Fully homomorphic encryption allows to perform arbitrary computation over encrypted data
which has great practical implications in the secure outsourced computation on an untrusted …

Abstract 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 …

As widespread development of biometrics, concerns about security and privacy are rapidly
increasing. Secure matrix computation is one of the most fundamental and useful operations …

We present a novel construction that enables two parties to securely train a logistic
regression model on private secret-shared data. Our goal is to minimize online …

Principal component analysis (PCA) is one of the most popular linear dimensionality
reduction techniques in machine learning. In this paper, we try to present a method for …

Fully homomorphic encryption allows a third-party to perform arbitrary computation over
encrypted data and is especially suitable for secure outsourced computation. This paper …

Fully homomorphic encryption enables to perform arbitrary computation over encrypted
data, providing a powerful tool for secure outsourced computation on an untrusted …

Despite the enormous technical and financial advantages of cloud computing, security and
privacy have always been the primary concerns for adopting cloud computing facilities …

Principal component analysis (PCA) is one of the most pop-ular linear dimensionality
reduction techniques in machine learning. Inthis paper, we try to present a method for …

Homomorphic Encryption (HE) is one of the mainstream cryptographic tools used to enable
secure outsourced computation. A typical task is secure matrix computation. Popular HE …