Improved Privacy-Preserving PCA Using Optimized Homomorphic Matrix Multiplication
This addresses privacy concerns in cloud computing for machine learning and data analysis, but it is incremental as it builds on existing homomorphic encryption techniques.
The paper tackled the problem of inefficient and inaccurate privacy-preserving PCA using homomorphic encryption by proposing a novel approach that improves efficiency, accuracy, and scalability compared to prior methods.
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 the secure cloud computing scenario. 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