7results about How to "Achieve privacy" patented technology

The invention relates to a multi-signature method with aggregable and responsibility-traceable public keys, relates to the technical field of block chains, and is used for solving the problems that in a privacy protection block chain system, user identity tracking is difficult, the privacy of signers cannot be guaranteed, and privacy protection and effective supervision cannot be balanced. The multi-signature method comprises the following steps: generating a public and private key pair and public parameters; generating a user key; key distribution; user signature; the combiner aggregates and outputs the signature; verifying signature validity; the tracker tracks the signer. According to the method, the roles of the combiner and the tracker are set, the work of aggregating the signature share is completed by the combiner, and only the signature signed and output by the combiner is recognized as an effective signature. And meanwhile, when some illegal behaviors occur, a participant signer is tracked through a tracker, so that identity tracking of the block chain is effectively realized, and meanwhile, privacy of legal users is ensured.

The present invention belongs to the field of image processing technology, and provides a distributed multi-source image fusion method and system based on privacy protection, including obtaining a global model; training the global model, obtaining local model parameters and sending them to a server for aggregation, obtaining a new global model, and sending the new global model to each client; classifying the clients into clusters according to task categories, training the new global model with specific tasks, obtaining specific task model parameters, aggregating the specific task model parameters in the cluster, and distributing the aggregated parameters to each client in the cluster, updating the global model to obtain a fusion network model; training a detail network model, and adding the outputs of the detail network model and the fusion network model to obtain a fusion result. The present invention can effectively improve the quality and efficiency of multimodal image fusion while ensuring data privacy and security, while reducing the operating cost and energy consumption of the system.

The application relates to an efficient and privacy-considered large model end-cloud collaborative inference method and system, which comprises the following steps: obtaining input and configuring KV cache control parameters through an end side; performing embedding calculation to generate a feature tensor and uploading the feature tensor to a cloud side; performing decoding layer inference on the cloud side according to authorization to call the KV cache and returning a hidden state; in a current generation step, performing speculative decoding prediction on subsequent tokens based on the hidden state on the end side, and calculating an end-side local inference result corresponding to a low-dimensional feature according to a dimension splitting rule; calculating a cloud-side local inference result corresponding to a high-dimensional feature on the cloud side, and returning the cloud-side local inference result after language self-adaptive cutting and encryption; fusing the two-end local inference results to obtain a complete distribution, sampling to generate an incremental token, uploading the incremental token to the cloud side for verification, and updating the KV cache.

The invention relates to a multi-microgrid cooperative operation optimization method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: constructing micro-grid operation constraints of each micro-grid according to new energy prediction power, energy storage constraint parameters, demand response parameters and transmission constraint parameters of each micro-grid; constructing a cooperative operation cost function according to the main network exchange cost parameter, the energy storage cost parameter, the electric carbon factor parameter and the carbon emission cost parameter of each micro-grid; constructing a multi-microgrid cooperative operation model according to the cooperative operation cost function and each microgrid operation constraint; and solving the multi-microgrid cooperative operation model by using a distributed alternating direction multiplier method in which an adaptive parameter updating mechanism and an error correction mechanism are introduced by taking the minimum cooperative operation cost function as an optimization target to obtain target electric energy exchange parameters among the microgrids. By adopting the method, the target electric energy exchange parameters for realizing the optimal cooperative operation among the microgrids can be efficiently obtained.

The invention provides a full-function layered deterministic wallet model supporting invisible address and signature aggregation. The full-function layered deterministic wallet model comprises a full-function layered deterministic wallet management layer supporting hierarchical organization and a transaction function layer supporting each manager entity in the organization, the full-function hierarchical deterministic wallet management layer supports hierarchical organization and comprises wallet key generation of a root manager entity and wallet key generation of an upper layer manager entity for a direct lower layer manager entity of the upper layer manager entity; a transaction function layer of each administrator entity in the organization is supported, including generation and verification of a single transaction signature and generation and verification of an aggregate signature. According to the invention, the invisible address function is added to the layered deterministic wallet, and a sender can very conveniently obtain the receiving address of the digital assets through a non-interactive key derivation mechanism, so that the main public key characteristic of the layered deterministic wallet is enhanced, and the user convenience and user privacy are better realized.

The present application relates to a power grid data audit method, device, power grid system and storage medium. The method includes receiving an audit request and a smart contract sent by a user terminal; sending the audit request to a target user through a server so that the target user can feedback a first ciphertext; the first ciphertext is obtained by encrypting the power grid data by the target user based on an elliptic curve encryption model and the public key of the user terminal; receiving a zero-knowledge proof sent by the user terminal; the zero-knowledge proof is obtained by using a proof generation model to process the public key, the private key of the user terminal, the first ciphertext and the second ciphertext when the user terminal obtains the second ciphertext and the first ciphertext from a blockchain node; the second ciphertext is obtained by encrypting the power grid data by the user terminal based on an elliptic curve encryption model and a public key; verifying the validity of the zero-knowledge proof based on a smart contract, and outputting a verification result for determining whether the power grid data has been tampered with. The present application can improve the security of data transmission in the power grid system.

The invention belongs to the technical field of higher education management, and discloses a semi-supervised learning-based college student consumption data mining and distinguishing method, which comprises the steps of obtaining college multi-source original consumption data, converting the data into a desensitization behavior unit according to a preset mapping interval, counting a single score of each dimension, and generating a monthly consumption toughness index; creating a consumption toughness time sequence atlas, mounting external event tags, and integrating to form a whole school consumption toughness time sequence atlas database; optimizing a toughness rule weight through semi-supervised learning and mining a recessive consumption mode combination to obtain an optimized five-dimensional consumption toughness weight matrix and an enhanced feature set, and further generating a pseudo tag set; performing economic difficulty state judgment on the to-be-judged students through a three-step fusion judgment rule chain, generating a judgment analysis report, and performing division to obtain an economic difficulty student candidate list, a non-economic difficulty list and an observation list; and performing three-dimensional synchronous feedback optimization in combination with an artificial rechecking result to form a discriminant management link of closed-loop evolution.