Résumé: The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart ehealthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is amajor challenge. Adequate protection of patients' privacy helps to increase public trust in medical research. Additionally, DL-based models are complex, and in a cloudbased approach, efficient data processing in such models is complicated. To address these challenges, we propose an efficient and secure cancer diagnostic framework for histopathological image classification by utilizing both differential privacy and secure multi-party computation. For efficient computation, instead of performing the whole operation on the cloud, we decouple the layers into two modules: one for feature extraction using the VGGNet16 module at the user side and the remaining layers for private prediction over the cloud. The efficacy of the framework is validated on two datasets composed of histopathological images of the canine mammary tumor and human breast cancer. The application of differential privacy preserving to the proposed model makes the model secure and capable of preserving the privacy of sensitive data from any adversary, without significantly compromising the model accuracy. Extensive experiments show that the proposed model efficiently achieves the trade-off between privacy and model performance.

Langue: Anglais