526 results about "Secret sharing" patented technology

The invention discloses a financial data processing method and system based on machine learning, and a storage medium. The method comprises the following steps: encrypting customer credit data of a financial institution through Paillier homomorphic encryption and extracting a risk feature vector; performing secret sharing segmentation on the local gradient parameters and then safely aggregating the local gradient parameters into global gradient parameters through a BGW protocol; on the basis of the privacy level, global gradient parameter differential privacy noise is injected and subjected to federal training to obtain a cross-institution credit evaluation result; calculating risk characteristics and evaluation results through homomorphic encryption to obtain an encrypted credit score, and performing secure multi-party calculation and decryption to generate a customer credit report; and block chain supervision compliance verification is carried out to generate an audit record, and a privacy budget optimization scheme is dynamically adjusted according to the business emergency degree. The technical problems that in an existing financial data processing method, information is incomplete due to data islands, data leakage risks are caused by insufficient privacy protection mechanisms, and an effective supervision compliance verification mechanism is lacked are solved.

The invention discloses a government affair data dynamic authorization management method based on secure multi-party computing, and relates to the field of government affair data security authorization management, and the method comprises the steps: distributing a unique identity identifier for each participant, generating an asymmetric key pair through employing a national secret SM2 algorithm, registering a public key and mechanism attribute information to an alliance chain smart contract, and generating an identity certificate package; and based on attribute information in the identity credential packet, the participants cooperatively generate anti-quantum dynamic attribute-based encryption key fragments through a secure multi-party computing protocol, and the anti-quantum dynamic attribute-based encryption key fragments are distributed to the participants by adopting a threshold secret sharing technology. Threshold decryption and token verification are achieved through an alliance chain verification intelligent contract, an authorization record is generated, an access control strategy of an edge computing node is configured according to the authorization record, an oblivious transmission protocol is adopted to respond to data query, and an audit node monitors an access log, dynamically adjusts attribute weight parameters and synchronizes the attribute weight parameters to a key fragment.

The invention discloses a self-random cross-chain dynamic cooperative supervision method based on zero knowledge proof, which is used for realizing safe and supervisible privacy protection transactions in a block chain cross-chain environment. The method comprises the following steps: dynamically screening users through MACRO credit scores to form supervision chains, the number of which is consistent with that of application chains, and generating public and private keys; deploying a contract and a ZKP circuit in each chain, wherein ZKP generation adopts self-random hash; according to the supervision chain secret key and the self-random hash, the transaction is encrypted, verified and signed, and a random block header and a transaction link are recorded in a linker (TEE); after the supervision chain verifies the signature, the transaction is linked and stored in a supervision chain Merkel mountain range, and a linker is updated at the same time; a supervision chain of any party puts forward a supervision application, and after the supervision application is agreed by the opposite party, two rounds of two-layer secret sharing technology collaboratively supervise the recoverable transaction. According to the method, self-random encryption, zero-knowledge proof and dynamic supervision mechanisms are fused, and multi-key supervision, self-random encryption key dynamic change and multi-supervision-chain dynamic cooperative supervision are realized.

The invention discloses a key sharing and security detection scheme based on an intelligent electric meter, and aims to solve the problems that an untrusted electric meter in an intelligent power grid is difficult to identify effectively, the detection scale is uncontrollable and the communication security is insufficient. According to the scheme, the elliptic curve cryptography, the threshold secret sharing mechanism and the physical unclonable function are combined, and hardware-level binding of the unique identity of the equipment and the secret key is achieved in the registration stage. Through a threshold password mechanism, a plurality of intelligent electric meters cooperatively participate in key reconstruction and encryption communication, and the intelligent electric meters can still work normally when part of the electric meters fail or are broken through. The center node can actively identify a fault or a malicious ammeter and dynamically adjust a communication strategy according to the integrity and correctness of the feedback information. Meanwhile, the scheme supports dynamic identity updating and integrity verification, and effectively resists modeling attacks, Sybil attacks, replay attacks and DoS / DDoS attacks. According to the invention, secure identity authentication, reliable key distribution and efficient anomaly detection are realized, and the security of smart grid terminal communication is improved.

The invention belongs to the technical field of information security, and particularly relates to a super-threshold data set intersection method and system for security information processing. The method comprises the following steps: S1, carrying out casual key value pair storage OKVS encoding and decoding between each participant and other participants; s2, each participant constructs a hash table according to a decoding result and elements of the participant; and S3, sending the constructed hash table to a specified party, summarizing information in the hash tables of all participants by the specified party, and finally obtaining a super-threshold intersection result by using a secret sharing scheme. The method has the characteristics that the hardware and operation and maintenance cost can be reduced, the data security is enhanced, and the calculation efficiency is improved, so that the industrial control requirement of short-time response is met.

The invention discloses a federal learning security training method and system based on differential privacy, belongs to the technical field of artificial intelligence, and aims to solve the technical problem of how to realize data privacy protection and model precision balance in cross-mechanism model training. Comprising the following steps: constructing a distributed structure comprising a plurality of clients and a server; each client constructs a noise variance calculation model by taking the gradient feature, the privacy budget coefficient, the data sensitivity level coefficient and the training stage coefficient as calculation parameters after each round of local training is finished, and Gaussian noise is calculated according to the noise variance; verifying the noise variance of each round, and adjusting the calculation parameters of the noise variance based on the comparison result of the accumulated budget consumption and the global privacy budget; dynamically allocating the data sensitivity level of the local training data and the budget allocation proportion of the client; and the server performs secure aggregation through a secret sharing algorithm, and distributes the updated global model parameters to each client.

The invention relates to the technical field of power system frequency modulation control, in particular to a thermal power generating unit collaborative frequency modulation method and system based on block chain federated learning. According to the method, a decentralized P2P network is constructed through a block chain technology, and model parameters are initialized based on a secret sharing mechanism; carrying out local training by adopting an adaptive federal near-end optimization algorithm, measuring data isomerism through KL divergence and dynamically adjusting a regular coefficient; carrying out noise addition on a sample by adopting differential privacy, and carrying out homomorphic encryption to realize parameter encrypted transmission; selecting representative nodes based on a block chain consensus mechanism to execute parameter aggregation in the encryption domain; and multi-unit collaborative frequency modulation is realized through model prediction control. According to the method, the problems of data privacy protection, heterogeneous data adaptation and decentralized cooperative training are solved, and the stability of power grid frequency regulation and control is improved.

The invention provides a privacy protection multi-task allocation method for an unmanned aerial vehicle crowdsourcing perception system, which combines addition secret sharing and homomorphic encryption to realize bilateral privacy protection of a task side and an unmanned aerial vehicle position. The task requester and the working party split a task position and a UAV departure position into two secret shares [.] 1 and [.] 2 by adopting an additive secret sharing mechanism respectively, and send the two secret shares [.] 1 and [.] 2 to the two independent service parties S1 and S2 respectively; the service party calculates an encrypted square Euclidean distance between the UAV and the task based on the shared share, and generates two distance matrix shares D1 and D2. On the basis, the two service parties cooperatively execute privacy comparison and a minimum index protocol under the optimized Paillier, and multi-task encryption shortest path distribution is realized in combination with a Hungary algorithm. According to the method, addition secret sharing is fused, Paillier encryption and task matching optimization are optimized, the total flight path of the UAV is remarkably reduced while position information privacy of the whole task allocation process is guaranteed, and good safety and deployability are achieved.

The invention discloses an identity authentication method and system based on a national secret algorithm, and the method comprises the steps: building a hierarchical key management system based on a national secret IBE framework, generating a system master key pair through employing an SM2 algorithm, and achieving a decentralized key distribution mechanism; constructing a domain perception differential privacy protection module, defining a privacy budget allocation strategy according to the security level, and adding calibrated Laplace noise to the user identity feature vector; designing a distributed batch matrix multiplication protocol, and decomposing the distributed batch matrix multiplication protocol to a plurality of computing nodes for parallel processing through a secret sharing technology; a zero-knowledge proof verification mechanism is implemented, and identity verification is completed through a commitment scheme based on an SM3 hash algorithm; deploying a self-adaptive key updating strategy, and analyzing threat level change through a threat situation evaluation function; and establishing a secure communication channel based on SM4 symmetric encryption, and performing encryption processing by using the temporary session key. The security and expandability of the system are improved, the privacy of the user is effectively protected, and the system adapts to a dynamically changing network threat environment.

The invention discloses a channel security transmission method for protecting data privacy of an edge gateway of the Internet of Things, and relates to the technical field of security and privacy protection of the Internet of Things, and the method comprises the steps: constructing an edge gateway security architecture, generating a quantum key through a QKD module based on the constructed edge gateway security architecture, and generating sensitivity data through an IPS vNSF; calculating noise based on the sensitivity data, generating disturbance data based on the noise, calculating a shared key, generating an encryption key in combination with the quantum key and the shared key, segmenting the encryption key through Shamir secret sharing, and encrypting and decrypting the key; encrypting the noise by using the decrypted key, generating global noise based on the encrypted noise, and calculating secondary disturbance data based on the global noise; and generating coded data based on the secondary disturbance data, and generating reconstruction data based on the coded data. According to the invention, the comprehensive security authority of the edge gateway in the aspects of key security and privacy protection intensity is improved.

The invention relates to the technical field of data encryption, in particular to a VPN dynamic hierarchical encryption system and method for cross-border data transmission, and the method comprises the steps: dividing a data flow into different hierarchies based on information entropy; the high-entropy core data is embedded in a steganography mode through a chaos algorithm by means of the low-entropy redundant space; carrying out global basic encryption on the mixed carrier, and respectively executing chaotic scrambling and differential iterative diffusion for the steganography region and the middle entropy region; dynamically injecting a filling block according to entropy feedback to realize flow homogenization shaping; and splitting the deconstructed index through threshold secret sharing, and performing hidden distribution. According to the method provided by the invention, the concealment and security of cross-border data transmission can be effectively improved.

The invention discloses a short message data encryption and secure transmission system, which relates to the technical field of short message encryption and comprises an environmental parameter monitoring module, an encryption granularity adjusting module, a key management module, a data transmission module and a security audit module. The environment information is acquired in real time through the environment parameter monitoring module, accurate evaluation of environment risks is achieved, the encryption granularity adjusting module dynamically adjusts the encryption granularity according to the environment information, the problem of resource waste or insufficient safety caused by the fixed encryption granularity is solved, the effect of improving the resource utilization efficiency while the safety is guaranteed is achieved, and the user experience is improved. The secret key management module adopts a Shamir secret sharing technology to carry out fragmentation storage and recovery verification on the secret key, the problem that the secret key is easy to leak in centralized storage is solved, the effect of improving the secret key security is achieved, on the whole, the system can flexibly adjust the encryption strategy according to the environmental risk, and the encryption efficiency is improved. And the reliability and adaptability of short message data encryption and secure transmission are remarkably improved.

The invention discloses a decentralized virtual identity key collaborative management system based on a block chain, and belongs to the field of block chain technology and cryptography. The system comprises a block chain network layer used for distributed storage of key fragments and execution of an automation strategy; the virtual identity management module is used for generating a unique identity identifier (DID) through a threshold signature algorithm after fusing the biological characteristics and the device fingerprints; the key collaborative management module is used for storing the main key in a fragmented manner through a Shamir secret sharing algorithm, and dynamically selecting an encryption algorithm to generate a sub-key according to the security score of the target platform; the verification and auditing module is used for verifying the holding legality of the secret key through a zk-SNARKs technology and injecting random noise to resist side channel attacks; and the block chain network layer, the virtual identity management module, the key collaborative management module and the verification and auditing module are connected with an encrypted communication protocol through an intelligent contract interface.

The invention discloses a data management method and system for a data credible delivery platform. Access is realized through symmetric encryption of source data, and credible right confirmation is realized by relying on identities of owners and users of alliance chain evidence storage, source data hash and authorization rules; adding a timestamp to the encrypted data stream verified by the certificate to form a traceability mark, and encrypting or desensitizing privacy data; the trusted computing is provided with three modes: a local mode executes an algorithm in an ownership party isolation environment and records a log on a chain, a third-party mode constructs a trusted sandbox based on TEE, executes computing and links log hash after remote certification, and an algorithm privacy mode realizes cooperative computing through a Shamir secret sharing splitting algorithm in combination with MPC, a confusion circuit and zero-knowledge certification; and the result delivery adopts public key encryption, a decryption key is issued after the receipt of the user is verified, and the result hash and the receipt are linked for evidence storage, so that the problems that the existing trusted platform cannot support the execution of various algorithms and is lack of effective supervision are solved.

The embodiment of the invention discloses a safety calculation method and device for robot cluster collaboration and a medium, and relates to the technical field of safety calculation, and the method comprises the steps: carrying out the participant authentication of a robot cluster and a production enterprise node in advance, and carrying out the dynamic identity verification of each robot after the participant authentication is passed, the operation feature data of each target robot passing verification are extracted and encrypted, and encrypted feature vectors are determined; splitting the encrypted feature vector into a plurality of data fragments, storing the data fragments to a distributed node, obtaining a predetermined task cooperation strategy, and distributing the task cooperation strategy to an edge computing node in a secret sharing manner; executing safety calculation in a ciphertext state through a task cooperation strategy and the plurality of data fragments, determining a joint motion control instruction of each target robot, and generating corresponding zero-knowledge proof data; and after the verification is passed, the encrypted control instruction is distributed through the block chain smart contract, so that safety calculation of robot cluster collaboration is realized.

The embodiment of the invention discloses a ciphertext domain reversible information hiding method and device based on secret image sharing. A specific embodiment of the method comprises the following steps: carrying out secret sharing on secret information to be embedded to obtain a secret information sharing copy sequence; carrying out system conversion on the secret information sharing part sequence to obtain a converted information sharing part sequence; performing image scrambling on the secret image by using a preset scrambling key to obtain a scrambled image; performing region division on the scrambled image to obtain an image division region sequence; embedding each converted information sharing copy in the converted information sharing copy sequence into a corresponding image division region in the image division region sequence so as to carry out ciphertext domain reversible information hiding, and obtaining a marked sharing copy sequence; and storing the marked shared copy sequence. According to the embodiment, the calculation complexity of the encryption process can be reduced.

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 present disclosure provides a method for processing a secret-shared adjacency list of a graph. The method includes partitioning the secret-shared adjacency list into blocks, adding a vertex identifier to each tuple in the secret-shared adjacency list, defining a boolean variable for each block, computing a secret-shared vector of row identifiers for each tuple, shuffling tuples and additional tuples, extracting components from the shuffled tuples to form a d-normalized replicated adjacency list, and storing the d-normalized replicated adjacency list in a non-volatile storage device. The method utilizes parallel processing capabilities and a hardware-based random number generator for shuffling. The method also includes securely transmitting and receiving encrypted data related to adjacency lists through a network interface.

The invention discloses a privacy protection neural network reasoning method based on multi-point evaluation. According to the method, in the reasoning process of the privacy protection neural network, a given nonlinear function is constructed into a K-segment k-degree polynomial, a safe multiplication tree and a safe remainder tree are established, and then the remainder polynomial is safely evaluated. Due to the fact that the polynomial power needing to be evaluated in the online stage is reduced, the number of self-multiplication terms is reduced, the number of communication rounds of overall polynomial evaluation is reduced, the number of calling times of a secure multiplication triple in secret sharing is reduced, and the overhead of offline pre-calculation is correspondingly reduced. In addition, the hierarchical structure of the secure multiplication tree and the remainder tree supports multi-point input parallel computing, and the parallel computing capability of a modern multi-core processor or a GPU can be fully utilized.

Embodiments are directed to secure configuration change at an edge device. A method includes receiving configuration change data at a device, the configuration change data associated with an encrypted authentication key; sending the configuration change data to a plurality of peer devices; receiving secret shares from a quorum of the peer devices, wherein each of the quorum of peer devices sends its respective secret share if it determines that the configuration change data complies with a configuration policy; constructing an decryption key using a quorum of the secret shares; decrypting the authentication key using the decryption key; and applying the authentication key to install the configuration change on the device. The method may further comprise determining, by the device, whether the configuration change data complies with the configuration policy; and constructing the encryption key using the quorum of the secret shares and a secret share stored on the device.

The invention discloses a privacy protection federated learning method based on result-agnostic function encryption, and the method comprises the steps: dividing a derived key assembly into a plurality of shares, and carrying out the reconstruction through the remaining shares even if a part of clients are offline and the key shares are missing, thereby guaranteeing that a function key can be recovered, and an encryption model can be correctly aggregated, and improving the privacy protection efficiency. And the robustness and fault tolerance of the system are greatly improved. A non-interactive key exchange technology is adopted, and a secret sharing mechanism is combined, so that a client can generate a private key in a collaborative manner under the condition that no trusted third party participates, the deployment complexity is reduced, and the security and expandability of the system are improved. A function encryption process with an unknown result is designed, only an intermediate result in an encrypted state is output in an aggregation stage, and decryption is finally completed by local joint of a client, so that the possibility of snooping an aggregation result and reversely deducing original data by a server under a semi-honesty model is fundamentally avoided, and data privacy in a federated learning process can be effectively guaranteed.

The invention relates to the technical field of federated learning, in particular to a federated learning security aggregation method based on vector space secret sharing, which comprises the following steps that: a client divides a local model gradient into a plurality of secret shares and distributes the secret shares to a plurality of servers; the server carries out aggregation operation on the secret share through a security computing protocol, wherein the aggregation operation comprises addition and multiplication operation; calculating the similarity between the clients based on the secret state data, and screening out a credible client set; and executing security aggregation on the gradient share of the trusted client, reconstructing a global model gradient and issuing the global model gradient. According to the method, client poisoning attacks can be resisted, server reasoning attacks can also be resisted, a federal learning global model security aggregation method is designed based on vector space secret sharing, and threats caused by server offline are reduced.

The invention discloses a railway freight data security encryption method based on distributed storage, which relates to the technical field of railway information security, and comprises the following steps: registering distributed storage nodes, collecting railway freight tasks, tracks and node registration data, constructing a distributed storage network based on the node registration data, generating a node list, and encrypting the node list. Performing identity authentication, key initialization and identity verification during access among the nodes on the nodes in the node list, issuing a short-time access token after verification is passed, generating a track interval key according to freight track data, fragmenting the track interval key by using a threshold secret sharing method, distributing the track interval key to multiple nodes and dynamically deriving a key, and based on the derived key, using an improved ECC parallel encryption method in combination with Paillier dynamic encryption to encrypt data, and generating an encrypted data packet. According to the invention, track data is used as key drive, and dynamic hierarchical encryption and collaborative credible decryption of railway freight task data in a distributed environment are realized.

The invention discloses a user credit risk assessment method, system, equipment and medium based on multi-industry federated learning in order to improve data security of federated learning, and belongs to the technical field of information. The method comprises the following steps: extracting operator data and bank financial data, and performing encryption preprocessing through a Hash mechanism; performing federated learning on the operator data and the bank financial data after encryption preprocessing, accessing a PSI module as a plug-in to an FATE core, converting RSA of an FATE algorithm into an elliptic curve, and performing secret sharing through a hexagonal grid cellular automaton to obtain a trained federated model; and evaluating the credit risk of the user by using the trained federal model. According to the method, the requirement of hiding a keyword of a queried object or a client ID is met, the hidden query service provides a matched query result, calculation can be carried out without going out data of both parties, and the method follows the data authentication and access process of multi-party security calculation and the overall business process. Data barriers among different industries are broken through, and deep data cooperation of multiple industries is realized.

The invention discloses a unified key management method and device for a distributed password service component, and aims to solve the problems of single-point failure, consistency deficiency and insufficient life cycle management and control of existing key management. According to the method, based on a distributed key generation protocol, Shamir secret sharing, PBFT consensus and Lagrange interpolation, key generation, distributed storage, safe distribution and dynamic rotation whole-process management and control are achieved. The device is composed of n nodes including a processor, a memory and a communication and key processing module, and supports encrypted storage, secure communication and consensus collaboration. Security is guaranteed through a threshold mechanism, hash verification and a hash chain technology, availability is improved through SDN optimization and failover, and nodes and parameters can be flexibly adapted and expanded. The method is applied to scenes such as a power grid, gives consideration to safety, availability and compliance, and is suitable for unified key management of distributed system password services.

The invention provides a block chain privacy protection method and system based on dynamic threshold homomorphic encryption, and efficient encryption and flexible management of privacy data are realized by combining a Paillier semi-homomorphic encryption algorithm and a dynamic threshold secret sharing mechanism. The method comprises the following steps: generating a public key-private key pair and a threshold parameter set based on a large prime number, carrying out grouping encryption on privacy information to be protected, directly executing dynamic data updating in a ciphertext state by utilizing an addition homomorphic characteristic, then splitting a decryption key into share ciphertexts, and realizing decentralized key distribution and security recovery through threshold parameter constraint; for a participant adding or exiting event, a threshold parameter set is dynamically adjusted and a key rule is reconstructed, and an encrypted data set is synchronously updated to maintain consistency. Therefore, the flexibility and expandability of the block chain system are improved while the account book privacy and the network privacy are guaranteed.

An audio authentication and identity verification system and method that encodes dynamic cryptographic information across the audible and inaudible spectrum (0 Hz→30 kHz, scalable to 124 kHz). A rolling acoustic signature simultaneously embeds identity, time, date, and GPS location using a hybrid of frequency shift keying (FSK), phase shift keying (PSK), Morse style pulse duration modulation, and time domain multiplexing. To guarantee post-event privacy and integrity, the system further incorporates a dual custody audio architecture in which every authenticated recording is split into complementary bit interleaved segments stored separately, and the decryption key is fragmented via Shamir 3-of-5 secret sharing—preventing unilateral access or tampering. The system establishes a tamper proof, real time, and offline capable authentication layer that functions in air, through conventional microphones, and via dedicated emitter hardware.

The invention discloses a data encryption method and device based on a server mode, and relates to the technical field of server mode data encryption, and the method comprises the steps: a server node registers in a control center through a hardware fingerprint, the control center generates a global master key, and employs a threshold secret sharing mechanism to distribute the master key to a registration node in a fragmentation manner, meanwhile, an anti-quantum noise matrix initial seed is generated based on cluster cooperation, and a reputation score of each node is initialized according to node hardware performance and network proximity; the control center periodically collects load data of each node, and generates an encryption sub-key dynamically bound with a node state by using the master key and the load data in combination with a current timestamp to obtain an encrypted ciphertext fragment; the server cluster generates an anti-quantum noise matrix of the current period through a consensus protocol, the encrypted ciphertext fragments and the noise matrix are subjected to linear superposition under the modulus condition, session vectors distributed by the control center are introduced in the superposition process, and ciphertext fragments containing an anti-quantum confusion layer are generated.

The embodiment of the invention discloses a federated learning method and device for a low earth orbit satellite network and electronic equipment. A specific embodiment of the method comprises the following steps: sending an initial global parameter to each participating satellite through a ground station; for each participating satellite, the following steps are executed: generating trained parameters based on local data collected by the participating satellite, initial global parameters and a preset model; performing secret sharing on the trained parameters to obtain a secret share parameter set; sending each secret share parameter to each aggregation satellite based on a preset safe path set; for each aggregation satellite, performing weighted summation on the secret share parameter from each participating satellite to obtain a weighted secret share parameter, and sending the weighted secret share parameter to a ground station; global training parameters are generated through the ground station according to the received weighted secret share parameters. According to the embodiment, the practicability and safety of federated learning in the satellite network are improved.

Systems and methods are disclosed herein for protecting data in a storage device by encrypting or decrypting the data with a Data Encryption Key (DEK). The storage device is communicatively coupled to a host and is locked with the host by secret sharing. In one example, the storage device comprises a Physically Unclonable Function (PUF) configured to, during a key generation phase of operation, generate a set of DEK responses based on a set of DEK challenges (chalDEK) and an assembler configured to obtain a set of SED DEK secret shares (SSSED) based on the first set of DEK responses, receive additional data, and assemble at least the set of SED DEK secret shares (SSSED) and the additional data to create a DEK master secret. The storage device also comprises a crypto module configured to receive a DEK based on the master secret and perform encryption and / or decryption of data using the DEK.