35 results about "Outsourcing computation" patented technology

The invention relates to the fields of cryptographic algorithms and homomorphic encryption, in particular to an anti-quantum-attack homomorphic encryption method. Along with increasing demands of outsourcing computing and privacy protection application in a multi-user scene, multi-key homomorphic encryption becomes an important research direction. The construction of a traditional multi-key homomorphic encryption scheme generally depends on a common random character string (CRS) model, and a trusted party needs to generate a common parameter for a public key so as to support calculation on a joint ciphertext. Due to the dependence on the central trusted mechanism, the anti-attack capability and the decentralization characteristic of the system are greatly reduced. In order to avoid excessive dependence on trusted third parties and public parameters, a distributed key generation protocol is used in the invention, all users are enabled to independently generate keys thereof, and ciphertexts of a plurality of users are expanded to the same dimension through a ciphertext expansion technology, so that the encryption efficiency is improved. Therefore, multi-key homomorphic encryption in a multi-user scene is realized without depending on a trusted third party, a lattice-based CRS-free multi-key homomorphic encryption method is provided, and the security and decentration characteristic of the scheme are enhanced. Furthermore, the invention provides a directional decryption mechanism and introduces the directional decryption mechanism into the scheme, the public key information of the target user is embedded into the ciphertext, it is ensured that only the specific target user can decrypt the ciphertext after homomorphic operation, and more flexible privacy protection is provided.

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 an attribute-based encryption method and system based on support of multi-authority accountability and outsourcing calculation, and the method specifically comprises the steps: introducing a multi-attribute authority mechanism to be responsible for key distribution and management, reducing the dependence on a single mechanism, and improving the safety and reliability of the system; the cloud server locally updates the access strategy and the ciphertext according to the minimum change increment set uploaded by the data owner; a white-box tracking technology is adopted to bind a user identity with an attribute key, so that a system can locate a specific identity of a malicious user and a specific identity of a malicious attribute authority through a leaked key; intensive computing tasks in the encryption and decryption processes are outsourced to a cloud server, and meanwhile it is ensured that user privacy is not leaked in the outsourcing process; for the untrusted problem of the cloud server, the calculation result is verified by using the smart contract in the block chain, and it is ensured that the verification process is open and transparent and the result is trusted; and an attribute version mark is introduced, so that efficient revocation of old version attributes is realized, and the safety is further 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 the field of data sharing, and particularly discloses an access control method based on outsourcing calculation and attribute-based searchable encryption on a block chain, comprising the following steps: S1, an attribute authority initializes to generate a system public key and a master key, and generates an attribute private key of a user; s2, the data owner formulates an access control strategy to assist the outsourcing encryption service provider in partial encryption, and the data user and the outsourcing encryption service provider encrypt the keyword, the data ciphertext storage address and the symmetric key and upload the encrypted ciphertext to the block chain for storage; s3, the data user generates a search trap door and sends the search trap door to the block chain, and user attributes are verified; and S4, the data user assists the outsourcing decryption service provider in partially decrypting the ciphertext, locally decrypting to obtain the data storage address and the symmetric key, downloading the data ciphertext, and decrypting by using the symmetric key to obtain the data plaintext. According to the invention, the calculation burden of encryption and decryption of a local user is reduced, and the sharing efficiency and security of data are improved.

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 invention discloses a fair and verifiable intelligent contract matrix outsourcing calculation method. The method comprises the steps that a user inputs initial data; generating a plurality of auxiliary blinding matrixes according to the initial data; according to the auxiliary blinding matrix, constructing a plurality of blinding matrixes, and determining contents needing to be calculated by a cloud computing service provider; inputting a smart contract, applying for a calculation request from the smart contract by a cloud calculation service provider, and performing calculation according to the blind matrix to obtain a matrix result; the matrix result is returned to the smart contract; the verifier performs blind removal processing on the matrix result to obtain a blind removal result; comparing the blind removing result to judge whether the matrix result is correct or not; if the matrix result is correct, the smart contract sends the matrix result to the user and awards the cloud computing service provider; if the matrix result is incorrect, determining that the matrix result is invalid; according to the method, the calculation difficulty of verification is remarkably reduced, and the verifier difficulty of a block chain worker is avoided.

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 present invention relates to a method for additive homomorphic encryption and cryptographic accumulator calculation. The present invention proposes a verifiable outsourced calculation method for graph data based on additive homomorphism and bilinear map accumulators. First, homomorphic cryptography is used to encrypt graph data instantiated by an adjacency list. Additive homomorphic encryption and obfuscation circuits are combined to implement a breadth-first Dijkstra algorithm based on a priority queue to calculate the exact shortest path on the encrypted graph data. Second, a binomial heap structure is constructed based on the encrypted graph data, and a bilinear map accumulator is used to calculate verification evidence for the encrypted shortest path result, thereby achieving verifiable correctness of the outsourced shortest path query calculation result for the encrypted graph data. In addition, the scheme also uses certificateless short signatures to ensure the authenticity and non-tamperability of the shortest path query result and verification result of the cloud server, thereby achieving probabilistic correctness verification of the outsourced calculation result of the shortest path query for encrypted graph data.

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.

The embodiment of the invention provides an outsourcing decryption data access control management method and device based on a block chain. In the embodiment of the invention, a user side applies for an attribute secret key from a management side; when the user attribute meets a preset access strategy, obtaining a corresponding attribute ciphertext and a target address from the block chain, and obtaining encrypted data according to the target address; sending a conversion secret key in the attribute secret key and the attribute ciphertext to an outsourcing computing center so as to partially decrypt the attribute ciphertext according to the conversion secret key to obtain a conversion ciphertext, and returning the conversion ciphertext to the user side; and the user side decrypts the converted ciphertext based on the attribute secret key to obtain the secret key of the symmetric encryption algorithm, and decrypts the encrypted data according to the secret key of the symmetric encryption algorithm. Therefore, the problem of large consumption of attribute encryption computing resources can be reduced through the outsourcing computing center, and since the user completes the final decryption operation, the computing center does not need to be trusted, and the information of data plaintext cannot be leaked by a part of decryption results.

The invention relates to a supply chain data sharing system based on a block chain and multiple cryptography technologies. Comprising a block chain storage layer, a cryptography function layer, a business logic layer and a user interaction layer. The block chain storage layer is responsible for distributed storage and tracing of data; the cryptographic function layer integrates three cryptographic technologies of distributed attribute-based encryption, zero-knowledge proof and verifiable credentials, and optimizes the functional characteristics of DABE, so that the DABE supports the characteristics of outsourcing calculation, data layering, key responsibility and the like, and realizes data access control, privacy protection and identity authentication; the business logic layer processes a business process related to the supply chain; the user interaction layer adopts a front-end and back-end separation architecture to provide a friendly operation interface. The method is mainly characterized in that through cooperation of multiple cryptography technologies, access control, data privacy protection and identity authentication with finer granularity are achieved while data sharing convenience is guaranteed, full-cycle protection of data circulation is achieved, and the safety and availability of supply chain data sharing are improved.

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.

The present invention relates to the field of data sharing and specifically discloses an access control method based on outsourced computing and attribute-based searchable encryption on a blockchain. The method comprises the following steps: S1: an attribute authority initializes and generates a system public key and a master key, and generates a user's attribute private key; S2: a data owner formulates an access control policy and assists an outsourced encryption service provider in partial encryption. The data user and the outsourced encryption service provider encrypt keywords, data ciphertext storage addresses, and symmetric keys, and upload the encrypted ciphertext to the blockchain for storage; S3: the data user generates a search trapdoor and sends it to the blockchain to verify the user's attributes; S4: the data user assists an outsourced decryption service provider in partially decrypting the ciphertext, locally decrypting to obtain the data storage address and symmetric key, downloading the data ciphertext, and decrypting it using the symmetric key to obtain the data plaintext. The present invention reduces the computational burden of encryption and decryption for local users and improves the efficiency and security of data sharing.

The invention discloses a privacy protection reasoning method of a binary neural network model under outsourcing computing, and belongs to the field of neural network reasoning under an outsourcing computing environment. The method comprises the steps that firstly, an inference system composed of a model owner, an edge server group and a user is built, the model owner executes RSS, BNN model parameters are encrypted and distributed to three edge servers, sign functions are operated, FSS function keys are generated, and the FSS function keys are distributed to all the edge servers; and simultaneously generating a random number mask for multiplication and data transmission. And then, aiming at the BNN model, designing an activation function optimization nonlinear operation based on an FSS and RSS hybrid protocol, constructing a convolutional layer based on RSS, and providing efficient linear operation by a full connection layer. And finally, the user adds and reconstructs the query result of the BNN model locally by using the addition homomorphic property of secret sharing to obtain a plaintext reasoning result. According to the method, lightweight construction of each module in the BNN is completed, the reasoning efficiency is improved, and reasoning data privacy is protected in a computing resource limited environment.

The invention discloses a single cloud architecture efficient and secure outsourcing computing protocol design based on a TEE technology, and relates to the field of secure outsourcing computing. The system architecture only comprises two main bodies, namely a client and a cloud service provider (CSP). A server is arranged in the CSP, and code execution is divided into two areas, namely a rich execution environment (REE) and a trusted execution environment (TEE). The REE and the TEE are innovatively used as independent participants, cooperative calculation is realized through a threshold Paillier encryption system, so that an efficient and safe multivariate operation protocol is designed, and specifically, the efficient and safe multivariate operation protocol comprises unary operation (a safe absolute value protocol SABS), binary operation (a safe multiplication protocol SMUL, a safe comparison protocol SCMP and a safe judgment equality protocol SEQL) and ternary operation (a safe decision protocol SDEC). According to the method, the problems of low efficiency of the existing security outsourcing computing method, difficulty in application of the TEE due to limitation of storage and computing resources, and security problems of excessive dependence on hypotheses of non-collusion among multiple servers and absolute security black box of the TEE are effectively solved.

The invention discloses a single cloud architecture intra-field security data transaction method based on a TEE technology, and relates to the field of security outsourcing computing. The method only relates to three main bodies of a data seller, a data buyer and a cloud proxy server, and a completely trusted third party does not need to be introduced. According to the method, a data protection service is innovatively converted into a data security processing service, in the method, a data seller does not directly sell source data, but provides the right to use the data, and a data buyer only needs to pay for a data analysis service. Specifically, the method allows a data seller to encrypt a data set, and uploads the data to a cloud in a ciphertext form for sale. The cloud proxy server serves as a provider of the data transaction platform, a server equipped with the TEE technology is deployed in the cloud proxy server, and based on a (2-2) threshold Paillier encryption system, safe data analysis service is provided for data buyers, so that safe hosting of data is realized. The designed intra-field security data transaction process comprises five stages of identity authentication between participants, system initialization, transaction data outsourcing, security model training and result returning. In addition, based on the architecture, the invention further provides a detailed ciphertext linear regression training transaction example. According to the invention, the double challenges of difficult original data copy prevention and difficult re-transaction prevention in a traditional data transaction scheme are effectively solved, and finally the data security of both the data seller and the data buyer is protected in an efficient, safe and low-cost manner.

The present invention relates to an attribute-based outsourced decryption and its verification and auditing method based on a blockchain payment mechanism. By combining blockchain payment technology with attribute-based encryption, the functionality of attribute-based encryption is extended. The computing burden of lightweight devices with limited computing resources is reduced by adding outsourced computing. The correctness of the outsourced result is ensured by adding a verification function. The computing burden of lightweight devices with limited computing resources is further reduced by adding batch verification. The normal operation of the outsourced function is ensured by adding a payment function. The present invention conducts a public audit of the decryption result to prevent the cloud service center from obtaining payment rewards by forging the decryption result, and at the same time prevent the end user from refusing to recognize its correctness and evading payment rewards after obtaining the decryption result, so as to ensure the fairness of the transaction between the two parties.

The present invention provides a blockchain-based outsourced trusted computing method, system, and storage medium, relating to the field of outsourced computing technology. The method includes: constructing an outsourced trusted computing system, wherein the outsourced trusted computing system includes a computing power resource pool, a computing service platform, and a verification platform; sharding the computing task to obtain multiple sharding tasks, generating multiple random numbers, and performing computational redundancy analysis; randomly and redundantly distributing the multiple sharding tasks to multiple outsourced computing nodes based on computational redundancy and multiple random numbers, and performing two-stage delayed verification; after passing the test, calculating the multiple sharding tasks according to the preset calculation rules of the computing task, obtaining multiple sharding calculation results, uploading them to the verification platform for privacy verification according to the preset verification contract, and generating multiple reward and punishment results. The present invention solves the technical problem of low credibility of outsourced computing in the prior art.

The present invention relates to the field of machine learning privacy computing technology, and more specifically, to a method for secure outsourcing verification of floating-point matrix multiplication based on TEE and GPU. ′ ,Y ′ and Z ′ In this way, whether it is TEE or GPU, in the same calculation order, Z ′ Each element in must be consistent with X ′ Y ′ The product of the two elements is identical. Compared with existing solutions, this approach avoids verification errors caused by computer precision and the problem of GPUs producing erroneous results within a certain range due to tolerance that cannot be verified by the TEE. It also solves the problem of correctness verification for outsourced floating-point matrix multiplication.

The invention relates to the field of medical data privacy protection, in particular to a medical data security outsourcing calculation method based on a block chain and an application system.The method comprises the steps that firstly, system initialization is conducted, a problem provider PP encrypts an LP problem and sends the LP problem to a cloud service provider CSP, meanwhile, the PP generates a problem summary transaction and anchors the problem summary transaction to the block chain, and the problem summary transaction is sent to the block chain; the CSP confirms the integrity of the encrypted LP problem and generates a advance payment compensation transaction, the PP prepays service charges for the CSP by generating a service charge transaction, the CSP solves the encrypted LP problem to obtain an optimization result and sends the optimization result to the PP, the CSP generates an optimization result abstract transaction and anchors the optimization result abstract transaction to a block chain, the PP and a verifier set RV execute double-layer verification on the result, and the PP completes the decryption step according to the verification result. According to the invention, a double-layer verification mechanism and a fair payment protocol are realized based on a block chain technology, and a safe, credible and efficient outsourcing computing system is provided for medical equipment to solve an LP problem under large-scale data.

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 present invention discloses a fair and verifiable smart contract matrix outsourcing calculation method, comprising: a user inputting initial data; generating a plurality of auxiliary blinding matrices based on the initial data; constructing a plurality of blinding matrices based on the auxiliary blinding matrices, and determining the content that needs to be calculated by a cloud computing service provider; inputting a smart contract, and the cloud computing service provider applying for a calculation request to the smart contract, obtaining a matrix result based on the blinding matrix calculation; and returning the matrix result to the smart contract; a verifier performing unblinding processing on the matrix result to obtain an unblinding result; comparing the unblinding result to determine whether the matrix result is correct; if the matrix result is correct, the smart contract sends the matrix result to the user and rewards the cloud computing service provider; if the matrix result is incorrect, the matrix result is deemed invalid. The present invention significantly reduces the computational difficulty of verification and avoids the verifier dilemma of blockchain workers.

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.