Abstract

Fully Homomorphic Encryption (FHE) permits processing information in the form of ciphertexts without decryption. It can ensure the security of information in common technologies used today, such as cloud computing, the Internet of Things, and machine learning, among others. A primary disadvantage for its practical application is the low efficiency of sign and comparison operations. Several FHE schemes use the Residue Number System (RNS) to decrease the time complexity of these operations. Converting from the RNS to the positional number system and calculating the positional characteristic of a number are standard approaches for both operations in the RNS domain. In this paper, we propose a new method for comparing numbers and determining the sign of a number in RNS. We focus on the even ranges that are computationally simple due to their peculiarities. We compare the performance of several state-of-art algorithms based on an implementation in C++ and relatively simple moduli with a bit depth from 24 to 64 bits. The experimental analysis shows a better performance of our approach for all the test cases; it improves the sign detection between 1.93 and 15.3 times and the number comparison within 1.55–11.35 times with respect to all the methods and configurations.

Highlights

  • Number comparison and sign detection are simple and basic operations for calculation in computing systems

  • Determining the Sign of a Number (DSN) in Residue Number System (RNS) is based on the fact that the residues xi of the negative number X are the complement of xi to the modulus pi

  • The formula based on Chinese Remainder Theorem (CRT) and an Approximate Method (AM) allows determining the sign of the number based on the following equation [29]

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Summary

Introduction

Number comparison and sign detection are simple and basic operations for calculation in computing systems. Various methods are used to reduce their computational complexity, such as the Pirlo and Impedovo function [1], Diagonal Function (DF) [2,3], Modified Diagonal Function (MDF) [4], Approximate Method (AM) [5,6], and Core Function (CF) [7] Both operations are fundamental for the applicability of RNS for both hardware and software implementations. Many methods in RNS work using this property They are often ineffective, since they apply division by large numbers or use computationally complex algorithms to calculate the positional characteristic of the number.

Residue Number System
Mixed Radix Conversion Method
Approximate Method
Diagonal Function
Core Function
Determining the Sign of a Number
Modified Diagonal Function
Determining the Sign of a Number in RNS with an Even Range
Performance Evaluation
Method
34 Maximum MMaexainmum
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