92 results about "Trusted third party" patented technology

The invention discloses a phase coding-based non-third-party quantum multi-party privacy set intersection method and application, and belongs to the technical field of quantum privacy calculation. An existing quantum private set intersection scheme mostly aims at a two-party scene or depends on a third party to realize confidential intersection, and potential safety hazards are easily caused by constructing a trusted third party in practical application. In order to solve the problem, the invention provides a quantum intersection protocol for multiple participants, and the protocol effectively realizes intersection calculation of private sets of the participants through a quantum state phase encoding technology. And meanwhile, comprehensive correctness and security analysis is performed on the protocol, and the feasibility and security of the protocol are verified through a Qiskit simulation protocol core process. The method not only can efficiently solve the problem of intersection of multi-party private sets and obtain public elements on the premise of protecting the privacy of each party, but also can be used as a basic module in a multi-party cooperative computing application scene, and is wide in application range.

The invention relates to the field of artificial intelligence and federated learning, and discloses a supply chain data security sharing method based on federated learning. According to the method, a decentralized multi-participant collaborative architecture is constructed, local differential privacy disturbance is applied to a local gradient in each round of iteration, and a threshold secret sharing and Paillier homomorphic encryption dual mechanism is adopted to segment, encrypt and transmit a gradient share; and each participant reconstructs a global gradient through linear superposition, and locally completes model updating. The method does not need a trusted third party, effectively prevents leakage of original data and gradient information, gives consideration to model effectiveness and strong privacy protection, and is suitable for a cross-enterprise supply chain intelligent collaboration scene.

The invention discloses a multi-party cooperative charging load safety prediction method and system capable of resisting malicious behaviors, and the method comprises the steps: enabling a trusted third party to generate a global authentication key, a function key and a related random value, which are needed in a load prediction process, at an offline stage in a system initialization stage; the data owner splits the original power data into a plurality of parts and sends the parts to the server; the charging operator decomposes the load prediction model into a plurality of secret shares, and the secret shares are deployed on different server nodes respectively; the plurality of server nodes cooperatively execute a safety function protocol, complete a neural network reasoning process and send a load prediction result share to a power grid dispatching center; and the power grid dispatching center reconstructs the received load prediction result share to obtain a load prediction result. The method can support a plurality of participants, greatly reduces the calculation and communication overhead under the condition of guaranteeing high prediction precision, and can resist malicious behaviors of server nodes.

This document discloses a module and method for establishing application-to-application (app-to-app) mutual trust so that exchange of information and data between applications takes place in a secure manner. This document discloses a secure module that comprises an identification and trusted-storage modules. The identification module is configured to establish a cryptographic bond between an application provided within a standard operating system of a computing device and the secure module. Secure communication may then be established between the application and the secure module. The application may then make use of the cryptographic keys in the secure module to establish secure communications with other applications. The integrity of the application is authenticated based on the cryptographic bond and information contained in the trusted-storage module. The application may then proceed to process an action as an authenticated application bearing a set of trust attributes issued by a trusted third party.

The application relates to the field of artificial intelligence and federal learning, and discloses a supply chain data security sharing method based on federal learning. The method constructs a decentralized multi-participant collaborative architecture, applies local differential privacy disturbance to local gradients in each iteration, and adopts a double mechanism of threshold secret sharing and Paillier homomorphic encryption to split, encrypt and transmit gradient shares; each participant reconstructs the global gradient through linear superposition and completes model updating locally. The application does not require a trusted third party, effectively prevents leakage of original data and gradient information, balances model utility and strong privacy protection, and is suitable for cross-enterprise supply chain intelligent collaboration scenarios.

The invention discloses a light client communication method, system and device based on a consensus committee, and a storage medium, belongs to the technical field of block chains, and solves the technical problem that an existing light client protocol still has obvious limitations in the aspects of storage and communication overhead, security and consensus mechanism compatibility. The invention discloses a light client protocol compatible with various consensus mechanisms and having universality and security, the protocol designs a consensus committee dynamic election mechanism without a trusted third party based on chain quality attributes, and provides a concise and efficient proof generation normal form in combination with weighted multiple signatures. On the premise of not depending on a centralized relay, a light client protocol keeps communication and storage complexity lower than a sub-linear level, and security threats such as Eclipse attacks and the like are effectively defended.

This invention relates to the field of privacy computing and discloses a collaborative analysis method for potential financial risks based on fuzzy privacy set intersection. The method first protects the privacy of deposit and loan data from various financial institutions through Paillier homomorphic encryption. Then, it utilizes a fuzzy privacy set intersection algorithm based on Unintentional Key-Value Storage (OKVS) and the discrete logarithm problem to achieve fuzzy matching of user identities across multiple institutions. While ensuring the privacy of user feature data, it identifies records of the same user that meet a predetermined distance threshold. Finally, it aggregates the encrypted deposit and loan data of the matched records and compares them with the threshold. A trusted third party decrypts the data and completes the judgment of potential financial risks, thus realizing financial privacy and security risk analysis under cross-institutional data collaboration.

The application discloses a kind of multi-party privacy calculation method, device and system based on blockchain, and the method steps include: in task execution phase, task executor receives ciphertext proof sent by trusted third party, ciphertext proof is generated according to the data fingerprint generated by task executor and task publisher generated task ID, and chain is uploaded with task ID and ciphertext proof;In task result verification phase, task executor provides random sampling data to task publisher, to challenge randomly the data used in execution process, verify whether the execution data of task executor contains sampling data, in random challenge, the data fingerprint of the data participating in task by sampling in task publisher is sent to trusted third party with task ID, after obtaining ciphertext proof from blockchain, verify using challenge key to decrypt ciphertext proof.The application has the advantages of simple implementation method, high safety and reliability, verifiable correctness of task execution result, verifiable task work proof and the like.

The invention discloses a CPK-based TCM platform identity credibility construction method, and belongs to the field of information security. According to the method, platform owner authorization data is obtained through processing according to a password input by a platform owner, and the platform owner authorization data is encrypted through a CPK public key and implanted into a trusted cryptographic module; and in the trusted cryptographic module, decrypting the encrypted platform owner authorization data by using a CPK private key to obtain decrypted owner authorization data, and storing the decrypted owner authorization data in the trusted cryptographic module. According to the method, the CPK combined public key system is adopted to identify the identity of the trusted computing password support platform, the CPK password system can be decentralized and does not depend on a trusted third party to provide identity authentication support, a remote center verification link is reduced, local direct verification is adopted, and the verification efficiency is improved. Therefore, the requirements of identification authentication, entity authentication and data secrecy in a super-large-scale information network and a non-information network are met, and a basis is provided for application on the Internet of Things.

The invention provides an anti-collusion low-overhead two-way verification privacy protection federated learning method, which specifically comprises the following steps: S1, a trusted third party initializes system parameters, and a server initializes a global model and distributes the global model to a client; s2, the client uses local data to train a global model and carries out compression encryption, signature and share division on an output calculation result; s3, the server reconstructs the client ciphertext by integrating the share shared by the client and verifies the correctness of the reconstruction result; s4, aggregating the ciphertext by the server; and S5, the client decrypts and decompresses the aggregated ciphertext and verifies an aggregation result. The invention provides an anti-collusion low-overhead two-way verification privacy protection federated learning method. According to the method, while bidirectional verification between the client and the server is realized, a scheme is constructed by using a super-incremental sequence and lightweight cryptographic primitive secret sharing, so that the computing load of the system is effectively reduced, and the tolerance of the system to collusion of the client and the server is improved.

This invention provides a local differential privacy collaborative sampling data collection method and system resistant to poisoning attacks, belonging to the field of information security and privacy protection technology. The scheme compares the single-dimensional probability distribution reported by the user terminal with the median probability distribution to identify and eliminate abnormal reported data, thereby effectively suppressing data poisoning attacks initiated by malicious user terminals. Subsequently, under the constraint of local differential privacy, combined with a generalized random response mechanism and a collaborative sampling strategy, multidimensional data is sampled differentially to achieve more accurate frequency estimation. The scheme of this invention improves the privacy protection strength and the accuracy of statistical results in multidimensional data collection without relying on a trusted third party.

The present invention relates to a method for processing sensitive data, particularly biomedical images, securely, automatically, and reproducibly on a cloud computing infrastructure. The invention also discloses the device for implementing this method. The invention relies in particular on cloud computing, cryptography, biomedical imaging, pseudonymization, anonymization, and advanced signal and image processing technologies. The invention also covers a use case for such a method through the secure implementation of image processing technologies (a business application) applied to biomedical images. In one embodiment, these images are obtained from magnetic resonance imaging (MRI), specifically for applying advanced processing with the business application to map the apparent transverse relaxation rate (R2*) and perform quantitative susceptibility imaging (QSM).Figure for the abbreviation: figure 1.

The application relates to a communication establishment method based on security evaluation and an electronic device, and belongs to the technical field of communication security. The method comprises the following steps: obtaining a first system running state measurement value of a first device; after certificate authentication of the first device and a second device is passed, the first device requests a trusted third party to authenticate the first system running state according to the first system running state measurement value; if the second system running state of the second device and the first system running state of the first device are both authenticated, communication between the first device and the second device is established. The communication establishment method based on security evaluation and the electronic device provided by the application significantly improve the active defense capability of communication, improve the reliability of security evaluation, and realize communication security evaluation in a TPM-free environment.

The invention discloses a hierarchical weighted electronic voting method and system based on secure multi-party computing, and the method comprises the steps: system initialization, identity authentication and vote submission, independent execution of dynamic truncation of privacy set intersection in each region, independent execution of ciphertext weighted vote counting in each region, and execution of hierarchical secondary aggregation and decryption and publishing by an aggregation server. Dependence on a trusted third party is avoided through secure multi-party calculation and a homomorphic encryption technology, hierarchical vote counting is realized through introduction of ciphertext domain hierarchical weighted aggregation, ciphertext matching attack is avoided through recombination of vote information, and privacy and security of electronic vote and adaptability to large-scale vote scenes are realized.

The application discloses a periodic non-repudiation ring signature method and system based on an SM9 national encryption algorithm, relates to the technical field of ring signature and information security, and comprises the following steps: S1, initializing public parameters and a master key pair; S2, generating a signer private key according to the identity of a signer; S3, the signer selects ring members participating in signature to form a ring group, the ring group signs a message according to the signer private key, obtains a ring signature, and sends the message and the ring signature to a verifier; S4, the verifier verifies the ring signature, and if the verification is passed, the process proceeds to S5, otherwise, the signature is stopped; S5, the verifier sends a verification message to ring members in the ring group to confirm the signer. The application introduces a time period, realizes periodic non-repudiation ring signature, modifies a confirmation and denial algorithm, and ensures that the scheme can not only complete identity privacy protection, but also track a real signer without the participation of a trusted third party.

An apparatus for generating metadata, an apparatus for checking metadata, and an apparatus for storing metadata are disclosed. An apparatus is provided that includes interface circuitry, machine-readable instructions, and processor circuitry to execute the machine-readable instructions. The machine-readable instructions include instructions for obtaining data from a first party. The data is configured for training of a machine learning model of a second party. The machine-readable instructions further include instructions to generate metadata corresponding to the data, the metadata including an identifier of the data. The machine-readable instructions further include instructions for issuing data appended with corresponding metadata. The machine-readable instructions further include instructions to communicate metadata for storage to a trusted third party.

The invention discloses an anti-quantum function commitment method based on a chain function commitment, and the method specifically comprises the steps: carrying out the system initialization, generating a system public key parameter through a trusted third party according to a function scale parameter and a safety parameter, and distributing the system public key parameter to a commitment end and a receiving end; the commitment end generates a commitment value for the function input value based on the public key parameter and sends the commitment value to the receiving end; the commitment end represents an objective function as a directed acyclic graph and splits the directed acyclic graph into a plurality of multivariate quadratic polynomials, and the objective function comprises a function capable of being represented as a finite-scale arithmetic circuit; on the basis of the function input value and the split multivariate quadratic polynomial, the commitment end generates an output proof that the target function takes the input value as the input; and the receiving end verifies the validity of the function value and the corresponding commitment information by using the public key parameter and the proof, if the verification is passed, the commitment value and the function value are valid, otherwise, the commitment value and the function value are invalid. According to the scheme of the invention, the quantum collapse attack can be effectively prevented, and the node storage and synchronization overhead can be greatly reduced.

The present invention relates to a method for processing sensitive data, particularly biomedical images, securely, automatically, and reproducibly on a cloud computing infrastructure. The invention also discloses the device for implementing this method. The invention relies in particular on cloud computing, cryptography, biomedical imaging, pseudonymization, anonymization, and advanced signal and image processing technologies. The invention also covers a use case for such a method through the secure implementation of image processing technologies (a business application) applied to biomedical images. In one embodiment, these images are obtained from magnetic resonance imaging (MRI), specifically for applying advanced processing with the business application to map the apparent transverse relaxation rate (R2*) and perform quantitative susceptibility imaging (QSM).Figure for the abbreviation: figure 1.

In some implementations, a secure setup is performed by a trusted third party to generate, based on an obfuscated watermark extractor, a prover key and a verifier key to prove ownership of a first machine learning model by a model owner without divulging a trigger key or a watermark corresponding to the first machine learning model. Next, the model owner of the first machine learning model generates, based on the prover key, a proof of ownership of a second machine learning model by at least determining that the obfuscated watermark extractor extracts the watermark from the second machine learning model in response to the second machine learning model being provided as a public input to the obfuscated watermark extractor. Then, the proof of ownership is verified, based on the verifier key, to acknowledge that the second machine learning model matches the first machine learning model.

The application discloses an identity authentication mode of a consortium chain in a strong permission control mode based on identification, calculates a user public key from a user identification and a set of public mathematical parameters by using an elliptic curve bilinear pair theory, the user public key comprises a user identification and a version number of the user on the consortium chain, wherein the user identification is used for identity authentication, and the version number is used for key update; and a user private key corresponding to the user public key is calculated from the user identification, the set of public mathematical parameters and a secret value in a domain range, and is uniformly generated by a key generation center and downloaded to the user. The application adopts the above identity authentication mode, does not need the participation of a trusted third party, effectively resists attacks of intermediaries, does not need to bind identity information and a public key by using a digital certificate, reduces the bandwidth occupied by the transmission of a certificate in an identity authentication process, improves identity authentication efficiency, and solves the problem of IBC key update.

The invention belongs to the technical field of remote monitoring, and discloses a remote monitoring method and system for a high-voltage equipment area. The method comprises the following steps: constructing a data analysis model and a transmission path planning model based on a cloud data center, and constructing a data transmission network based on all edge computing gateways; based on a trusted third party, performing key generation and real name authentication on all edge computing gateways; based on the current edge computing gateway, real-time monitoring data of the corresponding high-voltage equipment area is collected, encrypted and signed; acquiring an optimal real-time data transmission path, and uploading the encrypted real-time monitoring data and signature data to a cloud data center; and carrying out signature authentication and decryption based on the cloud data center, and carrying out data analysis by using the data analysis model to obtain a corresponding real-time data analysis result. According to the invention, the problems of low practicability, poor accuracy, poor transmission stability, poor data security and poor reliability in the prior art are solved.

The application discloses a blockchain-oriented atomic exchange method and system and belongs to the technical field of blockchains. The method comprises the following steps: confirming an exchange amount and a timeout period together with an asset holder; selecting a reference message, generating a commitment value of a secret value, a ciphertext, and zero-knowledge proof that the ciphertext and the commitment value correspond to the secret value, and then sending the reference message and the commitment value to the asset holder; deploying a functional contract by the asset holder based on the exchange amount, the timeout period, the reference message and the commitment value; initiating an asset extraction transaction, signing the reference message by a verifier committee; and combining the timeout period, the signature result of the reference message and the functional contract to execute the exchange of the secret value and the property. The application can solve the bribery attack problem without introducing any additional trusted third party or any other security assumption.

The application relates to the technical field of blockchains, and particularly discloses a digital file storage and verification method and system, a storage medium and a terminal device. The method comprises the following steps: generating a corresponding hash value of a digital asset source file in a local area through a hash algorithm; constructing a UTXO, writing the hash value into the UTXO, and then sending the UTXO to a full node for packaging; collecting information of a block header where the UTXO is located after block output and a Merkle proof; locally storing the information of the block header, the Merkle proof, the digital asset source file and the UTXO; sending the information of the block header, the Merkle proof, the digital asset source file and the UTXO to a verification party; and verifying the digital asset source file by the verification party. The application can store the file without the intervention of a trusted third party, so as to ensure that the stored file cannot be maliciously tampered with, guarantee the effectiveness of the stored file, and be suitable for a blockchain system based on all UTXOs.

The invention belongs to the field of quantum security multi-party computing, and discloses a verifiable quantum security multi-party XOR protocol based on super-dense coding. According to the preprocessing stage, for n participants Pj and two classical bits held by each participant, P0 obtains specific aggregation information of the classical bits of each participant through a super-dense coding means under the condition that only n quantum bits are transmitted, and meanwhile it is ensured that P0 cannot obtain the part, which is not authorized to check, of the bits held by each participant, so that the P0 is not allowed to check the part of the classical bits of each participant; through the method, the communication overhead in multi-party computing is greatly reduced. In the calculation stage, real input is hidden by using the secret key and the auxiliary value generated in the preprocessing stage, and exclusive-or operation is carried out under the condition that privacy input is not leaked. The protocol does not need the participation of a trusted third party, the trust dependence is eliminated, and decentralized security is realized; quantum state input is supported, and future quantum computing threats are defended; whether someone maliciously tampers the calculated value or not in the exclusive OR seeking process can be verified, and the method is suitable for sensitive scenes such as privacy calculation and conjoint analysis.

The application provides a data packet security verification method in an information center network, comprising the following steps: 1) a legal content publisher registers a password parameter to a trusted third party, and signs a data packet; 2) an access router directly connected to the content publisher periodically updates the reputation of the content publisher according to a verification result of the data packet; 3) the access router performs batch verification on the data packet and probabilistically discards the data packet; and 4) an intermediate router verifies each data packet that needs to be verified one by one according to a verification probability set by the access router for each data packet, and discards the data packet verified as false. The application is used for preventing content pollution attacks in the information center network, can verify the authenticity and integrity of the content with low cost, and avoids false content from entering the network.

To provide a computer-implemented method for granting payment by a targeter to a verifier.SOLUTION: The method includes, by a verifier: performing payment verification to verify a funding source of a target; and performing identity verification to verify an identity of the target. The identity verification comprises: accessing response data stored in a data store in association with the identity of the target, said data store being implemented at a third party computing facility of a trusted third party or on a peer-to-peer public medium, the response data comprising either: a) a stored instance of the response to the challenge; or b) an attestation comprising a transformation of the response. Payment is granted on condition that the outputs of both payment verification and identity verification are true.SELECTED DRAWING: Figure 11

A first party quantum trusted executed environment (QTEE) receives a first party initial key from a first party and generates an expanded key; a second party QTEE receives a second party initial key from a second party and generates an expanded key; an untrusted third party controls an untrusted quantum source to distribute an input quantum system to the QTEEs; the first party QTEE encodes the input quantum system into a first quantum system and sends the first quantum system to the untrusted third party; the second party QTEE encodes the input quantum system into a second quantum system and sends the second quantum system to the untrusted third party; the untrusted third party performs an entangling measurement on the quantum systems resulting an entangling measurement outcome and sends to the parties; and the parties generate secret keys using the expanded keys and the entangling measurement outcome.

The application discloses a non-sampling factor decomposition machine service method under a longitudinal federal learning architecture, belongs to the field of recommendation systems, and initializes local models of both parties participating in longitudinal federal learning, calculates respective auxiliary vectors through the initialized local models, and a trusted third party sends a homomorphic encryption public key to both parties participating in longitudinal federal learning, wherein one party transmits an encrypted model and the auxiliary vector of the one party to the other party to obtain a predicted value of an encrypted longitudinal federal factor decomposition machine algorithm at the other party, and the predicted value is accepted; after loss functions are calculated through local data and the received data, respective encrypted gradients are solved, and the encrypted gradients are sent to the trusted third party for decryption; after the model gradients are received by both parties, the models are updated, and the converged models are used in a recommendation task. Advantages of the application are that the technical problem of poor training efficiency of a recommendation framework model of an implicit feedback factor decomposition machine under a longitudinal federal learning architecture in a non-sampling training condition is solved.

This invention discloses a hidden query method based on a trusted third party. This method, building upon a hidden query algorithm based on row index retrieval using homomorphic encryption, introduces a trusted third-party service. This third party will not disclose any client's query content. This invention also provides a hidden query system, apparatus, and storage medium. The method provided by this invention constructs a keyword index file with a specific structure from keywords and row indexes and transmits it to a trusted third party. The data querying party obtains the row index through the keyword retrieval service provided by the trusted third party, and then reuses the row index hidden query technology to query the data provider, thereby achieving keyword hidden query.

The application provides a verifiable federated learning method and device based on double decomposition and aggregation, and belongs to the field of privacy security protection of federated learning. Specifically, a trusted third party (TTP) performs secret key negotiation with each client to generate corresponding secret keys. The TTP generates random numbers through a random number generator and sums them to obtain TTP. The TTP sends the secret keys to each client through a symmetric encryption method. Each client sends the trained model parameters to other clients in n parts and accepts the parameters sent from other clients. Each client performs a second round of decomposition on the parameters, and the parameters are decomposed into a public part (sent to the server) and a private part (retained locally). The private part is constructed based on the uploaded local model parameters for each client to construct a verification function v i , and sends it to the server together. The server aggregates the model parameters ω i and the verification function v i sent by the client, and sends the aggregation result to the client. The client verifies whether the data is equal through the verification formula. If they are equal, subsequent update operations are performed. The received global model parameters with bias are aggregated with the private part retained locally to recover the real parameter model.