123 results about "Trusted third party" patented technology

The invention relates to a federated learning system based on multi-key homomorphic encryption and adaptive differential privacy, and belongs to the technical field of privacy computing. According to the system, on the premise that no trusted third party exists, a multi-client collaborative key generation and threshold decryption mechanism is achieved, it is ensured that model parameters are always in an encrypted state in the aggregation process, and leakage of a single node is prevented. By introducing a parameter sensitivity analysis and selective encryption strategy, the system only encrypts high-risk parameters, and the encryption burden is effectively reduced. Meanwhile, in combination with an adaptive privacy budget allocation mechanism, the system dynamically adjusts noise intensity according to a model training state, and model performance and convergence speed are maintained while privacy protection capability is improved. According to the method, high robustness and collusion resistance are realized, stable operation under the condition that part of clients are offline is supported, and the method is suitable for application scenes such as medical treatment and finance with high data sensitivity and strict performance requirements.

The invention discloses a verifiable privacy information retrieval mechanism based on homomorphic encryption. The method comprises the following steps: S1, after initialization is completed, a data owner generates a zero-knowledge proof while constructing a private information retrieval challenge, proves that a challenge structure is legal, and submits the proof to a trusted third party (TTP); s2, the server receives the inquiry verified by the TTP and then performs homomorphic calculation, generates an encrypted response and sends the result to the data owner and the TTP, and the TTP synchronously records response information; s3, the data owner carries out decryption and Hash comparison on a returned result, if an exception is found, an objection request can be submitted, and verification is carried out by the TTP in combination with challenge and response zero-knowledge proof; and S4, arbitration and responsibility tracing: the TTP judges the dispute based on a verification result, and determines a responsibility party. The method is suitable for private information retrieval scenes with high requirements on data security and integrity.

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 model fingerprint intellectual property protection method based on a frequency visual angle, and the method comprises the steps: extracting the frequency domain information of a model weight: S1, extracting a 3D array of a convolution kernel weight of a bottom convolution layer of a model, and obtaining a frequency domain coefficient through 3D discrete cosine transform (3DDCT); s2, the model owner extracts low-intermediate frequency components in the frequency domain coefficient according to a preset threshold value, and orderly connects the low-intermediate frequency components into a feature vector; s3, using a random projection technology to bind the feature vector and the identity information to generate a fingerprint, and submitting the fingerprint to a trusted third party (TTP) for timestamp verification; and S4, performing suspicious model detection: repeating the above steps on the suspicious model to obtain a feature vector, performing calculation by using an identity matrix of a model owner to obtain a fingerprint of the suspicious model, comparing the fingerprint with a fingerprint in a TTP database, and judging whether the suspicious model is pirated or not through cosine similarity. According to the method, the intellectual property protection capability of the model is effectively improved, and relatively high robustness is shown.

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.

A computer-implemented method includes building, with a network security platform, a trusted database having software including software updates and files including file information from trusted third party software providers. The computer-implemented method further includes receiving a software update or file from a third party software provider, determining an identifier for the software update or file, validating the software update or file to determine validity of the software update or file, and determining whether to update the trusted database with the identifier for the software update or file.

The invention relates to the technical field of network security, and discloses a distributed space hiding protection method based on self-certification trust, which comprises the following steps: a requester broadcasts a first request for constructing an anonymous area; the potential collaborator and the requester receiving the first request mutually verify historical trust certificates of each other, and judge whether the interaction is successful or invalid according to a preset interaction verification mechanism; after the interaction result is judged, one of the two interaction parties generates a trust voucher for the interaction of the other party and sends the trust voucher to the other party; and the requester selects a preset number of verified target collaborators to construct the anonymous area. According to the method, through self-certification of trust, the dependence on a trusted third party in an anonymous region construction process is avoided, so that the problems of single-point failure and the like are solved. The trust value of the user can be accurately and efficiently evaluated by combining the consistency of trust evaluation, the time attenuation of the trust value and the similarity of trust evaluation. According to the method, credible anonymous region construction can be realized, and the location privacy of the user is protected.

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.

A multi-server secure aggregation system and method based on homomorphic chameleon hashing utilizes a bulletin board system maintained by a trusted third party to output, download, and collect data. Public-key encryption and secret sharing ensure data security. Homomorphic chameleon hashing is used to verify aggregation and ensure integrity. This creates a non-interactive secure aggregation model that eliminates the need for secure channels between clients and multiple servers. This system boasts a simple, easy-to-implement architecture, is secure and verifiable, and further improves communication efficiency.

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.

A computer-implemented method of encoding a digital contract between first and second parties of a blockchain network, the digital contract being for transferring an amount of a digital asset from the first to the second party based on a condition being fulfilled; the method comprising: obtaining a plurality of data elements, each representing a different contract condition, wherein at least one of the conditions is linked to the second party; generating a hash tree based on the data elements, wherein the hash tree comprises: i) first leaf hashes generated by hashing a respective data element, and second leaf hashes comprising at least one hash key generated by hashing a secret value known only to the trusted third party, ii) internal hashes, and iii) a root hash; and making the root hash available to the first party for inclusion in a transaction of the blockchain.

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 present invention discloses a Bell-state-based authenticated multi-party quantum key negotiation method and system, with m-1 participants, P i (i=1,2,...,m‑1), each of which holds its own n-bit secret string #imgabs0# Each participant has an identity information ID of length l i In order to ensure the legitimacy of the participants’ identities, P i (i = 1, 2, …, N-1) require identity authentication with a semi-trusted third party, P0, and each participant shares #imgabs1# with P0. After executing the following steps, these participants will obtain a negotiated key #imgabs3# of approximately #imgabs2# length. As can be seen, Bell states are leveraged to authenticate user identities with the help of a semi-trusted third party, resisting impersonation attacks. Bell states are used as information carriers and transmitted between participants. Encoding operations are designed using quantum state differentiation to ensure the correctness and security of the proposed protocol, which can resist collusion attacks among participants.