Lemon Inc.

Xin Yang

Chong Wang

Jiankai Sun

Junyuan Xie

Yuanshun Yao

Weihao Gao

Aonan Zhang

Tianyi Liu

Ruihan Wu

The present disclosure relates to a data protection method, apparatus, medium and electronic device. The method comprises: obtaining a specified batch of reference samples of an active participant of a joint training model; determining generation gradient information of the first reference sample; determining target gradient information sent to the passive participant according to the generation gradient information, and sending the target gradient information to the passive participant, to update, by the passive participant, parameters of the joint training model according to the target gradient information. Through the above solution, the influence of the generated data on the training process and model performance of the joint training model is avoided as much as possible, and the privacy and security of data are improved.

Data protection method, apparatus, medium and electronic device

The present disclosure describes techniques of releasing data while protecting individual privacy. A dataset may be compressed by applying a first random matrix. The dataset may be owned by a party among a plurality of parties and there may be a plurality of datasets owned by the plurality of parties. A noise may be added by applying a random Gaussian matrix to the compressed dataset to obtain a processed dataset. The processed dataset ensures data privacy protection. The processed dataset may be released to other parties.

Data processing for release while protecting individual privacy

There are proposed a method, device, apparatus, and medium for protecting sensitive data. In a method, to-be-processed data is received from a server device. A processing result of a user for the to-be-processed data is received, the processing result comprising sensitive data of the user for the processing of the to-be-processed data. A gradient for training a server model at the server device is determined based on a comparison between the processing result and a prediction result for the to-be-processed data. The gradient is updated in a change direction associated with the gradient so as to generate an updated gradient to be sent to the server device. Noise is added only in the change direction associated with the gradient. The corresponding overhead of processing noise in a plurality of directions can be reduced, and no excessive noise data interfering with training will be introduced to the updated gradient.

Method, apparatus, device and medium for protecting sensitive data

Growing community of inventors

Los Angeles, CA, United States of America

Xin Yang