9results about How to "Guaranteed verifiability" patented technology

The application provides a teaching intervention method, system and device based on multi-modal intelligent analysis, and relates to the technical field of intelligent education. The method comprises the following steps: collecting multi-modal learning data of a learner, extracting each modal learning feature, and inferring the current learning state of the learner; if the current learning state is abnormal, at least one main factor causing the abnormality is screened out based on the causal relationship between a plurality of preset candidate factors and the abnormal learning state, and a corresponding first set of intervenable variables is determined; the causal relationship is obtained based on learning portrait analysis including learning state history records; an intelligent agent is called, and a teaching plan and teaching intention for the current learning state are generated based on an intelligent agent data set including the learning portrait, the main factors, the first set of intervenable variables and the teaching task of the current round; and a digital person is driven to execute the teaching plan and the teaching intention. The application can comprehensively perceive the state of the learner, and realize collaborative optimization of teaching strategies and teaching interactive behaviors.

The invention provides a multi-party data cooperative computing method, electronic equipment, a readable medium, a program product and participant equipment, and the method comprises the steps: constructing a (k1)-degree polynomial with a constant term as original data in a finite field for the original data held by each participant, calculating function values of the polynomial at n different independent variable values to generate n data shares, wherein n is the number of participants; exchanging the data shares to enable each participant to obtain a data share set which is from all participants and corresponds to the same independent variable value; performing homomorphic cooperative calculation in a finite field based on the data share set to generate a calculation result share; k calculation result shares are collected, the shares are calculated in a finite field through an interpolation-based reconstruction method, and a final result of collaborative analysis is obtained. According to the invention, on the premise of not exposing the original data, the security cooperative calculation of the multi-party data can be realized.

The invention provides a trusted data space data interaction method based on a block chain oracle machine, and relates to the technical field of block chains, and the method comprises the steps: receiving a data packet sent by a data provider; performing BLS aggregation signature verification on the BLS signature data, recalculating a local commitment value, comparing the local commitment value with a vector commitment value to confirm content integrity, and judging data admission only after the dual verification is passed; and in response to a data request of a data user for a target data item, obtaining the original data, the digital signature and the Merkle member proof corresponding to the target data item based on the Merkle DAG data structure, generating a verifiable object, and returning the verifiable object to the data user. The block chain oracle machine is introduced as a trusted data space access data connector, and verifiable access and traceable circulation of data resources are realized by constructing a trusted bridging link among the data resources, the block chain oracle machine data connector, the block chain account book and the trusted data space.

The application discloses a blockchain cross-chain transaction credible verification method, an adapter of an access cross-chain gateway is configured for each blockchain, the blockchain sends self verification rules and corresponding verification functions to the adapter and the cross-chain gateway through registration, the adapter saves the verification functions, the cross-chain gateway converts executable files of the verification rules into verification services, each blockchain, the adapter and the cross-chain gateway obtain identity credentials through a cross-chain alliance; the blockchain initiates a cross-chain transaction request to the corresponding adapter, an origin blockchain adapter generates a cross-chain Merkle tree and packs the cross-chain Merkle tree into cross-chain transaction data to forward to a target blockchain through the cross-chain gateway and an adapter of the target blockchain, in the process, each component verifies identity through the identity credentials, and verifies data through the cross-chain Merkle tree, until the transaction is completed. The application realizes the reliability of a complete life cycle of cross-chain transaction based on a cross-chain Merkle tree, in combination with verification rules and a verification link.

This invention relates to the field of medical information security technology, and discloses a blockchain-based method and system for auditing and detecting unauthorized access to medical data. The method includes: structurally modeling medical data access behavior to construct access behavior feature vectors; performing multi-source fusion risk assessment based on an access control policy model and a behavior learning model to obtain a comprehensive risk value; executing a graded processing strategy according to the risk level and generating corresponding audit logs; structurally organizing the audit logs and constructing a Merkle tree to generate data fingerprints; writing the data fingerprints and summary information into the main chain, storing the complete audit logs in a side chain or off-chain storage, and achieving consistency verification through Merkle proofs; using smart contracts to verify the audit data and automatically execute access control responses, while simultaneously feeding back the audit results for model updates. This invention enables real-time detection, graded processing, and trusted auditing of medical data access behavior.

The invention discloses a public counting hidden weighted distributed responsibility-traceable electronic voting method, which comprises the following steps of: dividing a vote into a certain number of shares by a voter, carrying out password commitment, disclosing the commitment, encrypting the shares of the vote, and sending the encrypted shares to a corresponding distributed bill server; the bill servers verify the correctness of the vote shares and aggregate the vote shares of each voter after weighting the vote shares, and after the voting stage is finished, all the bill servers send the aggregated value of the collected vote shares to a certain specific bill server; and entrusted to carry out final aggregation to obtain a complete vote counting result and publish the final vote counting result in a verifiable manner. According to the invention, the vicious bill server cannot obtain the vote information of a single voter and the vote counting result is not known by participants except the selected bill server, so that the privacy of the voters and candidates is protected, the verifiability of the vote result is ensured, and the vicious behaviors of the participants can be responsibilized.

The invention discloses a traceable privacy protection method and system for crowd sensing. The method comprises the following steps: step 1, initializing a system; 2, a data demander registers; step 3, establishing a task group; 4, submitting the collected data; and 5, tracing the user. According to the invention, the block chain technology and the group signature technology are combined, so that the problem of user privacy leakage in the traditional block chain traceability process is solved. The group signature allows the user to participate anonymously in the transaction, and the unforgeability and traceability of the signature are ensured at the same time. While the privacy of the user is protected, the malicious behavior can be effectively traced. When dispute occurs or auditing is needed, an auditing party can track the signature under the condition that privacy of other users is not leaked, and accuracy and fairness of source tracing are ensured.

The invention provides an intelligent electric meter data security storage and tamper-proof block chain authentication method and system, and relates to the technical field of data security, and the method comprises the steps: receiving intelligent electric meter data, dividing a structured data object into a public fragment and a private fragment, executing zero-knowledge proof on the private fragment, and generating a mixed data carrier; establishing a cross-chain anchoring relationship, and synchronizing the Hash abstract of the mixed data carrier to a main chain and a side chain; triggering the smart contract to verify data validity; and verifying the data integrity through the global identifier when receiving the traceability request. According to the invention, secure storage and tamper-proof authentication of ammeter data are realized, sensitive data privacy is guaranteed, and data traceability and non-tampering are ensured.

The application discloses an auditable chain model digital watermarking method, comprising the following steps: an owner inputs a salt value and an owner identifier into a computer system; a model training period is divided into training fragments; a model weight of a previous training fragment is used to calculate an identifier of a current training fragment through a hash function; the identifier is used as a seed of a generator to generate watermark information of the current training fragment; a total loss function containing a task loss and a watermark loss is constructed; watermark loss is realized by constraining a target embedded parameter symbol or a value to approximate a binary watermark bit stream; watermark positioning is realized through statistical analysis; a private noise is introduced before a training fragment is saved; a model saved after noise injection is saved as a detection point file, which is used as initial weight of a next fragment; training is repeated and iterated to output a model hash chain; double verification is performed based on the model hash chain according to a verification request, and a verification result is sent to a verifier. The method improves the robustness of the watermark to various attacks and is suitable for model distribution and copyright evidence scenarios.