Classical machine learning modeling demands considerable computing power for internal
calculations and training with big data in a reasonable amount of time. In recent years …

The secure and efficient processing of private information in the cloud computing paradigm
is still an open issue. New security threats arise with the increasing volume of data into cloud …

Homomorphic encryption (HE) is a promising solution for handling sensitive data in semi-
trusted third-party computing environments, as it enables processing of encrypted data …

Ensuring reliable data storage in a cloud environment is a challenging problem. One of the
efficient mechanisms used to solve it is the Redundant Residue Number System (RRNS) …

The security issues that arise in public cloud environments raise several concerns about
privacy-preserving. Conventional security practices successfully protect stored and …

The residue number system (RNS) is widely used in different areas due to the efficiency of
modular addition and multiplication operations. However, non-modular operations, such as …

The security of data storage, transmission, and processing is emerging as an important
consideration in many data analytics techniques and technologies. For instance, in machine …

Fully Homomorphic Encryption (FHE) permits processing information in the form of
ciphertexts without decryption. It can ensure the security of information in common …

The protection of data processing is emerging as an essential aspect of data analytics,
machine learning, delegation of computation, Internet of Things, medical and financial …

Homomorphic encryption with the ability to compute over encrypted data without access to
the secret key provides benefits for the secure and powerful computation, storage, and …