PPMO-AHE: Efficient Merge Operations for Encrypted Data Using Additive Homomorphism

This study introduces a novel privacy-preserving methodology for executing merge operations on encrypted data, utilizing the principles of additively homomorphic encryption (AHE). The motivation behind this research is the growing need to securely integrate sensitive data from multiple sources without compromising individual privacy. Traditional methods for data merging often fall short in preserving privacy when handling encrypted data, leading to potential security vulnera-bilities. In contrast, our proposed work leverages AHE to enable computations directly on encrypted datasets, ensuring that the confidentiality of the data is maintained throughout the merging process. We evaluated our approach on a dataset comprising 100,000 encrypted records. The experimental setup was designed to assess both the efficiency and privacy preservation capabilities of our method. Results indicated that our approach achieved an average merge time of 0.5 seconds per record, highlighting its practicality for real-world applications. Moreover, the privacy analysis confirmed that the methodology effectively safeguards the anonymity of individual data contributors, with no evidence of information leakage. In conclusion, the findings from this study underscore the viability and effectiveness of using AHE for privacy-preserving merge operations on encrypted data. Our research contributes significantly to the field by presenting a secure, efficient solution that respects individual privacy, thereby facilitating the safe integration of sensitive datasets.