Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Yangyu Tao

Yong Cheng

Pin Xiao

Fangcheng Fu

Bo Li

Yuhong Liu

Jie Jiang

Chen Hou

Jie Xu

Xiaosen Li

Wen Ouyang

Junwei Pan

Xiaoliang Fan

Huanran Xue

Embodiments of this application provide a training method, apparatus, and device for a federated neural network model, a computer program product, and a computer-readable storage medium. The method includes: performing forward computation processing of sample data through a first model, to obtain an output vector of the first model; performing forward computation processing through the interaction layer of the output vector of the first bottom model, at last one model parameter of an interaction layer, and at least one encrypted model to obtain an output vector of the interaction layer; performing forward computation processing on the output vector of the interaction layer through the second model, to obtain an output vector of the federated neural network model; back propagating the federated neural network model according to a loss result corresponding to the output vector of the federated neural network model, and updating the at least one model parameter of the interaction layer, a parameter of the first bottom model, and a parameter of the top model according to a back propagation processing result; and obtaining a trained federated neural network model based on the updated model parameters of the interaction layer, the first bottom model, and the second model.

Training method, apparatus, and device for federated neural network model, computer program product, and computer-readable storage medium

A data processing method includes: acquiring a first intersection set, acquiring a second intersection set, calculating an intersection between the first intersection set and the second intersection set to obtain an intersection result set that includes an intersecting portion of the first intersection data and the second intersection data, and obfuscating the intersection result set to obtain an obfuscation set that includes obfuscated data based on data in the second intersection set and an intersection data set based on the intersection result set.

Data processing method and apparatus, device, and computer-readable storage medium

A method for federated data query includes: using, by a first electronic device, a joint query statement corresponding to a federated data query to obtain at least one operator containing a joint query operator; transforming the joint query operator to a first joint security operator; determining first data for intersection on the first electronic device by executing the first joint security operator, receiving second data for intersection from a second electronic device involved in the federated data query, and performing joint computing on the first data for intersection and the second data for intersection in a form of a ciphertext to obtain a joint data table; and determining a federated data query result corresponding to the at least one operator based on the joint data table.

Method and apparatus for federated data query, electronic device, and computer-readable storage medium

This disclosure relates to a method for vertical federated learning. In multiple participation nodes deployed in a multi-way tree topology, an upper-layer participation node corresponds to k lower-layer participation nodes. After the upper-layer participation node and the k lower-layer participation nodes exchange public keys with each other, the upper-layer participation node performs secure two-party joint computation with the lower-layer participation nodes with a first public key and second public keys as encryption parameters to obtain k two-party joint outputs of a federated model. Further, the upper-layer participation node aggregates the k two-party joint outputs to obtain a first joint model output corresponding to the federated model. As such, a multi-way tree topology deployment-based vertical federated learning architecture is provided, improving the equality of each participation node in a vertical federated learning process.

Method and apparatus for vertical federated learning

A secure multi-party computation method and apparatus, a device, and a storage medium are provided, which belong to the field of data security technologies. The method includes: performing homomorphic encryption processing on first plaintext data to generate first ciphertext data; transmitting encrypted data containing the first ciphertext data to a second node device; receiving difference ciphertext data transmitted by the second node device; and decrypting the difference ciphertext data to obtain a positive and negative property of a difference between the first plaintext data and the second plaintext data. The foregoing method and apparatus, device, and storage medium are conducive to reducing the number of times of communications of secure multi-party computation, have low communication overhead and high computation efficiency, and enable magnitude comparison and equality testing to be performed simultaneously.

Secure multi-party computation method and apparatus, device, and storage medium

This application belongs to the technical field of artificial intelligence, and particularly relates to a data processing method, a data processing apparatus, a computer-readable medium, and an electronic device. The method includes acquiring a relationship graph network, the relationship graph network comprising nodes for representing interacting objects and edges for representing interactive relationships between multiple interacting objects; performing core degree mining on the relationship graph network through a device cluster comprising multiple computing devices, and iteratively updating node core degrees of all or some of the nodes in the relationship graph network; pruning the relationship graph network according to the node core degrees to remove some of the nodes and edges in the relationship graph network; and compressing the device cluster to remove some of the computing devices in the device cluster, when a network scale of the relationship graph network satisfies a network compression condition.

Data processing method and apparatus, computerreadable medium, and electronic device

Provided is a graph data processing method, including: acquiring a degree of association of each node in a network graph; splitting the network graph to obtain a dense subgraph according to the degree of association of each node; determining stable nodes in the network graph and coreness of the stable nodes based on the dense subgraph, the coreness of the stable nodes being greater than a preset threshold; obtaining a sparse subgraph in the network graph according to the preset threshold, and remaining nodes in the network graph other than the stable nodes and connecting edges between the remaining nodes; and determining the coreness of each node in the sparse subgraph based on the sparse subgraph and the stable nodes.

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Shenzhen, China

Yangyu Tao