Arithmetic PCA for Encrypted Data

ePrint Report: Arithmetic PCA for Encrypted Data

Jung Hee Cheon, Hyeongmin Choe, Saebyul Jung, Duhyeong Kim, Dah Hoon Lee, Jai Hyun Park

Reducing the size of large dimensional data is a critical task in machine learning (ML) that often involves using principal component analysis (PCA). In privacy-preserving ML, data confidentiality is of utmost importance, and reducing data size is a crucial way to cut overall costs.


This work focuses on minimizing the number of normalization processes in the PCA algorithm, …

analysis confidentiality critical data encrypted encrypted data eprint report kim large lee machine machine learning privacy report size task