Improved privacy-preserving PCA using optimized homomorphic matrix multiplication

doi:10.1016/j.cose.2023.103658

Improved privacy-preserving PCA using optimized homomorphic matrix multiplication

2024-03-01

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DOI：10.1016/j.cose.2023.103658

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Principal Component Analysis (PCA) is a pivotal technique widely utilized in the realms of machine learning and data analysis. It aims to reduce the dimensionality of a dataset while minimizing the loss of information. In recent years, there have been endeavors to utilize homomorphic encryption in privacy-preserving PCA algorithms for secure cloud computing. These approaches commonly employ a PCA routine known as PowerMethod, which takes the covariance matrix as input and generates an approximate eigenvector corresponding to the primary component of the dataset. However, their performance is constrained by the absence of an efficient homomorphic covariance matrix computation circuit and an accurate homomorphic vector normalization strategy in the PowerMethod algorithm. In this study, we propose a novel approach to privacy-preserving PCA that addresses these limitations, resulting in superior efficiency, accuracy, and scalability compared to previous approaches. We attain such efficiency and precision through the following contributions: (i) We implement space and speed optimization techniques for a homomorphic matrix multiplication method, specifically tailored for parallel computing scenarios. (ii) Leveraging the benefits of this optimized matrix multiplication, we devise an efficient homomorphic circuit for computing the covariance matrix homomorphically. (iii) Utilizing the covariance matrix, we develop a novel and efficient homomorphic circuit for the PowerMethod that incorporates a universal homomorphic vector normalization strategy to enhance both its accuracy and practicality. Our privacy-preserving PCA scheme, implemented using our innovative homomorphic PowerMethod circuit, surpasses the state-of-the-art approach with an average speedup of 1.9 times on datasets with size 200 x 256. Notably, our scheme demonstrates an even more remarkable estimated speedup of 25 times when applied to larger datasets of size 60000 x 256, along with an R2 accuracy improvement of up to 0.285, showcasing efficiency and accuracy that has not been reported by previous approaches.

Keyword:

Privacy-preserving PCA

CKKS

Homomrophic matrix multiplication

Cloud computing

Machine learning as a service

Improved privacy-preserving PCA using optimized homomorphic matrix multiplication

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