IACR News item: 09 October 2023
Jung Hee Cheon, Hyeongmin Choe, Saebyul Jung, Duhyeong Kim, Dah Hoon Lee, Jai Hyun Park
ePrint Report
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, which is a costly procedure in encrypted PCA. By modifying Krasulina's algorithm, non-polynomial operations were eliminated, except for a single delayed normalization at the end.
Our PCA algorithm demonstrated similar performance to conventional PCA algorithms in face recognition applications. We also implemented it using the CKKS (Cheon-Kim-Kim-Song) homomorphic encryption scheme and obtained the first 6 principal components of a 128$\times$128 real matrix in 7.85 minutes using 8 threads.
This work focuses on minimizing the number of normalization processes in the PCA algorithm, which is a costly procedure in encrypted PCA. By modifying Krasulina's algorithm, non-polynomial operations were eliminated, except for a single delayed normalization at the end.
Our PCA algorithm demonstrated similar performance to conventional PCA algorithms in face recognition applications. We also implemented it using the CKKS (Cheon-Kim-Kim-Song) homomorphic encryption scheme and obtained the first 6 principal components of a 128$\times$128 real matrix in 7.85 minutes using 8 threads.
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