10 results about "Trusted client" patented technology

The application discloses a kind of securities asset risk prediction data encryption method and risk prediction method, including label semi-ordered tree construction step, the generation step of ciphertext-based random forest classification model, customer securities asset risk prediction step.First, in the trusted client, the securities asset information of historical customer is in clear text, the construction of LSOPE tree is completed and semi-ordered encryption is carried out to user data, and the yield-loss data as label is encrypted using deterministic symmetric encryption scheme, to hide the actual securities asset information of user, only the relative ordering relationship is retained.Then, the ciphertext is sent to the server, and a classification model is constructed using the random forest algorithm.Finally, the client encrypts the new securities asset information, the server performs risk prediction, and the prediction result is transmitted back to the client for decryption.The application can protect the privacy of customer data in all directions, while ensuring the calculation efficiency and accuracy of risk prediction.

Root trusted client memory page management is described. The root trusted client is loaded and authenticated by the hardware platform. The root trusted client is configured to manage memory operations of different clients via special permissions that allow it to perform memory operations using clients' private memory pages. To this end, the client page table includes a novel "T bit" in each entry, indicating whether the root trusted client or a different client owns the associated memory page. Each entry in the root trusted client's client page table additionally includes a "C bit," indicating whether the corresponding memory page is a protected page. The combined C and T bit values ​​of the page table entry indicate whether the operation performed as part of processing a client's memory request is offloaded from the hardware platform to the root trusted client.

The invention discloses a federal recommendation system end cloud collaborative defense method based on disturbance trajectory consistency, which comprises the following steps: a client locally executes optimization of a personalized recommendation model, and introduces a structure perception disturbance control mechanism; the client introduces an embedded amplitude constraint term in local training; the client adds a user preference direction guide regular item in a local training stage; updating uploaded by the client side is transmitted to the server side through a communication channel, the server records uploading history of the client side in multiple rounds of training, and behavior characteristics of the client side are modeled and analyzed; according to the uploaded track information, the server calculates the credibility score of each client; and a credible client set is screened out according to an evaluation result, and global model parameters are updated. According to the method, complex poisoning attacks such as slow drift type and alignment type can be effectively identified and inhibited under the condition that an external verification set is not needed, and the method has good universality and privacy friendliness and is suitable for various federal recommendation application scenes such as e-commerce, social contact and music recommendation.

1. Name of the product in this design: Quantum Zero Trust Client Graphical User Interface for Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design features of this product are its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: Used to set up and display the Quantum Zero Trust client software. 7. Human-computer interaction method of graphical user interface: Click "Connect" in the main view of Design 1 to enter the interface change state diagram 1 of Design 1; in the interface change state diagram 1 of Design 1, enter the username and password and click "Login" to enter the interface change state diagram 2 of Design 1; in the interface change state diagram 2 of Design 1, obtain and fill in the SMS verification code, click "Recharge Shield" to enter the interface change state diagram 3 of Design 1. Click "Connect" in the main view of Design 2 to enter the Design 2 interface change state diagram 1; in the Design 2 interface change state diagram 1, click "Username SMS" to enter the Design 2 interface change state diagram 2; in the Design 2 interface change state diagram 2, enter the username, obtain and fill in the SMS verification code, and click "Login" to enter the Design 2 interface change state diagram 3; in the Design 2 interface change state diagram 3, obtain and fill in the SMS verification code, and click "Recharge Shield" to enter the Design 2 interface change state diagram 4.

The application discloses a kind of based on hierarchical index screening's blockchain federated learning security aggregation framework, it is related to federated learning technical field, including pre-training stage, formal training stage and aggregation stage, wherein: pre-training stage, training client participates in pre-training and uploads the loss value calculated to blockchain;Official training stage, trusted client trains model and provides scoring standard, training client trains and uploads local model parameter, trusted client scores local model and calculates its weight, uploads model trust score and weight;Aggregation stage, model aggregation is carried out according to model trust score and weight, obtains the global model of this round and is chained.The application aggregates the trust score of uploaded local model by FLTrust algorithm, excludes malicious client, to some extent, counteracts byzantine attack, guarantees the accuracy of finally obtained global model, accelerates aggregation speed by loss prediction and aggregation weight, obtains better global model with less convergence round.

The invention provides a terminal equipment access control method, a communication system and a zero-trust security management platform, and relates to the technical field of communication. The terminal equipment access control method comprises the following steps: receiving terminal feature information sent by a zero-trust client deployed on terminal equipment; acquiring access control information of the terminal equipment according to the terminal feature information; and sending a subscription data modification request for the terminal equipment to a network equipment function NEF network element according to the access control information, so that the NEF network element modifies subscription data of the terminal equipment in a unified data management UDM network element according to the subscription data modification request, and triggers PDU session modification of the terminal equipment. Generating access control information of the terminal device based on the terminal feature information collected by the zero-trust client; therefore, the state of the terminal equipment accessing the target core network can be subjected to safety management based on the access control information, and potential safety hazards are avoided.

The application relates to a label noise robust federated learning method based on self-step learning and an adjacency matrix, a horizontal federated learning framework based on a client-server mode, and distributed training, and comprises the following steps: step S1, selecting a trusted client; step S2, calculating the near neighbor relationship of all client samples according to the trusted client; performing federated learning according to the trusted client obtained in step S1 to obtain a global federated model, and using the global federated model to calculate the near neighbor relationship of all client samples; step S3, self-step updating of sample near neighbor relationship and label evaluation and correction; and step S4, out-of-cluster sample processing. The application provides a safe and reliable label noise robust federated learning method based on self-step learning and an adjacency matrix, can effectively reduce the interference of noise data on a model, and improves the model convergence speed.

The application discloses a data sharing system based on P2P technology and relates to the technical field of data sharing.The client module acquires the data pre-shared by a user and shares the data into the rest trusted client modules.The device scoring unit scores the trusted client modules.The sharing analysis unit periodically analyzes the data capacity and click time of all the shared data and calculates the sharing capacity evaluation quantity in combination with the device score.The sharing management unit intercepts a plurality of trusted client modules according to the size of the sharing capacity evaluation quantity, obtains a sharing management table, and then selects the sharing client modules from the sharing management table when a certain client module pre-shares data, so that the sharing client modules replace the relay service unit to perform the sharing operation of the data.Through the method, the load pressure of the server on the shared data can be greatly reduced, and the sharing resources of the trusted client modules can be reasonably utilized as much as possible.

The application relates to an authorized login method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: in response to an access request of an authorized page sent by a mobile terminal, feeding back the authorized page to the mobile terminal; the access request is sent by the mobile terminal in response to a graphic code displayed by a zero-trust client; the graphic code is generated based on parameter information in a management and control server; the parameter information at least contains an access address and a user identifier of the authorized page, and the authorized page is used for triggering a login authorization request; receiving a login authorization request sent by the mobile terminal, the login authorization request carrying an authorization code provided by a third-party platform and the user identifier; obtaining identity information corresponding to the user identifier from the third-party platform based on the authorization code, and completing the authorized login processing. The method can realize offline login authorization of the zero-trust client.

Embodiments of the present application provide a single package authentication method and related device, which are used to improve the convenience of obtaining a zero trust client and an SPA knocking key. The method of the embodiments of the present application comprises: receiving an access address of a zero trust server input by a user through a browser; displaying a download installation page of the zero trust client to the user according to a pointing address of the access address of the zero trust server and download information of the zero trust client; if the user installs the zero trust client, receiving a knocking key obtaining request sent by the zero trust client, wherein the knocking key obtaining request carries a knocking key identifier encrypted by an irreversible algorithm; verifying the knocking key obtaining request; if the verification of the knocking key obtaining request is passed, forwarding the knocking key obtaining request to the zero trust server according to an IP address of the access address of the zero trust server, so that the zero trust server calls a message gateway to send the knocking key to the zero trust client.