公开(公告)号：US20240061955A1

公开(公告)日：2024-02-22

申请号：US18260776

申请日：2021-01-08

Applicant: AGENCY FOR SCIENCE, TECHNOLOGY AND RESEARCH

Inventor： Chao JIN ,  Fook Mun CHAN ,  Khin Mi Mi AUNG

IPC: G06F21/62 ,  H04L9/00

CPC classification number: G06F21/6245 ,  H04L9/008

Abstract: There is provided a method of privacy-preserving logistic regression training based on homomorphically encrypted ciphertexts. The method includes: obtaining a first packed ciphertext comprising at least a portion of a first training data sample packed into a first vector of slots thereof for training a privacy-preserving logistic regression model; obtaining a second packed ciphertext comprising a plurality of weights of the privacy-preserving logistic regression model packed into a first vector of slots thereof; determining at least a first output probability of the privacy-preserving logistic regression model based on the first packed ciphertext and the second packed ciphertext; and updating the plurality of weights based on the first output probability. There is also provided a corresponding system for privacy-preserving logistic regression training based on homomorphically encrypted data.