87results about How to "Guaranteed data privacy" patented technology

The invention discloses a vehicle-road communication test system based on the 5G technology. A network element function is realized by adopting a virtualization and software mode; the network transmission rate can be obviously improved; concurrent network messages of dense network terminals in the Internet of Vehicles environment are ensured. the requirement of complex and huge Internet of Vehicles application on ultra-low time delay is met; the system cloud server adopts a virtualization technology. the number of servers is reduced; in a security aspect, multiple network slices are divided, asecurity isolation and height self-control special logic network is provided; network security is greatly improved, the information security of the Internet of Vehicles application and the data privacy of the user are ensured; the edge cloud server sinks a part of services to a network edge; the Internet of Vehicles application service processing speed based on roadside equipment can be remarkably increased, lower transmission delay is provided, the test platform can be used for verifying the actual effectiveness of the 5G technology applied to the Internet of Vehicles application, and support is provided for 5G-based Internet of Vehicles equipment layout and network design in a real scene.

The invention provides a 5G Internet of Things intrusion detection method and system based on federated transfer learning. The 5G Internet of Things intrusion detection method comprises the steps of:collecting a to-be-detected traffic in the Internet of Things, and acquiring a feature vector of an original data packet; and inputting the feature vector into a corresponding client model fm, k, andjudging whether the to-be-detected traffic is legal or not. According to the 5G Internet of Things intrusion detection, the federation transfer learning method in the 5G Internet of Things IDS is proposed for the first time, data from different Internet of Things can be safely aggregated, a good intrusion detection model for each Internet of Things is realized through knowledge transfer and sharing, the 5G Internet of Things intrusion detection method can be conveniently and safely applied to various different Internet of Things, and the method has very strong generalization ability and is suitable for popularization and application. Compared with an existing method, abnormal traffic can be detected more accurately, and unknown attacks can be detected more effectively.

The invention discloses a secure transaction execution method based on a smart contract. The method comprises the following steps: deploying a blockchain node service; receiving the original transaction voucher information for uplink storage; enabling each transaction processing node to sign a smart contract and deploy the smart contract to the blockchain; recording the encrypted transaction voucher information on a transaction data alliance chain based on the smart contract; judging whether a contract is deployed on a blockchain for a current smart contract, analyzing transaction parameters corresponding to the smart contract, checking the identity compliance, security and authority of the transaction parameters of the smart contract, packaging the smart contract, and enabling an execution end to call the smart contract and execute the smart contract; sending the effective consensus information to other transaction processing nodes, determining that the smart contract is effective, and performing uplink evidence storage. According to the invention, the information security is ensured, the requirements of the bank for transaction checking and account checking and clearing are met, the processing efficiency of each link in the transaction process is improved, the default risk caused by human factors is reduced, and the cost reduction and benefit increase of an industrial chain are promoted.

A data processing system has a browser with scripting engine means for executing a script. The scripting engine means implements a public scripting engine and a private scripting engine. The browser is configured to have the script executed by the public scripting engine if the script does not require access to a pre-determined resource at the system. The browser is configured to have the script executed by the private scripting engine if the script requires access to the pre-determined resource. Only the private scripting engine has an interface for enabling the script to access the predetermined resource. The scripting engine means is configured to prevent the private scripting engine from communicating data to the public scripting engine or to a non-approved server external to the data processing system.

The invention discloses a method for completely removing a deleted jpg format in a FAT 32 document system. The method comprises the following steps: S1: Searching all directory entries of the deleted jpg format document; S2: analyzing the directory entries and obtaining a distribution area of the jpg document; S3: writing in and filling empty value in the distribution zone; S4: obtaining a clear area position; S5: scanning and matching the clear area, and finding out fragment data; S6: writing in empty value and filling. Through a bottom layer, the method can analyze the deleted jpg document in the FAT 32 document system by, and find out the fragment data position of the deleted jpg document. The method ensures the safety of the normal jpg document while completely removes the jpg formatted document. Through the jpg document, the jpg document fragments in the clear area are removed for the second time, and thereby all fragment data are removed at the maximum degree, the public privacy is guaranteed, and malicious stealing behavior specific to private data is prevented.

The invention discloses a test case scoring model training method and a test case selection method. The test case scoring model training method comprises the following steps: determining a plurality of historical test cases for software testing; generating a training sample according to the historical test case, wherein the training sample comprises feature information of the historical test case and a defect discovery rate of the historical test case; according to the training sample, in combination with other participants, performing model training based on transverse federated learning, wherein a finally obtained test case scoring model is used for predicting a corresponding defect discovery rate according to feature information of a plurality of alternative test cases, and selecting an actual test case from the plurality of alternative test cases according to the predicted defect discovery rate. The model is trained based on a large amount of sample data by combining a plurality of participants, the method can be used for predicting the defect discovery rate of the test case, effective reference is provided for selection of the test case, the test case selection efficiency is improved, and the software test efficiency is improved.

The invention belongs to the technical field of data collection of distributed privacy protection, and particularly relates to a distributed differential privacy aggregation method. The method comprises the following steps: distributed implementation is performed on a centralized differential privacy algorithm in a mode of adding noise at a user side on the basis that the centralized differential privacy algorithm obeys the additive property of the distribution; in the process that a user sends data and aggregates the data, by using a secure multi-party computing method, the user sends data randomized through a differential privacy algorithm to all aggregators in a secret sharing mode, and then all the aggregators interact to obtain a final randomized aggregation result. According to the accurate and efficient distributed differential privacy aggregation method provided by the invention, a differential privacy algorithm is realized in a distributed manner, so that the collection accuracy of distributed privacy protection data is improved, and under the trust hypothesis that a data collection party is not trusted, the security of intermediate data of a user is ensured through secure multi-party calculation, and the security data aggregation efficiency is improved.

The embodiment of the invention discloses a data storage method, a device and electronic equipment. The electronic device is configured with a trusted execution environment. At least one safety application is running in the trusted execution environment. In this method, firstly, a data storage request from a service application is obtained; wherein the data storage request comprises service data to be stored; according to the method, the service data is encrypted based on the safety application in the trusted execution environment of the electronic equipment to generate the corresponding encrypted service data, and then the encrypted service data is stored in the service storage space of the electronic equipment, so that the safety storage of the terminal data can be realized, and the safety of user data or privacy can be protected.

The embodiment of the invention provides a manifest report setting method, which comprises the following steps: configuring a manifest report which is used for storing a plurality of records of a plurality of users, and each record comprising a plurality of data items corresponding to a plurality of fields; obtaining permission setting information of a target user; according to the permission setting information, selecting at least one field from the plurality of field middle sections as a permission distinguishing field; establishing a mapping relationship between the target user and the authority distinguishing field values of the authority distinguishing fields; and storing the mapping relationship, so that the target user obtains a target data item in the permission range of the targetuser from the list report according to the mapping relationship. According to the embodiment, the list report is configured by the list with the same logic, so that the configuration efficiency and the subsequent maintenance efficiency are improved. Meanwhile, the data downloading burden is reduced, unnecessary links such as screening are removed, and the data privacy of other institutions is effectively guaranteed.

The invention discloses a model training method and system based on federated learning. The method includes: a central server sends parameters to each node device; each node device performs neural network model training based on the parameters and a local data set, and obtains a gradient value; Each node device adds noise to their respective gradient values, obtains the noised gradient value and sends it to the central server, and the central server calculates the sum of the noised gradient values; Sent to the central server; the central server calculates and obtains the sum of gradient values ​​according to the sum of the noise-added gradient values ​​and the sum of the noise values, and obtains updated parameters based on the sum of the gradient values ​​and updates its own neural network model. The parameters are distributed to each node device; this method can ensure the model training accuracy while protecting the private data of the participants.