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.
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE 23rd International Conference on High Performance Computing & Communications, 7th International Conference on Data Science & Systems, 19th International Conference on Smart City and 7th International Conference on Dependability in Sensor, Cloud & Big Data Systems & Applications, HPCC-DSS-SmartCity-DependSys 2021 |
| Subtitle of host publication | PROCEEDINGS |
| Publisher | IEEE |
| Pages | 487-494 |
| ISBN (Electronic) | 978-1-6654-9457-1 |
| ISBN (Print) | 978-1-6654-9458-8 |
| DOIs | |
| Publication status | Published - Dec 2021 |
| Event | 23rd IEEE International Conference on High Performance Computing and Communications (HPCC 2021) - Hybrid, Haikou, China Duration: 20 Dec 2021 → 22 Dec 2021 https://dsg.tuwien.ac.at/team/sd/papers/Booklet_HIC_2021.pdf |
Publication series
| Name | 2021 IEEE 23rd International Conference on High Performance Computing and Communications, 7th International Conference on Data Science and Systems, 19th International Conference on Smart City and 7th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021 |
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Conference
| Conference | 23rd IEEE International Conference on High Performance Computing and Communications (HPCC 2021) |
|---|---|
| Place | China |
| City | Haikou |
| Period | 20/12/21 → 22/12/21 |
| Internet address |
Bibliographical note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).Research Keywords
- Feature Engineering
- Federated Learning
- Privacy Protection
- Secure Multi-party Computation
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