Privacy-Preserving Machine Learning for Health Institutes

Medical data is, due to its nature, often susceptible to data privacy and security concerns. The identity of a person can be derived from medical data. Federated learning, one type of machine learning technique, is popularly used to improve the privacy and security of medical data. In federated learning, the training data is distributed across multiple machines, and the learning process of deep learning (DL) models is performed collaboratively. However, the privacy of DL models is not protected. Privacy attacks on the DL models aim to obtain sensitive information. Therefore, the DL models should be protected from adversarial attacks, especially those which utilize medical data. One of the solutions to solve this problem is homomorphic encryption-based model protection. This paper proposes a privacy-preserving federated learning algorithm for medical data using homomorphic encryption. The proposed algorithm uses a Secure Multiparty Computation (SMPC) protocol to protect the deep learning model from adversaries. In this study, the proposed algorithm using a real-world medical dataset is evaluated in terms of the model performance

BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models

Medical data is frequently quite sensitive in terms of data privacy and security. Federated learning has been used to increase the privacy and security of medical data, which is a sort of machine learning technique. The training data is disseminated across numerous machines in federated learning, and the learning process is collaborative. There are numerous privacy attacks on deep learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in medical data applications. Homomorphic encryption-based model security from the adversarial collaborator is one of the answers to this challenge. Using homomorphic encryption, this research presents a privacy-preserving federated learning system for medical data. The proposed technique employs a secure multi-party computation protocol to safeguard the deep learning model from adversaries. The proposed approach is tested in terms of model performance using a real-world medical dataset in this paper

Privacy-Preserving Machine Learning for Health Institutes

Medical data is, due to its nature, often susceptible to data privacy and security concerns. The identity of a person can be derived from medical data. Federated learning, one type of machine learning technique, is popularly used to improve the privacy and security of medical data. In federated learning, the training data is distributed across multiple machines, and the learning process of deep learning (DL) models is performed collaboratively. However, the privacy of DL models is not protected. Privacy attacks on the DL models aim to obtain sensitive information. Therefore, the DL models should be protected from adversarial attacks, especially those which utilize medical data. One of the solutions to solve this problem is homomorphic encryption-based model protection. This paper proposes a privacy-preserving federated learning algorithm for medical data using homomorphic encryption. The proposed algorithm uses a Secure Multiparty Computation (SMPC) protocol to protect the deep learning model from adversaries. In this study, the proposed algorithm using a real-world medical dataset is evaluated in terms of the model performance

BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models

Medical data is frequently quite sensitive in terms of data privacy and security. Federated learning has been used to increase the privacy and security of medical data, which is a sort of machine learning technique. The training data is disseminated across numerous machines in federated learning, and the learning process is collaborative. There are numerous privacy attacks on deep learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in medical data applications. Homomorphic encryption-based model security from the adversarial collaborator is one of the answers to this challenge. Using homomorphic encryption, this research presents a privacy-preserving federated learning system for medical data. The proposed technique employs a secure multi-party computation protocol to safeguard the deep learning model from adversaries. The proposed approach is tested in terms of model performance using a real-world medical dataset in this paper.publishedVersio