A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917823466438656 |
|---|---|
| author | Wang, Huan-Chih Wu, Ja-Ling |
| author_facet | Wang, Huan-Chih Wu, Ja-Ling |
| contents | After entering the era of big data, more and more companies build services with machine learning techniques. However, it is costly for companies to collect data and extract helpful handcraft features on their own. Although it is a way to combine with other companies' data for boosting the model's performance, this approach may be prohibited by laws. In other words, finding the balance between sharing data with others and keeping data from privacy leakage is a crucial topic worthy of close attention. This paper focuses on distributed data and conducts secure model training tasks on a vertical federated learning scheme. Here, secure implies that the whole process is executed in the encrypted domain. Therefore, the privacy concern is released. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_22960 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example Wang, Huan-Chih Wu, Ja-Ling Cryptography and Security Machine Learning After entering the era of big data, more and more companies build services with machine learning techniques. However, it is costly for companies to collect data and extract helpful handcraft features on their own. Although it is a way to combine with other companies' data for boosting the model's performance, this approach may be prohibited by laws. In other words, finding the balance between sharing data with others and keeping data from privacy leakage is a crucial topic worthy of close attention. This paper focuses on distributed data and conducts secure model training tasks on a vertical federated learning scheme. Here, secure implies that the whole process is executed in the encrypted domain. Therefore, the privacy concern is released. |
| title | A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2410.22960 |