Integrating Secure Multiparty Computation with AI for Privacy-Aware Group Data Analysis

Abstract

Big data, especially sensitive data, needs privacy-preserving cooperative data analysis. Secure Multiparty Computation (SMPC) enables participants assess data without revealing private inputs. The computational complexity and restrictions of SMPC may limit its scalability and efficiency. AI's data processing may improve SMPC's scalability, efficiency, and applicability. This study analyzes how SMPC and AI can help privacy-conscious group data analysis. Using machine learning and optimization, AI may optimize SMPC protocols for faster processing, larger datasets, and privacy. Technology problems, future research, and real-world applications are covered in this paper.