التفاصيل البيبلوغرافية
| العنوان: |
A Distributed and Privacy-Preserving Random Forest Evaluation Scheme with Fine Grained Access Control |
| المؤلفون: |
Yang Zhou, Hua Shen, Mingwu Zhang |
| المصدر: |
Symmetry; Volume 14; Issue 2; Pages: 415 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2022 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
privacy-preserving, robustness, fine grained access control, random forest, ensemble learning |
| الوصف: |
Random forest is a simple and effective model for ensemble learning with wide potential applications. Implementation of random forest evaluations while preserving privacy for the source data is demanding but also challenging. In this paper, we propose a practical and fault-tolerant privacy-preserving random forest evaluation scheme based on asymmetric encryption. The user can use asymmetric encryption to encrypt the data outsourced to the cloud platform and specify who can access the final evaluation results. After receiving the encrypted inputs from the user, the cloud platform evaluates via a random forest model and outputs the aggregated results where only the designated recipient can decrypt them. Threat analyses prove that the proposed scheme achieves the desirable security properties, such as correctness, confidentiality and robustness. Moreover, efficiency analyses demonstrate that the scheme is practical for real-world applications. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
Computer and Engineering Science and Symmetry/Asymmetry; https://dx.doi.org/10.3390/sym14020415 |
| DOI: |
10.3390/sym14020415 |
| الاتاحة: |
https://doi.org/10.3390/sym14020415 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.5E924DE7 |
| قاعدة البيانات: |
BASE |