PRIVACY AND MODELING PRESERVED DATA SHARING

摘要

A method for generating a classification model using original data that is sensitive or private to a data owner. The method includes: receiving, from one or more entities, a masked data set having masked data corresponding to the original sensitive data, and further including a masked feature label set for use in classifying the masked data contents; forming a shared data collection of the masked data and the masked feature label sets received; and training, by a second entity, a classification model from the shared masked data and feature label sets, wherein the classification model learned from the shared masked data and feature label sets is the same as a classification model learned from the original sensitive data. The sensitive features and labels cannot be reliably recovered even when both the masked data and the learning algorithm are known.

主权项

1. A method for generating a classification model using original data that is sensitive or private to a data owner comprising:
receiving, from one or more first entities, a masked data set, each data set from an entity having masked data corresponding to the original sensitive data, the masked data set further including a masked feature label set for use in classifying the masked data contents; forming a shared data collection of said masked data and the masked feature label sets received from said first entities; and training, by a second entity, a classification model from the shared masked data and feature label sets, said model being a classification model configured to classify original sensitive data contained in masked data sets received from said entities, wherein the classification model learned from the shared masked data and feature label sets is the same as the model learned from the original sensitive data.