Ontology highlight
ABSTRACT:
SUBMITTER: Shen X
PROVIDER: S-EPMC9997048 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Data perturbation is a technique for generating synthetic data by adding "noise" to raw data, which has an array of applications in science and engineering, primarily in data security and privacy. One challenge for data perturbation is that it usually produces synthetic data resulting in information loss at the expense of privacy protection. The information loss, in turn, renders the accuracy loss for any statistical or machine learning method based on the synthetic data, weakening downstream an ...[more]