SECURE MULTI-PARTY COMPUTATION PROTOCOLS FOR PRIVACY-PRESERVING GENOMIC DATA ANALYSIS
Subjects/Theme:
Secure Multi-Party Computation, Genomic Data Privacy, GWAS, Homomorphic Encryption, Secret Sharing, Privacy-Preserving Analytics, Cryptography, Healthcare Data SecurityDescription
Security and Privacy in AI Systems,
Edited By: Dr. Sunita Chaudhary, Dr. Joydeb Patra
ISBN (978-81-685212-9-2)
The rapid advancement of genomic sequencing technologies has enabled large-scale data-driven discoveries in healthcare, personalized medicine, and disease prediction. However, genomic data is inherently sensitive, containing uniquely identifiable and hereditary information, raising serious privacy concerns. Secure Multi-Party Computation (SMPC) provides a cryptographic framework that allows multiple parties to jointly compute a function over their inputs without revealing the inputs themselves. This paper explores SMPC protocols for privacy-preserving genomic data analysis, focusing on secure statistical computations, genome-wide association studies (GWAS), and collaborative analytics across institutions. We evaluate protocols such as secret sharing, garbled circuits, and homomorphic encryption in terms of efficiency, scalability, and security. Experimental analysis demonstrates that hybrid SMPC approaches offer a practical balance between computational overhead and privacy guarantees, making them suitable for real-world genomic applications.
