Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.05092 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916524450643968 |
|---|---|
| author | Sepehri, Yamin Pad, Pedram Frossard, Pascal Dunbar, L. Andrea |
| author_facet | Sepehri, Yamin Pad, Pedram Frossard, Pascal Dunbar, L. Andrea |
| contents | The training phase of deep neural networks requires substantial resources and as such is often performed on cloud servers. However, this raises privacy concerns when the training dataset contains sensitive content, e.g., facial or medical images. In this work, we propose a method to perform the training phase of a deep learning model on both an edge device and a cloud server that prevents sensitive content being transmitted to the cloud while retaining the desired information. The proposed privacy-preserving method uses adversarial early exits to suppress the sensitive content at the edge and transmits the task-relevant information to the cloud. This approach incorporates noise addition during the training phase to provide a differential privacy guarantee. We extensively test our method on different facial and medical datasets with diverse attributes using various deep learning architectures, showcasing its outstanding performance. We also demonstrate the effectiveness of privacy preservation through successful defenses against different white-box, deep and GAN-based reconstruction attacks. This approach is designed for resource-constrained edge devices, ensuring minimal memory usage and computational overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05092 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PriPHiT: Privacy-Preserving Hierarchical Training of Deep Neural Networks Sepehri, Yamin Pad, Pedram Frossard, Pascal Dunbar, L. Andrea Computer Vision and Pattern Recognition Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning Image and Video Processing I.2.10; I.2.6; I.2.11; K.4.1 The training phase of deep neural networks requires substantial resources and as such is often performed on cloud servers. However, this raises privacy concerns when the training dataset contains sensitive content, e.g., facial or medical images. In this work, we propose a method to perform the training phase of a deep learning model on both an edge device and a cloud server that prevents sensitive content being transmitted to the cloud while retaining the desired information. The proposed privacy-preserving method uses adversarial early exits to suppress the sensitive content at the edge and transmits the task-relevant information to the cloud. This approach incorporates noise addition during the training phase to provide a differential privacy guarantee. We extensively test our method on different facial and medical datasets with diverse attributes using various deep learning architectures, showcasing its outstanding performance. We also demonstrate the effectiveness of privacy preservation through successful defenses against different white-box, deep and GAN-based reconstruction attacks. This approach is designed for resource-constrained edge devices, ensuring minimal memory usage and computational overhead. |
| title | PriPHiT: Privacy-Preserving Hierarchical Training of Deep Neural Networks |
| topic | Computer Vision and Pattern Recognition Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning Image and Video Processing I.2.10; I.2.6; I.2.11; K.4.1 |
| url | https://arxiv.org/abs/2408.05092 |