The increasing availability of genomic data for research purposes necessitates innovative approaches to ensure privacy while facilitating collaborative analysis. This study explores the integration of a variant of Secure Multi-Party Computation (SMPC) techniques into genomic data analysis. The conventional challenges of sharing sensitive genetic information among multiple entities, such as research institutions or healthcare providers, are addressed by leveraging advanced cryptographic protocols. The research focuses on the development and implementation of a secure framework for collaborative genomic data analysis using an adapted SMPC variant. This variant is designed to efficiently handle the complexities of genetic data while ensuring robust privacy preservation. By encrypting individual genomic inputs and enabling computations without revealing the raw data, the proposed SMPC variant facilitates joint analyses, contributing to advancements in personalized medicine, disease research, and genetic epidemiology. The variants of SMPC, namely oblivious transfer protocol, is used, this allows the receiver to obtain one out of several pieces of information forwarded by the sender without revealing which one they obtained. It can be integrated into SMPC protocols for enhancing the privacy with less effort and cost. The proposed mechanism involves the validation of the SMPC variant through simulations using real-world genomic datasets and assessing its performance in terms of computational efficiency and privacy preservation. Results from experiments demonstrate the feasibility and effectiveness of the proposed technique in enabling secure multi-party genomic data analysis. This research contributes to the evolving landscape of privacy-preserving techniques in genomics, offering a promising avenue for collaborative research without compromising the confidentiality of sensitive genetic information.

Secure Multi-Party Privacy Genetic Attack Security