Anonymizing Data for Privacy-Preserving Federated Learning

Abstract

Federated learning enables training a global machine learning model from data distributed across multiple sites, without having to move the data. This is particularly relevant in healthcare applications, where data is rife with personal, highly-sensitive information, and data analysis methods must provably comply with regulatory guidelines. Although federated learning prevents sharing raw data, it is still possible to launch privacy attacks on the model parameters that are exposed during the training process, or on the generated machine learning model. In this paper, we propose the first syntactic approach for offering privacy in the context of federated learning. Unlike the state-of-the-art differential privacy-based frameworks, our approach aims to maximize utility or model performance, while supporting a defensible level of privacy, as demanded by GDPR and HIPAA. We perform a comprehensive empirical evaluation on two important problems in the healthcare domain, using real-world electronic health data of 1 million patients. The results demonstrate the effectiveness of our approach in achieving high model performance, while offering the desired level of privacy. Through comparative studies, we also show that, for varying datasets, experimental setups, and privacy budgets, our approach offers higher model performance than differential privacy-based techniques in federated learning.

24th European Conference on Artificial Intelligence (ECAI)

References in corpus (8)

• Deep Learning with Differential Privacy
• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
• How To Backdoor Federated Learning
• Differentially Private Federated Learning: A Client Level Perspective
• A simple and practical algorithm for differentially private data release
• Differential Privacy-enabled Federated Learning for Sensitive Health Data
• Differentially Private Learning with Adaptive Clipping

Cited by in corpus (4)

• Differential Privacy-enabled Federated Learning for Sensitive Health Data
• Dopamine: Differentially Private Federated Learning on Medical Data
• Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare
• Differentially private cross-silo federated learning