SECURE MULTIPARTY COMPUTATION AND HOMOMORPHIC ENCRYPTION FOR PRIVACYPRESERVING DATA ANALYSIS

In order to enable collaborative computation while protecting data privacy, secure multiparty computation (MPC) and homomorphic encryption are two essential strategies in privacy-preserving data analysis. Without disclosing sensitive information, MPC enables many participants to collaboratively compute functions on their private inputs. The confidentiality of the data is maintained throughout the study thanks to homomorphic encryption, which enables computations to be done directly on encrypted data without decryption. The discussion of secure multiparty computation and homomorphic encryption in this study emphasises the importance of these ideas for privacy-preserving data analysis. It examines safe outsourcing, cooperative machine learning, and cloud computing as examples of privacy-preserving data analysis methods that use MPC and homomorphic encryption. The study also examines use applications and possible developments in the area, including standardisation, efficiency optimisation, privacypreserving machine learning, and practical implementations. Researchers and practitioners can use MPC and homomorphic encryption to analyse sensitive data while protecting individual privacy by comprehending their strengths and weaknesses