152 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 embodiment of the invention discloses a database integrity verification method, device and system, a medium and equipment, and the method comprises the steps: firstly, injecting a section enhancement code at a preset tangency point position of a database; then, when a modification operation for the database is received, executing the section enhancement code to generate a verification hash value corresponding to the modification operation; and further, calling a password service of a trusted third party, and performing data signature on the target data corresponding to the modification operation by the verification hash value through the password service. And finally, verifying the database data based on a hash operation message authentication code obtained by verifying the hash value and the data signature so as to determine whether the database is tampered or not.

The invention provides an image retrieval method and system supporting sharing of multiple data sources, and relates to the technical field of image retrieval, the method comprises the steps that secret keys are generated and distributed for retrieval members through a trusted third party, a data owner constructs a ciphertext cluster and a ciphertext image set based on a public key in an index secret key pair, an image secret key and an image set, and the ciphertext cluster and the ciphertext image set are stored; uploading to a first cloud server; the cloud server group re-encrypts the ciphertext cluster and constructs a hierarchical index; the query user processes the query image by using a public key in the retrieval key pair to obtain a query trap door, and uploads the query trap door to the first cloud server; the cloud server group re-encrypts the query trap door to obtain a new query trap door, and searches the hierarchical index based on the new query trap door to obtain a search result; and the query user obtains the image under the plaintext based on the search result. According to the method, the security of privacy information during retrieval is ensured, the complete secret culture of calculation in the retrieval process is realized, and the scene of multi-data source sharing is supported.

The invention belongs to the field of passwords, and discloses an anti-quantum threshold digital signature method and system capable of tolerating high network delay. The method comprises the following steps: in a secret key generation stage, a trusted third party randomly selects a secret key and performs Shamir secret sharing on the secret key, and distributes a secret sharing value to each signer; in a distributed signature stage, each signer carries out packaging and secret sharing on a secret sharing value, a proving sub-circuit of a public key is operated based on a secure multi-party computing protocol, and each participant outputs a signature through two rounds of interaction. The anti-quantum threshold digital signature scheme has function interchangeability, that is, the signature generated by the threshold scheme and the signature generated by the original signature scheme can be verified by the same verification algorithm; meanwhile, the threshold digital signature scheme is efficient and achievable, and a threshold signature protocol in a scene of two parties of a wide area network only needs 2 seconds; moreover, the method has a better number of rounds, and the signature algorithm only needs two rounds.

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.

A system includes one or more hardware processors, and at least one memory storing instructions that cause the one or more hardware processors to perform operations comprising receiving, from an online platform system, an advertisement opportunity data comprising a plurality of advert opportunities, and a plurality of opportunity timestamps. The operations additionally include receiving, from an advertiser system, advertisement event data comprising a plurality of events and a plurality of event timestamps, and receiving an attribute result of performing a first two-party computation between the advertiser system and the platform system to attribute the plurality of events to the plurality of advert opportunities based on comparing opportunity timestamps and event timestamps, The operations further include deriving an aggregation result that aggregates the events for a subcut of users based on performing a homomorphic encryption, or on performing a non-trusted third party computation.

The invention provides a distributed function secret sharing key generation method aiming at the problems that an existing secret sharing key generation scheme depends on a trusted third party and does not support limitation of arithmetic sharing operation. The invention provides a solution for efficient distributed key generation based on improved function secret sharing between two communication parties. The protocol designed by the invention supports arithmetic sharing input and output, the calculation overhead and the communication overhead in the distributed key generation process are greatly reduced by optimizing the communication process on the premise of ensuring the safety and the accuracy of calculation, and the requirement on a trusted third party is completely removed by designing an efficient two-party calculation 2PC protocol, so that the communication efficiency is improved. A participant is allowed to directly generate keys for a distributed point function (DPF) and a distributed comparison function (DCF) through a two-party calculation protocol, the security and practicability of key generation are remarkably improved, and the method is particularly suitable for a scene without a trusted third party.

A method for compliance with Know Your Customer (KYC) and other regulations includes a pseudonymous globally unique identifier stored on a blockchain that associates a pseudonymous first party address with a globally unique identifier representing the vetted identity of the owner of the address. The method also includes a trusted third party issuing a verifiable credential for a first pseudonymous party to a proposed transaction to a second pseudonymous party to the transaction.

The invention relates to the technical field of communication, in particular to a task allocation and scheduling method based on multiple rounds of auction, which constructs an auction framework for a scene in which terminal equipment simultaneously processes multiple tasks in parallel and based on a D2D collaborative MEC architecture, and takes a busy terminal as a buyer, an idle terminal as a seller and a trusted third party as an auctioneer. Edge collaborative computing unloading based on an incentive mechanism and task scheduling are combined to improve system utility, specifically, a resource provider selection strategy based on a bidding performance ratio and a task scheduling sequence strategy based on a greedy profit-emergency ratio are adopted, and optimal matching and an optimal task processing sequence are achieved. Simulation results show that the number of effective processing tasks and time delay of task processing are increased, the advantages of edge collaboration task unloading and flexible task processing sequence adjustment are fully utilized, and the resource utilization rate is increased.

The present invention relates to the technical field of communications. Disclosed is a two-party quantum secret magnitude comparison method based on a two-level bell state. The present invention comprises the following steps: step I: protocol participants Alice and Bob respectively possessing their own secret values SA and SB, and Alice and Bob sharing a secure n-bit key KAB=KAB1KAB2KKABn by means of QKD; and step II: Alice and Bob sharing a collision-resistant hash function Hash(.) that is resistant to quantum algorithms, and with the assistance of a semi-trusted third party (TP), Alice and Bob completing the comparison between the magnitudes of the secret values of the two. By means of the present invention, an equality relationship and magnitude relationship between the secret values of the two participants, i.e. Alice and Bob, can be effectively determined; a quantum state prepared by the TP is a two-particle maximally entangled Bell state; and by means of the first eavesdropping detection, the absence of eavesdroppers and the honesty of the TP are verified.

The invention discloses an on-orbit security identification method, which comprises the following steps: a system initialization stage: a trusted third party distributes a key for a satellite and initializes a Cuckou filter; in the coarse granularity comparison stage, a satellite divides a remote sensing scene into sub-images, extracts feature vectors, and performs preliminary difference identification through a Cuckou filter and a secure matrix encryption (SME) technology. In the fine granularity identification stage, if potential differences are found, difference sub-images are confirmed through construction of a binary hash tree and MSIP calculation. And the difference is further accurately positioned in the DCTree-based identification stage. And the differential sub-image is encrypted by using a symmetric session key and is safely transmitted to a ground station, so that the confidentiality and integrity of data are ensured. On the premise of protecting the data confidentiality of the remote sensing image, remote sensing satellite on-orbit safety difference data identification is completed, the method is suitable for the situation that satellite resources are limited, and satellite on-orbit data efficient safety processing and data downlink are achieved.

A cross-chain transaction method, applied to a cross-chain system, the cross-chain system comprising a first blockchain network and a heterogeneous second sub-blockchain network and third blockchain network. The method comprises: a cross-chain component of the first blockchain network acquiring a cross-chain transaction request, the cross-chain transaction request being used to request execution of a cross-chain transaction from a second blockchain network to the third blockchain network, the cross-chain component performing identity authentication and permission verification based the cross-chain transaction request, and obtaining a verification result; when the verification result indicates that verification is successful, and when nodes of the first blockchain network reach a consensus on the cross-chain transaction, the cross-chain component recording transaction information of the cross-chain transaction in a ledger of the first blockchain network, and the cross-chain component of the first blockchain network then notifying a cross-chain component of the third blockchain network to record the transaction information of the cross-chain transaction to a ledger of the third blockchain network. The method does not rely on a third party, and solves the problem of relying on the endorsement of a trusted third party or requiring a third-party relay chain to achieve cross-chain capabilities.

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 biological recognition verification method and system based on homomorphic encryption, and belongs to the technical field of cryptography. In order to solve the problem of user privacy disclosure in a biological feature authentication process, a homomorphic encryption technology is mainly adopted, and feature protection is realized through homomorphic similarity calculation and homomorphic approximation comparison. In the process of user registration and identity authentication, quantitative coding and homomorphic encryption are carried out on biological characteristics, homomorphic multiplication is executed at a server side to calculate similarity, bit decomposition and homomorphic calculation are carried out at a trusted third party to compare results, and only single-bit authentication information is output. According to the method, the biological characteristics, the similarity and the comparison threshold of the user can be protected, the authentication privacy and security are improved, and meanwhile, the calculation overhead is relatively low.

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 invention relates to the technical field of traffic prediction, in particular to a crowd traffic prediction method and system based on multi-party longitudinal federated learning, and the method specifically comprises the following steps: multi-party data owners perform local feature coding, and perform spatial-temporal feature coding on spatial-temporal crowd traffic data of spatial-temporal traffic data owners; the method comprises the following steps: performing external factor feature coding on external factor data of an external factor data holder, performing encryption and differential privacy protection on coded features of each holder through privacy protection distributed joint modeling, and transmitting encrypted features subjected to encryption and privacy protection to a global module of a trusted third party; and finally, decrypting the global module encryption feature of the third party, performing crowd flow prediction according to the decrypted feature, and updating the local model of each data holding party. On the premise of ensuring data privacy security, the problem of predicting mobile crowd traffic of scattered data sources can be effectively solved by utilizing cross-domain collaboration of space-time traffic data and external factors.

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.

Technology is disclosed for a method that may comprise: identifying an owner key seed; identifying a receiver key seed; determining a delta between the owner key seed and the receiver key seed; and storing the delta with a trusted third party in a manner that is inaccessible by a receiver. The delta may be provided to the receiver based on a transfer condition being satisfied.

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.