60 results about "Verified computing" patented technology

Disclosed are systems and methods for a hybrid cloud-edge computing platform using decentralized networks for solving resource-intensive computation tasks such as machine learning and video processing tasks. In some respects, the cloud-edge computing platform comprises a coordinator cluster and worker nodes partitioned into shards, which include cloud-based networks and edge networks, and which cater to a wide range of computation requirements. The coordinator cluster manages workloads by selecting the appropriate shard and worker nodes to execute tasks based on computed efficiency scores. Worker nodes may be incentivized with rewards such as cryptocurrency tokens for successful completion of tasks. The disclosed architecture also supports the pipelining of multiple tasks in a workload, where the output of one task is used as input for another task. In some respects, the computing architecture includes a blockchain-supported edge network, which enables verifying compute tasks and managing reward distribution for worker nodes using smart contracts.

An attestation service is configured to receive a request to enable attestation for a compute instance according to an attestation policy indicating one or more baseline health measurement values for validating compute instances. The attestation service provides a network endpoint for the compute instance to request attestation. The attestation service receives, via the network endpoint from a compute instance, one or more health measurement values of the compute instance. The attestation service validates the compute instance based at least on a comparison of the one or more current health measurement values and the one or more baseline health measurement values. The attestation service, in response to validating the compute instance, generates an attestation token indicating that the compute instance is authorized to access a secured resource of the provider network.

Various aspects of the disclosure relate to automated testing for application programming interfaces (APIs). A dynamic API validation computing system leverages a generative AI model to dynamically generate a multitude of data sets and rules for use during API validation activities. A federated byzantine agreement mechanism performs the validation of each API using the generated test cases and test data. A generator engine incorporates a generative AI model that may be trained on a large corpus of API metadata and / or data characteristics to predict data patterns and / or validation rules for each of the APIs under test. The generator engine may also predict a structure and format of requests based on the training model inputs. Multiple Test cases for an API created through use of the generative AI model may be distributed and executed on different testing nodes that reach a consensus about whether each API has passed or failed.

This disclosure describes techniques that include validation or other assessments of digital systems, such as machine learning models and other statistical models. In one example, this disclosure describes a method that includes receiving, by a validation computing system and from a development system, a request to perform a test on a model configured to execute on the development system; outputting, by the validation computing system to the development system and in response to the request, an instruction; enabling the development system to process the instruction; receiving, by the validation computing system, test response data; evaluating, by the validation system, the test response data.

The invention discloses an abnormal operator positioning method and device, a storage medium and a program product, and relates to the technical field of computers, and the method comprises the steps: rapidly locking the candidate range of an abnormal operator through comparing the operation result difference of a target model in different computing devices, and avoiding the resource waste of total operator troubleshooting; dynamically generating a to-be-verified operator set according to the operators called by the target model, and compressing the troubleshooting range from the whole operator set to a set corresponding to the operators called by the target model; the technical problem of low efficiency in a manual layer-by-layer verification method is solved by returning to verification computing equipment one by one and combining automatic result comparison and accurate positioning of abnormal operators, and the technical effects of improving the troubleshooting efficiency and avoiding the risk of manual misoperation are achieved.

The invention relates to a method for verifying the suitability of a computer decoy for a given objective, for its deployment on a computer system, said decoy being designed to adopt adaptive behavior depending on an attacker of the computer system, and the method comprising: - a modeling step (S1) of the adaptive behavior of the decoy in the form of a state graph, - a specification step (S2) of the objective, in the form of a formal equation, - a verification step (S3) based on the state graph and the formal equation

A method for providing a function via a network of processing units using multiple computing instances. Each computing instance implements the function and is configured, when it is executed, to determine at least one result in response to a call-up of the function. The method includes validating the computing instances, each being checked as to whether the computing instance corresponds to a respective predetermined state; determining a respective level of reliability for each of the processing units; starting execution of successfully validated computing instances in the processing units, which have a level of reliability that is equal / greater than a predetermined minimum level of reliability; receiving a function request from a user; calling up the function in at least a portion of the executed computing instances corresponding to the function request, in order to determine multiple results; determining a response based on the results; and sending the response to the user.

A method for verifying an application structured to execute on a client device. A challenge request is sent to the application. A candidate challenge answer is received from the application in response to the challenge request, which is then provided as input to a verification computation with a challenge input. Based on an output of the verification computation, it is determined that the candidate challenge answer is generated by providing the challenge input to a challenge computation. Based on the determination that the candidate challenge answer is generated by providing the challenge input to the challenge computation, the application is verified.

The invention is suitable for the technical field of data processing, and particularly relates to a financial data processing and classification method, system and equipment based on verifiable computing, and the method comprises the steps: obtaining on-chain transaction data verified by a block chain consensus node storage and risk-related index data verified by an under-chain oracle machine; the problems that the authenticity of traditional financial data is difficult to verify and the data is split are solved; calling a risk assessment model through an intelligent contract deployed on a block chain, and placing an assessment process in a decentralized block chain network; performing verifiable calculation through an intelligent contract to determine a total performance score and a risk index, and realizing whole-course tracing and verification of a calculation process by utilizing the characteristics of verifiable calculation of a block chain; the total performance score and the risk index are stored in the block chain as non-tampering records, and the risk rating groups are automatically divided according to the fixed rating rule on the chain through the smart contract, so that the authenticity, objectivity, verifiability and fairness of financial data processing are guaranteed.

The embodiment of the invention relates to the technical field of information security, and discloses a task proving method and device, a task execution method, equipment and a medium, and the task proving method comprises the steps: obtaining a task parameter containing an input parameter, a task identifier and a timestamp, and generating a first key and a parameter value based on a hash function and the task parameter; generating a first verification parameter based on the parameter value, the first key and the first value; sending the task identifier, the parameter value and the first key to a server, receiving a calculation result which is sent by the server and corresponds to the task identifier, and if the first verification parameter is smaller than a second numerical value, receiving a difficulty threshold value, a second difficulty value and a first variable value which are sent by the server; a third difficulty value is generated based on the hash function, the parameter value and the first variable value, and if the third difficulty value is equal to the second difficulty value and smaller than the difficulty threshold value, it is proved that the calculation task is executed. An interactive step of hash computation verifies that the compute task is executed. And the accuracy of verifying the execution of the calculation task is improved.

The invention provides a block chain verification service management method and system, a block chain comprises a plurality of block chain nodes, the block chain verification service management system comprises a plurality of shared nodes and a plurality of special nodes, and the shared nodes and the special nodes are used as the block chain nodes; and the shared node stores synchronous data of the block chain. The method comprises the following steps: receiving a user node creation request from a first user, creating at least one special node according to data of a first shared node in a plurality of shared nodes, and using the at least one special node as the user node of the first user; receiving first data from a first user verifying the computing application; second data are generated according to the first data, and the second data comprise the first data, the identifier of the first user and the identifier of the user node; and sending the second data to the plurality of block chain nodes. According to the method and the system, the effectiveness of the user node in the block chain can be remarkably improved.

The embodiment of the invention relates to the technical field of information security, and discloses a task proving method and device, a task execution method and equipment and a storage medium, and the task proving method comprises the steps: segmenting a to-be-executed calculation task into a plurality of sub-tasks; selecting at least one sub-target task from the plurality of sub-tasks based on a task selection rule; based on the at least one sub-target task, generating at least one arithmetic circuit, at least one proof key and at least one verification key corresponding to the at least one sub-target task; sending the to-be-executed calculation task, the at least one proof key and the at least one arithmetic circuit to a second server, and receiving a calculation result and a proof result of the second server; and proving the validity of the calculation task to be executed based on the at least one verification key, the at least one arithmetic circuit, the proving result and the calculation result. According to the task certification method disclosed by the invention, the problem that a lot of time needs to be consumed in the certification generation and verification process is solved, so that the efficiency of verifying the calculation task is relatively high.

The invention relates to distributed ledger technologies such as consensus-based blockchains. Computer-implemented methods for reducing arithmetic circuits derived from smart contracts are described. The invention is implemented using a blockchain network, which may be, for example, a Bitcoin blockchain. A set of conditions encoded in a first programming language is obtained. The set of conditions is converted into a programmatic set of conditions encoded in a second programming language. The programmatic set of conditions is precompiled into precompiled program code. The precompiled program code is transformed into an arithmetic circuit. The arithmetic circuit is reduced to form a reduced arithmetic circuit, and the reduced arithmetic circuit is stored.

An adaptive redundancy-scheduling system for column-oriented neural-network compute arrays is disclosed. The system periodically verifies correctness of compute columns through concurrent redundant-column execution and adjusts test intervals and substitution thresholds in response to observed error statistics. Redundant columns are activated only when measured fault probability exceeds a programmable confidence level, maintaining yield and reliability with minimal performance impact. Verification tasks occur during non-critical compute intervals, and error history is retained for long-term reliability prediction.

The present invention discloses a method, system and device for proving the property rights of a neural network output result. The method comprises: S1, generating a proof π; S2, submitting a proof; S3, adding a record and presenting a proof π; S4, checking uniqueness; S5, verifying the proof π; the system comprises: a proof generation π module, a proof submission module, a record addition and presentation proof π module, a uniqueness checking module and a proof verification π module; by applying the zkCNN protocol originally used in a neural network verifiable computing scenario to a neural network output result property rights certification scenario, while ensuring that the neural network weight parameters are not leaked, a secure, reliable and durable proof π is generated, thereby proving that a certain value or vector is indeed calculated by the neural network.

The application discloses a blockchain-based model verification method and device, equipment and a storage medium. The method comprises the following steps: through an on-chain verification layer, in response to a verification request, a random seed unpredictable before verification and verifiable after verification is obtained; based on the random seed, a plurality of to-be-verified commitments are extracted from a plurality of second cryptographic commitments; a proof generation request corresponding to the to-be-verified commitment is sent to a service provider node; through the service provider node, a reasoning task corresponding to the to-be-verified commitment is executed in a trusted execution environment using a reference model, and reference logic value vectors of each decision step are obtained; based on the distance between the reference logic value vectors and actual logic value vectors of each decision step corresponding to the to-be-verified commitment, tail probability is determined, and a verifiable computation proof is generated; through a verification node, the model verification result is determined based on the tail probability and the verifiable computation proof. Through the embodiment of the application, the reliability of model verification can be improved.

Mechanisms are provided to perform intelligent compliance verification for monitored computing environments. The mechanisms train a first artificial intelligence (AI) computer model to generate rules from natural language content, to thereby generate a first trained AI computer model. The mechanisms train a second AI computer model to verify compliance of a monitored computing environment with rules generated by the first AI computer model, to thereby generate a second trained AI computer model. The mechanisms receive a regulation electronic document that comprises natural language content describing a regulation and determining compliance of a monitored computing environment based on a processing of the regulation electronic document by the first trained AI computer model to generate a rule corresponding to the regulation electronic document, and application of the generated rule by the second trained AI computer model to verify compliance of the monitored computing environment.

An attestation service is configured to receive a request to enable attestation for a compute instance according to an attestation policy indicating one or more baseline health measurement values for validating compute instances. The attestation service provides a network endpoint for the compute instance to request attestation. The attestation service receives, via the network endpoint from a compute instance, one or more health measurement values of the compute instance. The attestation service validates the compute instance based at least on a comparison of the one or more current health measurement values and the one or more baseline health measurement values. The attestation service, in response to validating the compute instance, generates an attestation token indicating that the compute instance is authorized to access a secured resource of the provider network.

A method for verifying an application structured to execute on a client device. A challenge request is sent to the application. A candidate challenge answer is received from the application in response to the challenge request, which is then provided as input to a verification computation with a challenge input. Based on an output of the verification computation, it is determined that the candidate challenge answer is generated by providing the challenge input to a challenge computation. Based on the determination that the candidate challenge answer is generated by providing the challenge input to the challenge computation, the application is verified.

The present application relates to the field of specific computing model and information security technology, aiming at the problems of high computing cost and high cost in existing outsourcing reasoning technology, a polynomial chain verification method and device for outsourcing neural network reasoning are proposed.The method comprises: data input;in the preprocessing stage, the client generates the mask matrix of the input data, the model end generates the mask and the verification auxiliary matrix of the model parameter, the input data, the mask matrix and the Beaver triple are divided into multiple shares and distributed to different computing servers;by constructing a verifiable polynomial chain, the verification amount and the mask are generated under additive secret sharing and returned to the client, and by designing a polynomial form of verification equation, the verification of the reasoning process is completed.The present application completes the reasoning verification by designing a polynomial equation, ensures the integrity of the whole process and the privacy, and realizes the low-overhead, low-cost step-level verification with constant-level online verification calculation time of parallel computing.

A computer-implemented method (600) and system (1) for verifying ownership of a computer software for installation using a distributed hash table (13) and a peer-to-peer distributed ledger (14). This may be the Bitcoin blockchain or an alternative blockchain implementation. The method includes determining (610) a second user public key associated with a second user (24) from a transaction record stored on the peer-to-peer distributed ledger (14). A second public key associated with the second user may be determined (620) from an entry on the distributed hash table (13). The method further includes comparing (630) the second user public key and the second public key, and verifying (640) the ownership of the computer software based on the comparing of the second user public key and the second public key.

The application discloses a privacy data verifiable computing method and system based on zero-knowledge proof, and comprises the following steps: obtaining privacy data, generating pre-processed privacy data and a commitment value; loading a parameter set to generate a parameter commitment value; inputting an extreme random tree algorithm for reasoning to output a gating result; constructing an improved liquid time constant network to obtain a calculation result; constructing a zero-knowledge proof circuit to generate a zero-knowledge proof; and verifying the zero-knowledge proof circuit to confirm the calculation result. Through the extreme random tree gating reasoning and the improved liquid time constant network continuous time reasoning, the application realizes the correctness of the fast independent verification of the calculation result and the tamper resistance.

An embodiment of the present application provides a verification method, apparatus, computer, and computer configuration system. The verification method includes: in response to a computer power-on boot instruction, a processor reads BIOS code and BIOS verification information from a BIOS chip of the computer, where the BIOS code stores a unique identifier of computer hardware of the computer; the processor performs BIOS code security verification based on the BIOS code and the BIOS verification information; if the BIOS code security verification passes, the processor runs the BIOS code to start the work of computer power-on initialization, and the work includes verifying computer hardware; during the process of verifying computer hardware, according to the unique identifier of the computer hardware pre-stored in the BIOS code, it is verified whether the computer hardware of the computer is legal. The embodiment of the present application can, at the computer power-on boot stage, through the two-way verification of BIOS code security verification and computer hardware legality verification, ensure the reliability of computer verification, thereby improving the security of the computer.

A method and a system for dynamically deriving and verifying a measure of a computing environment is presented. The proposed method and system are used to reliably verify measurements of the computing environment. The method includes receiving a dataset recorded by an untrusted source describing elements used to create a computing system operating in a computing environment, receiving attestation evidence generated by a trusted source including an initial measurement value describing the elements of the computing system, deriving a measurement value based on the received dataset, and performing a verification process on a measurement of the computing environment. The verification process is performed by comparing the derived measurement value with the measurement value of the attestation evidence. In response to the comparison of the derived measurement value with the measure value of the attestation evidence being equal, trustworthiness of the computing environment is determined.

The invention discloses a data contract strategy credible execution and traceability system and method, and relates to the technical field of computer information security and cryptography application, and the method comprises the following steps: strategy formalized compiling and circuit generation, key generation and security initialization, verifiable execution and proof generation, and differential privacy watermark generation and injection. Safe destruction and result return; the digital policy trusted execution and traceability system comprises a policy compiling module, a key management module, a verifiable calculation engine, a differential privacy watermark module, a security sandbox and destruction module and an audit traceability module. According to the method, the dependence on a specific hardware architecture is replaced by a cryptographic algorithm and a formalized verification method, so that the side channel attack risk, high cost and compatibility limitation existing in a hardware scheme are effectively overcome, and the deployment and operation and maintenance threshold of a trusted computing environment is remarkably reduced; and a universal trusted infrastructure is provided for a large-scale data circulation scene.

A computer-implemented method and system for verifying the integrity of a computer software for installation using a distributed hash table and a peer-to-peer distributed ledger. This may be the Bitcoin blockchain or an alternative implementation. The method includes determining a metadata associated with a transaction record stored on the peer-to-peer distributed ledger. An indication of an entry stored on the distributed hash table may be determined from the metadata. The method further includes determining a third hash value based on the computer software and determining a fourth hash value from the entry on the distributed hash table. The method further includes comparing the third hash value and the fourth hash value and verifying the integrity of the computer software based on the comparing of the third hash value and the fourth hash value.

Facilitating peer-to-peer cloud computing resource sharing with a licensed distributed ledger is provided. A request of a computing resource consumer for additional computing resources from a computing resource provider via a peer-to-peer decentralized network is detected. In response to verifying that the computing resource consumer is authorized to consume the additional computing resource and the computing resource provider is authorized to transfer the additional computing resource to the computing resource consumer via the peer-to-peer decentralized network, a computing resource exchange environment is generated to transfer the additional computing resource from the computing resource provider to the computing resource consumer. Additional computing resources are transferred from a computing resource provider to a computing resource consumer for consumption via a computing resource exchange environment. Additional computing resources provided by computing resource providers and consumption of the additional computing resources by computing resource consumers are anonymized.

A method includes steps performed by a first computing device provisioned with a unique device secret, UDS, of generating an N chained certificate(s) attesting the authenticity of an N set(s) of device-specific data of the first computing device, and transmitting the N chained certificate(s) to a second computing device. The step of generating the N chained certificate(s) includes a generation process that includes: deriving, using a key ladder, a key from the UDS, generating an authentication tag from a set of device-specific data, using the key, generating a certificate that includes the set of device-specific data and the authentication tag, encrypting the set of device-specific data with the key, and providing the encrypted set of device-specific data as an input to the key ladder for a subsequent generation process.