Feature Engineering Framework based on Secure Multi-Party Computation in Federated Learning
Litong Sun (East China Normal University)
Runmeng Du (East China Normal University)
Daojing He (East China Normal University)
Shanshan Zhu (East China Normal University)
R. Wang (TU Delft - Integral Design & Management)
Sammy Chan (City University of Hong Kong)
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Abstract
Data and features often determine the upper limit of results, so that feature engineering is an important stage of federated learning. The existing research schemes all carry out feature engineering based on publicly sharing data. One is plaintext data sharing, the other is ciphertext data sharing, but both types of sharing bring security and efficiency problems. To address these challenges, we propose a feature engineering framework based on Secure Multi-party Computation, which supports multi-party participation in feature engineering and confines feature data locally to ensure data security. Moreover, the computational efficiency of the core algorithm of the framework is also improved compared with the existing methods.