EVALUATING NOISE MANAGEMENT APPROACHES IN HOMOMORPHIC ENCRYPTION

Abstract

Homomorphic encryption plays a crucial role in secure computation, enabling computations on encrypted data without the need for decryption. However, the presence of noise in ciphertexts poses a significant challenge in practical applications. This noise, which masks the plaintext, can accumulate during homomorphic operations, leading to decryption failures when it exceeds a certain threshold. Therefore, effectively managing noise becomes essential for the practicability of homomorphic encryption.

This paper presents an overview of the different types of noise encountered during homomorphic operations and explores various noise management strategies proposed by researchers between 2009 and 2016. Among these strategies, bootstrapping emerges as the most widely adopted technique for reducing noise in ciphertexts. Bootstrapping involves homomorphically evaluating the decryption circuit, thereby reducing the size of the noise and enabling successful decryption.

To address the noise accumulation issue, this paper discusses the impact of circuit depth on noise growth. As computations become more complex, the noise tends to increase, making noise management even more critical. By examining the existing noise management techniques, this paper aims to provide insights into effective noise control and mitigate the limitations of homomorphic encryption.

Furthermore, this study sheds light on the potential implications and challenges associated with noise management in practical applications of homomorphic encryption. It highlights the need for further research to develop novel noise reduction techniques that can improve the efficiency and scalability of homomorphic encryption schemes.