18 results about "Outsourcing computation" patented technology

The invention discloses a private data security sharing method based on federal learning, particularly relates to the field of private data security protection and sharing, and is used for solving the problems of insufficient model credibility and lack of precision control of data exchange in the existing cross-mechanism data collaboration process. According to the method, homomorphic encryption processing is carried out on local data of all participants, a ciphertext verifiable calculation task is constructed in combination with federated learning, forward propagation and back propagation calculation of a ciphertext are carried out by outsourcing calculation nodes, credible verification is carried out on a calculation process by utilizing zero-knowledge proof, and gradient aggregation and model updating are completed in a ciphertext domain; after model training is completed, based on federal learning model output, intelligent judgment is conducted on the sharing value of local data samples, ciphertext re-encryption and data exchange control are driven, multi-party safe and controllable data circulation is achieved, and therefore on the premise that privacy safety and compliance requirements are guaranteed, cross-mechanism data collaboration efficiency and decision-making precision are improved.

The invention discloses a non-interactive digital identity authentication method and system for privacy outsourcing calculation, and the method comprises the steps: a user mobile terminal analyzes an identity certificate of a user into a witness vector and generates a secret share, selects an authentication node, generates and transmits an anchor point secret share, a witness vector secret share and a label for the authentication node, and transmits the anchor point secret share, the witness vector secret share and the label to the authentication node; sending a global key secret share and a local key component secret share for generating a tag to the remaining computing nodes; each computing node in the privacy computing cloud cluster performs witness consistency check, if the check is passed, an identity authentication circuit is executed in a secret state to obtain a secret share of an authentication result, and a proof fragment of a simple non-interactive zero-knowledge proof is calculated based on a calculation track executed by the circuit; the authentication nodes gather to obtain a complete proof and recover an authentication result, the proof is delivered to a verification party, the authentication result is pushed to a user mobile terminal, and in the proof generation process, PIOP consistency check needs to be carried out after each round of polynomial commitment is generated.

The invention relates to a data processing method and device and computer equipment. The method comprises the following steps: receiving fragment data corresponding to score-type to-be-calculated data distributed by all data ends; performing security operation processing on the fragmented data to obtain an encryption operation result; and the encryption operation result is fed back to all the data ends, so that all the data ends generate a comprehensive operation result of the to-be-calculated data according to the encryption operation result fed back by each calculation server, and the comprehensive operation result is decrypted to obtain a target operation result. By adopting the method, any computing server cannot independently restore the original data according to the fragmented data, so that high-precision outsourcing computing of the floating point data can be realized on the premise of not leaking any plaintext information, and the security of the floating point data is improved.

The application provides a distributed security outsourcing computing processing method based on sparse matrix transformation, and relates to the technical field of computers, and the method comprises the following steps: acquiring a first matrix and a second matrix; randomly generating a key set comprising a binary permutation matrix, a sparse triangular matrix and a random vector; performing primary encryption on the first matrix and the second matrix based on the binary permutation matrix; performing secondary encryption on the primary encrypted matrix based on the sparse triangular matrix; distributing the secondary encrypted first secondary encrypted matrix to each edge end in an integrated manner, distributing the second secondary encrypted matrix to each edge end in an even manner after dividing the second secondary encrypted matrix into column vectors according to columns, and enabling the edge end to perform a subtask calculation operation by using the received matrix and return the result to the user end; receiving the calculation results of the edge ends, and aggregating the results; verifying the reliability of the aggregated results by using the random vector, and decrypting the final calculation result. The scheme can improve the security and timeliness of outsourcing computing.

The application discloses a kind of based on lightweight fog computing's Internet of Vehicles data sharing method, including system initialization, vehicle registration, multiple authority attribute organization generates part attribute key car to vehicle, information decryption and outsourcing request;Fog vehicle executes outsourcing task and carries out information part decryption and information complete decryption.The application adopts lightweight CP-ABE algorithm to the information in Internet of Vehicles is encrypted, effectively protects the privacy of vehicle and reduces the computing overhead in communication process.The application can realize the safe privacy protection communication information in Internet of Vehicles, carries out outsourcing calculation by fog car with computing resource, to greatly reduce the computing overhead of vehicle data acquisition, and adopts multiple authority organization access control scheme, prevents the single attribute organization possibly appears single point performance bottleneck, to cause the problem of low system efficiency.

This invention relates to a fine-grained, controllable blockchain rewriting method under cloud computing, featuring a hidden policy for user tracking, revocation, and attribute cancellation. The invention utilizes traceable and revocable ciphertext policy attribute-based encryption as its technical support. Only when the data modifier's attributes satisfy the access policy embedded in the transaction data written by the data owner in the block can the correct hash collision be found, thus modifying the data and achieving fine-grained, controllable, and rewriteable blockchain functionality. Secondly, by employing outsourced computing technology, some complex calculations are transferred to the data service manager under the cloud service provider, significantly improving algorithm efficiency and greatly reducing the burden on on-chain users. Simultaneously, the access policy written by the data owner in the block is partially hidden, making the controllable and rewriteable blockchain more secure and easier to expand in practical applications, effectively protecting privacy.

The invention discloses a verifiable outsourcing computing privacy protection method based on block data truncation, which adopts a three-party architecture of participants, an aggregator and a key generation center, and takes a non-collusion cloud node as a data block distribution and computing carrier. The key generation center constructs a distributed multi-trap-door public key cryptosystem based on security parameters, splits strong private keys and distributes the strong private keys to participants and aggregators. A participant splits original data into mutually exclusive blocks through binary segmented truncation and remainder operation according to the bit number of sensitive data, and distributes the blocks to different cloud nodes. And the cloud nodes complete data block addition, multiplication and equivalence verification by adopting a carry-aware mechanism to ensure that a calculation result is consistent with original data operation. And the aggregator collects a result and performs cross check, and the collaborative participant decrypts the result and then integrates the result to obtain a final weighted aggregation result. According to the method, the problem of unbalance among verifiability, privacy and practicability of the existing outsourcing computing technology can be solved.

The application discloses a privacy data security sharing method based on federated learning, and particularly relates to the field of privacy data security protection and sharing, and is used for solving the problems of insufficient model credibility and lack of precision control in data exchange in the existing cross-institutional data collaboration process; the method performs homomorphic encryption processing on local data of each participant, constructs a ciphertext verifiable calculation task in combination with federated learning, outsources ciphertext forward propagation and back propagation calculation to a computing node for execution, and performs trusted verification on the calculation process by using zero-knowledge proof, and completes gradient aggregation and model updating in the ciphertext domain; after the model training is completed, the sharing value of the local data sample is intelligently judged based on the federated learning model output, ciphertext re-encryption and data exchange control are driven, and safe and controllable data circulation of multiple parties is realized, so that the cross-institutional data collaboration efficiency and decision precision are improved under the premise of guaranteeing privacy security and compliance requirements.

The present disclosure provides a searchable encryption method and device supporting outsourcing computing and multi-user sharing, and relates to the technical field of cloud computing and big data. The method gives a novel outsourcing searchable CP-ABE encryption scheme supporting keyword semantic expansion and sorting, improves the security and availability of the existing cloud storage scheme, reduces the data maintenance cost, and guarantees the customer information security. First, the attribute-based encryption concept is introduced into the searchable encryption scheme, realizing efficient and secure multi-user sharing; second, the present disclosure realizes semantic expansion, which can help users obtain data with different expression methods but the same meaning; third, the present disclosure realizes the result sorting function, returning data according to the relevance with the search keyword from large to small, improving the user experience; finally, the present disclosure realizes the indistinguishability under the adaptive chosen ciphertext attack of the ciphertext and the indistinguishability under the chosen keyword attack of the token.

The application discloses a privacy protection inference method for a binary neural network model under outsourcing computation and belongs to the field of neural network inference under outsourcing computation environment. Firstly, an inference system composed of a model owner, an edge server group and a user group is constructed, the model owner performs RSS, encrypts and distributes BNN model parameters to three edge servers, runs a symbolic function, generates an FSS function key and distributes the FSS function key to each edge server; meanwhile, random numbers are generated to mask multiplication operation and send data. Then, aiming at the BNN model, an activation function is designed based on the FSS and RSS hybrid protocol to optimize nonlinear operation, and a convolution layer and a full connection layer are constructed based on the RSS to provide efficient linear operation. Finally, the user adds the query result of the BNN model locally by using the additive homomorphism property of secret sharing to reconstruct a plaintext inference result. The application completes the lightweight construction of each module in the BNN, improves inference efficiency and realizes the protection of inference data privacy under a limited computing resource environment.

According to the power grid data sharing system and method based on the block chain smart contract, the user key is generated by using the smart contract of the block chain, and dependence on a centralized attribute mechanism is eliminated. And in combination with an encryption technology, the confidentiality of the intelligent power grid data is protected, and flexible data access control is realized. Besides, through online / offline encryption and outsourcing computing technologies, most computing tasks are completed in an offline stage or on a cloud server, so that the computing burden of a data owner and a data access user is greatly reduced. By introducing an access control strategy updating mechanism, a data owner can flexibly modify a key ciphertext stored in a cloud server. According to the method, the power industry can better improve the power data processing efficiency, protect the data privacy of the smart power grid, verify the data integrity and resist serial attack, the identity of a malicious user leaking a private key can be tracked, and the development and progress of the power industry are promoted.

The application discloses a privacy protection training method of a generative adversarial network under outsourcing computing and belongs to the field of privacy calculation; specifically, first, an edge communication scene including a server and a user end is built, then based on the built security protocol, the user end issues split input shares and random noise shares to the server, the server executes a security generation process in cooperation with both parties to calculate the share of the synthesized data; the real data share and the synthesized data share are respectively executed in a security discrimination process to obtain the output share of the discriminator. The server calls the built security protocol to calculate the loss share of the discriminator and the generator; then based on the ASS, the security back propagation is executed, the gradient share of each layer parameter of the neural network is calculated and post-processed, and the parameters of the generator and the discriminator are synchronously updated; finally, the server returns the generated data share to the user end, and the data share is reconstructed into plaintext locally. The application significantly reduces the online communication overhead.

The application discloses an attribute privacy protection method and device based on attribute hiding and matching and a medium. Whether a user attribute is in an access strategy is determined by using a traditional Bloom filter to exclude unnecessary matching operations. In the case that the attribute is in the access strategy, an improved Bloom filter is used to determine the row number of the attribute in a corresponding access matrix. The attribute and corresponding row number information are secret split and saved in the improved Bloom filter, so that complete hiding of the access strategy is realized. A double Bloom filter is constructed during encryption, and a matching attribute set is determined based on the double Bloom filter during decryption, so that whether the attribute set satisfies the access strategy is determined, and outsourcing calculation is performed by using the powerful computing power of a cloud server, so that the burden of devices with limited computing capacity and storage space is reduced.

The present application relates to the technical field of data security, and particularly relates to a verifiable polynomial cloud outsourcing calculation method. The method comprises five processes, which are: a process of constructing a polynomial function in a finite field as an original polynomial function by a user end; a process of constructing and calculating a special polynomial function by the user end; a process of encrypting the original polynomial function into a blind polynomial, and converting polynomial parameters into two sets of encrypted parameters by the user end; a process of calculating by a cloud server using the two sets of encrypted parameters, and returning two calculation results to the user end; and a process of verifying the two calculation results by the user end. The method can confuse the calculation intention, and can ensure that the local user end verifies the correctness of the calculation result at a small calculation cost, thereby breaking through the key technical difficulties of the existing cloud outsourcing calculation technology. The method has important significance for ensuring the stability and effective utilization of the cloud computing platform system.

The invention discloses an anti-supercomputing attack secret state data query and analysis method, which is oriented to an untrusted or semi-trusted outsourcing computing environment, realizes query and analysis of sensitive data on the premise of ensuring that plaintext data is not exposed, and resists supercomputing and potential quantum computing attacks. The method comprises the following steps: carrying out encrypted storage on data based on a short-period or session-level key, and associating a key version (KeyVersion) to realize security governance; carrying out authentication authorization on the query request and generating a query token (Token) with a strategy; compiling a query expression into a secret operator sequence and an execution plan (Plan), and performing secret execution on a cloud side; information leakage in the query process is inhibited through key version rotation, session isolation and access mode fuzzification; and generating verification information (V) for a returned result, performing integrity verification, and performing controlled decryption output in a trusted environment after verification is passed. According to the method, through closed-loop design of key evolution, leakage suppression and result verification, the security, query privacy and result credibility of the system in long-term operation are improved, and the method has good engineering implementability and expandability.

The invention provides a transportation line combination method, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a plurality of transportation lines in a logistics network; combining the plurality of transportation lines to obtain a line set of a first type; the cost of the line set of the first type under the outsourcing calculation strategy is higher than that under the self-operation calculation strategy; the remaining transportation lines are combined to obtain a line set of a second type, and the remaining transportation lines comprise the transportation lines except the line set of the first type in the multiple transportation lines; and respectively outputting the line set of the first type and the line set of the second type. Two operation strategies of self-operation and outsourcing are considered in the whole combination process, so that the cost is reduced to the greatest extent, and the cost is well controlled.

In view of the problems that some existing outsourcing feature extraction methods cannot prevent face information leakage or the face recognition operation after encrypting the face is very time-consuming, a privacy protection outsourcing calculation method using cloud for image feature extraction and feature classification is provided, including the following steps: step one, converting the RGB image to YCbCr color space at the client end to obtain the pixel value matrix of R, B, Cb and Cr color channels; step two, using a one-time pad key to encrypt the matrix in step one, and sending the encrypted matrix to the cloud; step three, the cloud extracts the encrypted matrix in step two, inputs a neural network or an integrated classifier model for training, and obtains a result; and step four, the cloud returns the classification result to the client end.

A computer implementation method for outsourcing computations using blockchain, comprising: obtaining one or more respective secondary computations, each of which is performed using respective inputs based on each part of a primary computation and each verification algorithm, thereby generating the respective results of each part of a primary computation and each verification output of each verification algorithm; obtaining a plurality of respective inputs to a primary computation; generating each outsourcing transaction for each secondary computation, each of which is locked to a respective lock value based on its respective verification output; making each outsourcing transaction available to each computational party; and making both each secondary computation or its encrypted version and each input or its encrypted version available to each computational party.