contact person, detail information in the picture, such as an environment scene, position information, text content, associated figures and clothes and accessories, is automatically recognized, and a sensitive information prompt list is generated for the user to view. And the user can select to cancel the sending, perform desensitization processing or confirm the sending according to the prompt list, so that the sensitive information is effectively prevented from being leaked. According to the method, the sensitive information in the picture is automatically identified and the user is prompted, so that the safety awareness of the user is enhanced, and the risk of sensitive information leakage is effectively reduced. And meanwhile, flexible desensitization processing options are provided, so that the privacy of the user is protected, and the individual requirements of the user are met.

The invention discloses a cloud storage image privacy protection encryption method based on cross-plane dynamic grouping, and belongs to the technical field of information security and cloud storage. The method comprises the following steps: acquiring an image in a cloud storage environment, and preprocessing the image; encrypting the image by adopting a dynamic grouping cross-plane encryption method; constructing a ciphertext image structure containing permission labels and visual adjustment information and a data packet used for realizing access control and semantic hierarchical display; and uploading the ciphertext information to a cloud storage system through a secure transmission protocol. According to the method, dynamic grouping cross-plane encryption is adopted, so that the security of the encrypted image is improved, and security vulnerabilities caused by a fixed grouping mode are avoided; in combination with cloud storage, data access and authority control are adaptively optimized, so that different users can access corresponding encrypted data according to authorization levels, and the adaptability of the encryption method is improved; the effectiveness of local disturbance is improved through dynamic grouping, large information leakage is effectively avoided, and the data security is improved.

The invention discloses a video conference multi-modal data alignment method and device based on a causal mask, equipment and a medium, and relates to the technical field of computers, and the method comprises the steps: carrying out the feature extraction and fusion of an original audio, an original video stream and an original document in an online video conference, time sequence division is carried out based on the obtained multi-modal fusion features to obtain a triple time sequence window; determining an initial weight value corresponding to the triple time sequence window, and performing normalization adjustment on the initial weight value by using a preset constraint condition to obtain an adjusted weight; indexing a preset time sequence offset matrix by using a speaking identifier of a speaking party, correcting an original time sequence of the triple time sequence window based on an indexing result, and determining a target attention result corresponding to the triple time sequence window by using a preset causal mask mechanism, and performing multi-level alignment fusion on the multi-modal fusion features based on the target attention result to obtain a multi-modal alignment result. The precision of the multi-mode alignment technology is improved, and future information leakage is avoided.

The invention relates to the technical field of network information security vulnerability monitoring, and discloses a network information security vulnerability monitoring method, system and device and a storage medium, and the method comprises the steps: obtaining network information security data; performing initialization processing on the network information security data to obtain a preprocessed data sequence; performing autocorrelation calculation on the preprocessed data sequence to obtain an autocorrelation sequence; performing cross-correlation calculation on the self-correlation sequence to obtain a cross-correlation sequence; screening out a suspicious parameter interval according to the self-correlation sequence and the cross-correlation sequence, and determining a suspected information leakage sequence; calculating based on the self-correlation sequence, the cross-correlation sequence and the suspected information leakage sequence to obtain the leakage probability of the sensitive data; and according to the leakage probability of the sensitive data, analyzing the security of the data information to obtain a corresponding security report. According to the method, the accuracy and efficiency of information monitoring can be improved, and the security and integrity of network data transmission are guaranteed.

The invention provides a privacy protection method based on computing migration and AI behavior trajectory analysis, and relates to the technical field of privacy protection of the Internet of Things. According to the method, a privacy label is bound to target data, an AI behavior track fingerprint is generated in each AI processing stage, and matching verification is performed on the AI behavior track fingerprint and the privacy label, so that dynamic access control and security audit among terminal equipment, edge nodes and a cloud are realized. On one hand, data can be effectively prevented from being stolen or tampered in the migration process through an encryption transmission and fingerprint verification mechanism; and on the other hand, the fingerprint and label information can be selectively stored in the block chain or the alliance chain side chain, so that the tamper-proofing performance and the distributed credibility of the audit record can be enhanced. The method is suitable for various AI application scenes, the risk of unauthorized calling and sensitive information leakage can be reduced, and the traceability and safety of the whole data processing process are improved.

The invention discloses a secure video transmission method based on a block chain and multi-path dynamic routing, and belongs to the field of video data secure transmission. According to the invention, malicious nodes are identified and isolated through a black hole detection mechanism, data packet loss and tampering are avoided, and the security of video data transmission is improved. And credibility calculation is carried out on the nodes, and only credible nodes are selected for data transmission, so that retransmission and resource waste are reduced. According to the dynamic routing optimization, the optimal path is quickly selected by analyzing the network topology and the node trust value in real time, the transmission delay caused by node failure or path change is reduced, and the network stability and the transmission efficiency are improved. And finally, introducing a block chain technology, recording operation in a data transmission process in a tamper-resistant distributed account book, and verifying the integrity and authenticity of the data by using a content identifier. The decentralization characteristic of the block chain enhances the transparency and traceability of the transmission process, and effectively prevents data tampering and information leakage.

The invention discloses a Diithium rapid side channel attack analysis method, which belongs to the technical field of information security, and comprises the following steps of: detecting leakage by adopting a Gaussian mixture difference method to complete detection and selection of feature points; the signature information is used to classify the actual leakage in which the private key participates, and the distribution difference is used to recover the normal domain private key. According to the method, the problem of high time overhead under small sample leakage of the existing non-template attack is solved. A complete private key can be recovered within a few minutes at a high noise level by using side information leakage generated by a small amount of signatures. Compared with an existing leakage detection method, the Gaussian mixture difference method can complete detection and selection of feature points without obtaining a sensitive intermediate value. The method can be used for detecting whether the password equipment and the password chip are safe and reliable or not, such as the fields of bank cards of financial economy, physical digital currency cards, mobile phone chips of mobile communication and the like, so as to detect whether the equipment has security risks or not.

The invention discloses a privacy protection truth value discovery method based on reputation in a block chain. The method provided by the invention aims at the problems of perception data quality reduction, participant privacy information leakage and the like caused by lack of a trusted participant screening mechanism and insufficient privacy protection in the crowd intelligence network. Therefore, participants with high creditworthiness are screened on the premise of privacy protection to complete a data perception task, so that high-quality data are obtained. According to the method, firstly, a double-chain collaborative architecture is constructed based on a block chain, and privacy protection is performed on creditworthiness of participants through exclusive or homomorphic encryption; in the subsequent task allocation stage, the high-reputation participants are preferentially selected to execute the data acquisition task in combination with multi-round encryption communication and a dynamic screening mechanism; and finally, on the basis of the data of the high-credibility participants, calculating the task scores of the participants with unknown reputation, dynamically updating the reputation of the participants, and ensuring the authenticity and accuracy of the data.

The invention discloses an intelligent retrieval and hierarchical authorization utilization system and method for archive resources, and belongs to the field of intelligent retrieval. Natural language query of a user is received, the language type of the user is recognized, a semantic vector is generated in combination with a multi-language natural language processing model, and then the semantic vector is matched with each archive semantic representation in an archive database; for the situation related to cross-language matching, a cross-language similarity offset rate and a language context consistency score are further calculated, the semantic generalization ability is divided into a high level, a middle level and a low level according to a preset weight model, and for the middle level situation, a misjudgment risk assessment mechanism is introduced to judge whether additional authorization is needed or not; and when the file is low in level and the languages are inconsistent, the user is directly prohibited from accessing the high-security-level file, so that the information leakage risk caused by semantic generalization is effectively prevented, and the high-sensitivity file is safely and accurately controlled in a multi-language environment.