A systemic comparison comparison of concurrent multiparty secret sharing with SGD regression and classification
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In recent years Secure Multiparty Computation (MPC) via Secret Sharing has emerged as a key intersection between the fields of cryptography and machine learning. MPC's ability to allow N-number of participants to hide their inputs while able to share their outputs has presented itself as a solution for creating machine learning models for datasets with sensitive information such as those used in healthcare. Prior to or without MPC creating these models required that the information in these datasets would have to be shared with all participants in cleartext which then could be stolen or misused by dishonest participants or even in transit between the parties. As the demand for answers from machine learning models grows so does the need for privacy and confidentiality for data in those models. But MPC does have some pitfalls with both an increase in the cost of computations of secret shares and the increase in the amount of data transferred between the participating parties. In the context of my research, I Introduce A Systemic Comparison of Concurrent Multiparty Secret Sharing with SGD Regression and Classification in which I create and run experiments that compares different methods of MPC and different machine learning algorithms. The results of the experiments provide valuable insights into the practical viability of secure multiparty computation for machine learning applications. By comparing the performance of different secure computation methods, this research contributes to the understanding of the trade-offs between security and efficiency.
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M.S.