公开(公告)号：US11449639B2

公开(公告)日：2022-09-20

申请号：US16442336

申请日：2019-06-14

Applicant: SAP SE

Inventor： Daniel Bernau ,  Jonas Robl ,  Philip-William Grassal ,  Florian Kerschbaum

IPC: G06F21/62 ,  G06N3/04 ,  G06N20/00

Abstract: Machine learning model data privacy can be maintained by training a machine learning model forming part of a data science process using data anonymized using each of two or more differential privacy mechanisms. Thereafter, it is determined, for each of the two or more differential privacy mechanisms, a level of accuracy and a level precision when evaluating data with known classifications. Subsequently, using the respective determined levels of precision and accuracy, a mitigation efficiency ratio is determined for each of the two or more differential privacy mechanisms. The differential privacy mechanism having a highest mitigation efficiency ratio is then incorporated into the data science process. Related apparatus, systems, techniques and articles are also described.