6results about How to "Guaranteed verifiability" patented technology

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 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.