Name
Title | ||
|---|---|---|
Feature Engineering Framework based on Secure Multi-Party Computation in Federated Learning |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00088 | 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys) |
Keywords | DocType | ISBN |
Feature Engineering,Federated Learning,Secure Multi-party Computation,Privacy Protection | Conference | 978-1-6654-9458-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Litong Sun | 1 | 0 | 0.34 |
| Runmeng Du | 2 | 0 | 0.34 |
| Daojing He | 3 | 1013 | 58.40 |
| Shanshan Zhu | 4 | 0 | 0.34 |
| Rui Wang | 5 | 0 | 0.34 |
| Sammy Chan | 6 | 902 | 66.93 |