Privacy-Preserving Data Mining Techniques using Homomorphic Encryption Quantum-Resistant Encryption Algorithms for Secure Communications

Abstract

: In recent years, the rapid growth of data generation and communication networks has highlighted the critical need for secure and privacy-preserving data mining techniques. Homomorphic encryption (HE) has emerged as a powerful cryptographic tool enabling computations on encrypted data without revealing the underlying sensitive information. This property allows data owners and miners to collaboratively extract useful patterns while preserving privacy. This paper investigates advanced privacy-preserving data mining techniques leveraging homomorphic encryption to perform secure computations on large datasets. We analyze the strengths and limitations of various homomorphic encryption schemes, including partially, somewhat, and fully homomorphic encryption, focusing on their computational efficiency, security guarantees, and applicability in real-world mining scenarios. Additionally, with the advent of quantum computing posing threats to classical cryptographic systems, quantum-resistant encryption algorithms have gained importance to ensure long-term security in communication. This study explores several quantum-resistant algorithms such as lattice-based, code-based, and hash-based cryptosystems, evaluating their potential for securing data transmission against quantum attacks. By integrating homomorphic encryption with quantum-resistant algorithms, we propose a hybrid framework aimed at enhancing both privacy preservation during data mining and secure communication. Experimental results demonstrate that the proposed framework maintains data confidentiality with acceptable computational overhead and robust resistance to quantum adversaries. This paper contributes to the understanding of practical privacy-preserving methods in the era of emerging quantum technologies and highlights future directions for scalable, efficient, and quantum-secure data mining frameworks.