Watermarking Decision Tree Ensembles
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929529987006464 |
|---|---|
| author | Calzavara, Stefano Cazzaro, Lorenzo Gera, Donald Orlando, Salvatore |
| author_facet | Calzavara, Stefano Cazzaro, Lorenzo Gera, Donald Orlando, Salvatore |
| contents | Protecting the intellectual property of machine learning models is a hot topic and many watermarking schemes for deep neural networks have been proposed in the literature. Unfortunately, prior work largely neglected the investigation of watermarking techniques for other types of models, including decision tree ensembles, which are a state-of-the-art model for classification tasks on non-perceptual data. In this paper, we present the first watermarking scheme designed for decision tree ensembles, focusing in particular on random forest models. We discuss watermark creation and verification, presenting a thorough security analysis with respect to possible attacks. We finally perform an experimental evaluation of the proposed scheme, showing excellent results in terms of accuracy and security against the most relevant threats. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04570 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Watermarking Decision Tree Ensembles Calzavara, Stefano Cazzaro, Lorenzo Gera, Donald Orlando, Salvatore Machine Learning Cryptography and Security Multimedia Protecting the intellectual property of machine learning models is a hot topic and many watermarking schemes for deep neural networks have been proposed in the literature. Unfortunately, prior work largely neglected the investigation of watermarking techniques for other types of models, including decision tree ensembles, which are a state-of-the-art model for classification tasks on non-perceptual data. In this paper, we present the first watermarking scheme designed for decision tree ensembles, focusing in particular on random forest models. We discuss watermark creation and verification, presenting a thorough security analysis with respect to possible attacks. We finally perform an experimental evaluation of the proposed scheme, showing excellent results in terms of accuracy and security against the most relevant threats. |
| title | Watermarking Decision Tree Ensembles |
| topic | Machine Learning Cryptography and Security Multimedia |
| url | https://arxiv.org/abs/2410.04570 |