5248results about "Program/content distribution protection" patented technology

In embodiments, systems and methods for improving machine-learning systems are disclosed. In embodiments, a system includes a data pool system that is configured to receive data from a plurality of different data sources and maintain a training data set that is used to train a specific machine-learning model based on the data from the plurality of different data sources. In embodiments, the system further includes a data scoring system that determines a data reliability score corresponding to the new data based on a set of intrinsic features of the new data and a data scoring model, wherein the data pool system selectively adds the new data to the training data set based on the reliability score of the new data. The system also includes a machine learning system that trains the specific machine-learning model based on the training data set.

A method for structuring the latent space of an autoencoder is provided. The method includes analyzing natural language descriptions related to input data; creating language-guided libraries that categorize and abstract data features based on the analyzed descriptions; mapping input data into the categorized and abstracted features within the latent space of the autoencoder; and training the autoencoder to minimize reconstruction loss while adhering to the structure imposed by the language-guided libraries.

The invention relates to a security protection method, system and device for data sharing and exchange and a medium. The method comprises the steps that original data are loaded, sensitive information in the loaded data is recognized, and the original data are classified according to the category of the sensitive information; obtaining user demand information and a corresponding target scene condition, adding noise to the sensitive information through a differential privacy algorithm according to the user demand information and the target scene condition, and generating privacy protection data; transmitting and encrypting the privacy protection data to obtain corresponding encrypted data, transmitting the encrypted data through a corresponding encrypted channel, recording an operation log in a data transmission process, and embedding an invisible watermark to track the use condition of the encrypted data; and analyzing the privacy protection requirement of the target scene condition, dynamically adjusting the privacy protection parameter of the encrypted data according to a preset use specification, and recovering the encrypted data when the life cycle of the encrypted data is ended. The method and the device have the effect of improving the data sharing security.

A method is provided for dynamically generating a digital watermark for a data file. The method includes receiving a request to access the data file from a user; dynamically generating an encryption key based on at least one parameter selected from the group consisting of the identity of the user, the time of access, and the mode of access; embedding a digital watermark into the data file using the dynamically generated encryption key, wherein the digital watermark is unique to the request; providing access to the data file with the embedded digital watermark to the user; and storing information related to the encryption key and the parameters used for its generation in a secure database.

The invention relates to the technical field of information traceability, in particular to a digital information traceability management system. The system comprises an information fingerprint extraction module, an origin node consensus module, a circulation path mapping module, an information integrity verification module and a behavior consensus feedback module, and can be used for acquiring original information; performing text semantic feature extraction on the original information to obtain semantic features; performing information fingerprint extraction on the semantic features to obtain semantic fingerprints; performing consensus processing on the semantic fingerprint based on an origin node to obtain a consensus result; generating a source point voucher according to the consensus result to obtain the source point voucher; performing circulation behavior triggering according to the source point voucher to obtain a to-be-recorded behavior; and performing content change analysis according to the to-be-recorded behavior to obtain a changed semantic fingerprint. According to the method, through fusion of semantic hash and behavior consensus, the source tracing robustness, safety, efficiency and applicability are improved.

The invention relates to the technical field of data access security, and discloses a container mirror image security management method and system, and the method comprises the steps: constructing a container mirror image, generating a differential encryption key through a strategy center, carrying out the encryption strategy of a kernel dependence layer, a runtime environment layer, an application code layer and a sensitive configuration layer, and forming a hierarchical protection basis. If an access request is triggered, firstly collecting equipment fingerprints and geofence information and evaluating an environmental risk score, verifying access authority and an access scene matching degree through attribute-based encryption, analyzing operation track characteristics in real time, identifying an abnormal mode, and if the three-layer verification is passed, generating a temporary access token; according to the method, access request authority is verified, a temporary access token is matched, key fragments are synthesized, a master key is only temporarily generated in a memory and encrypted and stored, and through combination of a double-layer encryption channel and inner-layer and outer-layer defense, the anti-attack ability of container mirror image transmission is improved, and man-in-the-middle attack and data tampering are effectively coped with.

The invention relates to the technical field of automatic deployment of cloud computing platforms, in particular to a self-adaptive deployment method and system oriented to a credential heterogeneous environment. Aiming at three technical bottlenecks of low multi-CPU architecture adaptation efficiency, frequent software dependence conflicts and complex security baseline configuration in a localization process, a heterogeneous computing resource intelligent scheduling engine and a dynamic security policy generation mechanism are innovatively provided. The method comprises the following steps: constructing a heterogeneous resource portrait through a hardware feature automatic identification technology, and realizing component installation sequence optimization based on a DAG dependency relationship analysis algorithm and Kahn topological sorting; and creating an adaptive network security policy, and sensing the firewall state of the target system in real time through a probe. Compared with a traditional deployment mode, the method supports cross-architecture compatibility, solves the problem of dependency conflicts, improves deployment efficiency, and guarantees consistency and safety of the system.

The present disclosure relates to configured artificial intelligence methods and systems and related transportation systems and methods, including software-defined vehicles, for transportation systems using sensor and other data, and the integration of a transportation system with an AI convergence system of systems, providing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system.

Herein disclosed is receiving predetermined content, receiving a request to transform the predetermined content into a derivative work, receiving a requested theme for the derivative work, using generative artificial intelligence to create the derivative work generated as a function of the predetermined content and the requested theme, determining if the generated derivative work is approved, in response to determining the generated derivative work is approved, applying a digital watermark to the approved derivative work, configuring an authorization server to govern use of the approved derivative work based on the digital watermark and providing user access to the authorized derivative work. The requested theme may be determined using a Large Language Model (LLM) and a chatbot interview. The generative artificial intelligence may comprise a diffusion model. The content may comprise music.

The invention provides a block chain-based large model use traceability and access control method and system, and belongs to the technical field of block chains, and the method comprises the steps: extracting key version information of a large model, encrypting the key version information, storing the encrypted key version information to a block chain network, and generating a model evidence storage record; generating a dynamic watermark identifier based on the user identity identifier, the calling timestamp and the model evidence storage record, and embedding the dynamic watermark identifier into an output result of the large model; monitoring calling behavior data of the large model in real time through an intelligent contract in the block chain network; and when abnormal calling behavior data is detected, tracing the source of the abnormal calling behavior data based on the dynamic watermark identifier and the model evidence storage record, and executing permission limiting operation through the smart contract. According to the method, full life cycle protection and calling traceability management of the large model are realized based on the block chain evidence storage, the dynamic watermark identifier and the intelligent contract, so that effective traceability management and access control are realized in the use process of the current large model.

The invention discloses a big data-driven hydrogen fluoride purification risk entropy assessment and multi-parameter monitoring system, which relates to the technical field of chemical process monitoring, comprises a multi-mode heterogeneous sensor network, and realizes high-precision detection and three-dimensional monitoring by using a nanowire FET (Field Effect Transistor) and the like. The space-time tensor decomposition module extracts features and identifies a causal relationship; the risk entropy manifold learning module evaluates the risk; the self-adaptive twin network monitors the abnormity and simulates and disposes the abnormity; and a knowledge graph-reinforcement learning system decision making subsystem and a plurality of subsystems such as an optical monitoring subsystem and a multi-scale modeling subsystem are also provided, so that whole-process risk management and control are realized. The hydrogen fluoride purification monitoring level is greatly improved, the detection sensitivity and the anomaly detection accuracy are remarkably improved, risk early warning is more timely, decision response is accelerated, the production efficiency is improved, energy consumption is reduced, the system has self-repairing and self-power-supply capabilities, data are safe and traceable, reliable operation of the system is guaranteed, and economic losses and potential safety hazards are reduced.

According to the archive data tracking and multi-party cooperation auditing method based on the block chain smart contract provided by the invention, the authenticity and non-tampering property of archive data are guaranteed through a block chain technology, and an efficient and transparent auditing process is realized in combination with the smart contract. According to the method, a multi-level encryption storage and unique identifier generation mechanism of archive data is designed and is used for accurately tracking an archive circulation process; the access behavior is automatically recorded and verified through the intelligent contract, and the operation compliance is ensured. A distributed consensus algorithm is adopted for multi-party cooperative auditing, multiple auditing parties are allowed to participate together, and meanwhile objectivity and authority of auditing results are ensured. The method improves the safety, transparency and efficiency of archive data management and auditing, and can be widely applied to archive management and auditing scenes in the fields of governments, enterprises, education and the like.

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.

A computer-implemented method is provided that detects jailbreak attempts against generative models, which may involve a shift between benign and malicious content. The method includes determining a probability-based metric for each of a plurality of tokens in a target text using a language model, the probability-based metric being based on a probability at least one preceding token. The probability-based metrics are processed to identify a subset of the plurality of tokens having a change in the probability-based metric with respect to others of the plurality of the tokens not within the subset of the plurality of tokens. A jailbreak attempt in the target text is detected in response to identifying the change in the probability-based metric in the subset of the plurality of tokens.

The invention provides a cross-platform education data privacy protection analysis system, and relates to the technical field of data privacy protection. The cross-platform education data privacy protection analysis system comprises a multi-modal data acquisition unit, a dynamic privacy analysis engine, a federal learning processing core module, a block chain enhanced access control system, a privacy watermark traceability module and a compliance autonomy unit. Dynamic coupling of scene sensitivity and data protection intensity is realized through a dynamic privacy risk scoring equation and an adaptive differential privacy noise equation; the problems of privacy disclosure, compliance verification and traceability and responsibility investigation in cross-platform education data sharing are solved by combining federal learning, block chain evidence storage and privacy watermarking technologies. According to the method, the privacy risk assessment precision is remarkably improved, the model precision loss is reduced, full-life-cycle data security management and control are supported, and the method is suitable for multiple scenes such as K12 education and college educational administration.

The invention discloses a non-perpetual culture immersive interactive experience device and system based on a virtual digital human, and relates to the technical field of intangible cultural heritage intelligent interaction, and the device comprises a multi-mode sensing module which collects data such as user actions and voices through a depth camera and the like; the non-perpetual knowledge base module stores a non-perpetual knowledge graph and a case library; the digital human modeling engine fuses the inheritor characteristics and the user data to generate a virtual digital human; the immersion interaction engine constructs a cross-platform rendering environment to realize five-sense fusion experience; and the adaptive learning module analyzes the interaction sequence to optimize the digital human behavior, and all the modules cooperate to realize the intelligent interaction experience of non-genetic culture. Through the multi-modal perception module, the digital human modeling module and the like, the immersive interactive experience of the non-perpetual culture is realized, the user behavior can be accurately captured, the characteristic virtual digital human is generated, the five-sense fusion experience is provided, the content can be optimized based on the user interaction, the inheritance and propagation of the non-perpetual culture are promoted, and the participation degree and the sense of identity of the user are improved.

The invention provides an anonymization processing method and system for emotion data in a vehicle. The anonymization processing method comprises the steps of collecting multi-mode emotion data; correspondingly carrying out local preprocessing and multi-modal alignment processing on the multi-modal emotion data; corresponding sensitive emotion feature information in the preprocessed multi-modal emotion data is extracted through a lightweight recognition algorithm, and the sensitive emotion feature information at the recognized position is packaged in a unified mode; performing desensitization processing on various types of emotion modal data, and encapsulating and synchronizing desensitization results with labels; deep emotional feature extraction is performed on the image, the voice and the physiological signal through a multi-modal fusion model, and the extracted three types of deep features are fused to obtain an emotional representation vector; and inputting the emotion representation vector into a pre-trained emotion recognition model, and outputting the current emotion state of the passenger, including the specific emotion category and the corresponding confidence coefficient. According to the method, emotion analysis and transmission are performed after data anonymization is realized, and accurate judgment of the system on the emotion state is not influenced while privacy security of the user is ensured.

The invention belongs to the technical field of data management, and particularly relates to a block chain-based heterogeneous data source right confirmation method, which comprises the steps of multi-source heterogeneous data acquisition, digital fingerprint generation, block chain hash anchoring, data storage and circulation and ownership tracing and verification. According to the invention, original data is collected from various terminals and system platforms by adopting a multi-source heterogeneous data acquisition technology so as to ensure the diversity and universality of data sources, and the collected original data is processed and unique digital fingerprints are generated. The method comprises the following steps: processing a digital fingerprint through a hash algorithm to generate a corresponding block chain hash value, anchoring the block chain hash value on a block chain, ensuring authority and permanency of a data ownership record, storing original data in a safe data warehouse, recording the hash value and ownership information of the data on the block chain at the same time, and storing the data in the block chain. Through combination of multi-source heterogeneous data acquisition, digital fingerprint generation and block chain hash anchoring technologies, automatic registration and efficient management of data ownership are realized.

The invention discloses a large model generation content traceability technology based on model copyright ID watermark embedding, which comprises the following steps that: firstly, a large model content generator compiles a dynamic watermark embedding process into an arithmetic circuit, and generates a watermark text and a proof by using a zero-knowledge proof algorithm; then the large model content generator publishes the text with the watermark and the proof together, and hides the copyright ID and the secret key; a large model content user verifies the proof and the watermarked text through a smart contract by adopting a zero-knowledge verification algorithm; after verification is passed, an adversarial sample corresponding to the text with the watermark is generated, an adversarial training method is used for optimizing the text with the watermark, and parameters of the Viterbi balance algorithm are dynamically adjusted and improved. According to the method, the concealment and the generation quality are balanced, high-capacity watermark embedding is realized, the tamper resistance and the accurate traceability of multi-model copyright disputes are improved, and the privacy protection intensity is improved.

Systems and methods facilitate saving data storage through data storage management of storage location(s) by training, via an iterative training and testing loop, a predictive model using training data to detect data redundancies from two or more datasets stored to data storage location(s), the training includes testing the predictive model by predicting a target variable and iteratively adjusting weights and calculations during each subsequent iteration to improve predictability of the target variable, where the predictive model is trained to identify data similarities among the two or more datasets. Based on any error in predicting the target variable being less than a predetermined level, the predictive model is deployed and applied to at least two datasets to quantify a percentage of similarity among the at least two datasets. If it is determined the percentage of similarity surpasses a predefined threshold percentage, then an electronic notification is transmitted.

The invention provides a full-process protection method for embroidery digitization fusing deep learning and block chain evidence storage. The method comprises the following steps: step 1, acquiring multi-modal data of an embroidery work by adopting a multi-modal data acquisition technology; 2, eliminating environmental noise from the multi-modal data acquired in the step 1 through an improved adaptive Kalman filtering algorithm, and compressing redundant time sequence information through a key frame extraction technology to obtain a standardized process data set; step 3, utilizing deep learning to drive stitch recognition, and realizing reproduction of an embroidery method through a convolutional neural network, a generative adversarial network and an edge detection algorithm; 4, linking through a block chain protocol, determining digital ownership, and managing resources in batches; and step 5, mobile terminal application deployment: constructing a mobile application through a lightweight cross-platform framework to realize embroidery digital protection. According to the method, the data basis is greatly improved, and rich and systematic data resources are provided for embroidery digital research and application.

The invention discloses a digital content tracing system based on a block chain technology, and particularly relates to the technical field of block chains. The tampering risk of the digital content in the distributed storage process is evaluated by analyzing the multi-node data synchronization behavior; the transaction overstock risk judgment module evaluates the transaction overstock and local congestion hidden danger according to the transaction verification rate in the propagation process; and the reliability evaluation module judges the reliability of the digital content tracing chain in combination with the tampering risk and the local congestion hidden danger. The abnormal risk assessment module analyzes operation behavior abnormity through the intelligent contract log, and the conflict possibility assessment module detects consistency deviation of circulation records; and the potential risk assessment module synthesizes the multi-dimensional risk, generates a potential risk assessment result of the tracing chain, and determines whether to trigger an on-chain responsibility node tracing verification mechanism, so that potential hidden dangers in the tracing chain can be comprehensively identified, and an efficient and reliable solution is provided for block chain digital content tracing.

The invention provides an audio watermark generation method, audio watermark generation equipment and a computer storage medium. The audio watermark generation method comprises the following steps: acquiring target audio data; extracting amplitude spectrum features and phase spectrum features of the target audio data; acquiring watermark adding frame information of the amplitude spectrum features; selecting one watermark information code from a watermark codebook library, and performing frame-level copying according to the frame number of the amplitude spectrum characteristics to obtain first watermark data; performing frame selection on the first watermark data according to the watermark adding frame information to obtain second watermark data; fusing the amplitude spectrum feature with the second watermark data to obtain a watermark amplitude spectrum feature; and fusing the watermark amplitude spectrum feature and the phase spectrum feature to obtain watermark audio data. According to the audio watermark generation method, the watermark adding selector is designed, the frame number and the frame number needing to be added with the watermark are selected through the frame selection logic, and it is guaranteed that after the audio watermark is generated, the audio listening feeling is not affected.

Computerized systems and methods are disclosed to generate a document from one or more first and second text prompts, generating one or more context-sensitive text suggestions using a transformer with an encoder on the text prompts and a decoder that produces a text expansion to provide the context-sensitive text suggestions based on the one or more first and second text prompts by applying generative artificial intelligence with token biased weights for zero-shot, one-shot or some-shot generation of the artificial intelligence context-sensitive text suggestions from the one or more first and second text prompts.

The invention discloses a multi-source data isolation method and system based on a privacy computing platform, and relates to the technical field of data isolation. After basic parameters and transmission parameters of platform multi-source data are obtained, the basic parameters and the transmission parameters are comprehensively analyzed based on an isolation rule to generate an isolation value for each piece of data; whether the data needs to be isolated or not is judged according to a comparison result of an isolation value and an isolation threshold value, after all multi-source data are analyzed, an unisolated data set and an isolated data set are generated, the isolated data set is divided into public information, sensitive information and privacy information, a dynamic isolation strategy is appointed according to a hierarchical division result of the isolated data set, and a dynamic isolation result is obtained. And adjusting the isolation level according to the real-time privacy demand and the data flow direction. According to the isolation method, after the multi-source data is divided into the non-isolated data set and the isolated data set, the data in the isolated data set is dynamically isolated, so that the analysis and processing efficiency of the multi-source data is improved, and the isolation effect is better.

The invention provides a firmware encryption method and system based on a device unique identifier and dynamic key verification, and relates to the technical field of firmware protection, and the method comprises the steps: obtaining a unique identifier of a device, the unique identifier being hardware feature information which cannot be modified; a dynamic key is generated based on the unique identifier and the random number, and the dynamic key is realized through an encryption algorithm and is regenerated every time the device is started or firmware is updated; the dynamic key is stored in a storage area protected by hardware, and the storage area is subjected to double protection of hardware isolation and logic encryption; and when the device is started, the dynamic key is regenerated, the unique identifier of the dynamic key and the unique identifier of the dynamic key stored in the storage area are compared and verified, if the dynamic key and the unique identifier are consistent, firmware operation is allowed, and otherwise, a safety response mechanism is triggered. According to the invention, the security and reliability of firmware protection can be improved.

The invention discloses a digital collection full-life-cycle traceability tracking system based on a block chain, and the system comprises a digital identification module, a verification module, a packaging transfer module, a verification module, an integration module, and a system construction module, and achieves the full-life-cycle traceability tracking of a digital collection through the technologies of multi-dimensional feature extraction, watermark embedding, distributed storage, and zero-knowledge proof. And tracking and verification of the whole process from creation to transaction of the digital collections are realized. According to the system, the transaction authorization model is encapsulated by adopting the intelligent contract, so that safe transfer of ownership is ensured; a tamper-proof verification mechanism is constructed through multi-level integrity verification; different platform data are integrated by using a cross-chain technology to form a comprehensive traceability graph, so that key problems of authenticity verification, copyright protection, value evaluation and the like faced by a digital collection market are effectively solved. In addition, the system also establishes a scientific authenticity evaluation and value dynamic evaluation system, provides an objective basis for market pricing, and significantly improves the credibility and safety of the digital collections.

The invention discloses an open source software risk detection method and device based on an SBOM list, equipment and a medium, and relates to the technical field of software detection. The method comprises the following steps: acquiring an SBOM of target software, and analyzing component information of the SBOM; generating a feature character string according to the component information, and calculating a feature hash value through a hash algorithm; based on a known open source component, analyzing component information and vulnerability information of the component, calculating a feature hash value of the component, and storing the feature hash value to form a feature database; searching a matching item in a feature database based on the feature hash value of the target software component, and if the matching item is found, obtaining a license and vulnerability information of the target software component; if not, marking, manually judging and supplementing the data to the feature database; constructing a license compatibility matrix, and checking the compatibility of the obtained license and the overall license of the project; and comparing the acquired vulnerability information with a feature database, calculating vulnerability severity by combining a CVSS scoring model, and generating a priority repair report. According to the method, the software security detection speed and accuracy are improved.

A method of controlling access to a digital resource of a group of digital resources, the method comprising: receiving a request from a user via a computer browser of a user equipment for access to a digital resource of a group of digital resources; determining if the resource comprises at least one information sensitive feature; determining for each of the determined at least one information sensitive feature a confidence level (CON) for the feature; determining for the user a clearance level (CLR) for each of the determined at least one information sensitive feature; if the request for access is allowed, determining whether to watermark the resource responsive to the CON and the CLR for each of the at least one information sensitive features; and if it is determined to watermark, watermarking the resource without affecting data defining the resource at a source from which the resource is downloaded to provide the requested access.

The invention discloses a vehicle-mounted controller firmware upgrading method, which relates to the technical field of intelligent networked automobiles, and comprises the following steps of: accurately describing a dependency relationship between versions by introducing a directed acyclic graph, providing a clear topological sorting basis for an upgrading process, and improving the upgrading efficiency. The system function conflict and disorder caused by disordered upgrading sequence are fundamentally avoided; a distributed consensus protocol is utilized to ensure that each controller achieves state coordination before upgrading, so that the overall robustness of a multi-controller cluster is effectively improved when the multi-controller cluster faces network delay or individual node abnormity, and upgrading failure or version bifurcation caused by the fact that part of nodes are not ready is avoided; through the combination of the group-level binding token and the atomic switching mechanism, the controller group with a strong function dependency relationship can be ensured to be effective and updated as a unified transaction unit, and the safety and reliability of cross-domain function linkage are remarkably enhanced.