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Next Generation of Secure and Resilient Healthcare Data Processing

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Dear Colleagues,

The evolution of the Internet of Things (IoT) has contributed to advances in the interconnectivity, efficiency and data accessibility, improving environmental sustainability, quality of life and well-being. Recent developments in digital health technologies have led to a larger interest in wearable sensors. The successful application of these sensors hinges on the ability to interpret and elaborate on a considerable amount of heterogeneous data, that are exposed to attacks such as noise, privacy invasion, replay and false data injection attacks. The variety and complexity of these data require the provision of new models and technologies able to process and analyze them in a reliable and safe way. Resilient IoT is designed to withstand disruptions and remain functional, despite the operation of adversaries and artificial intelligence algorithms can play an increasing role in offensive and defensive measures to provide an accurate response to threats. In order to be resilient, such systems need to be able to understand what's wrong, figure out how to overcome the resulting problems, and then take what they have learned to overcome those challenges and preserve it for the future. This Special Issue will present both review and original research articles related to the processing of various sensor data, including classification, clustering, anomaly detection, resilience strategies, security and privacy. This Special Issue covers, but is not limited to, the following topics:

Internet of Things for healthcare;
Data mining and knowledge discovery in healthcare;
Machine and deep learning approaches for health data;
Explainable AI models for health, biology, medicine and resilience;
Resilient machine and deep learning;
Decision support systems for healthcare and wellbeing;
AI-based security solutions for IoT systems, smart sensors, wearables, etc.;
Smart sensors with edge AI;
Robust decision making for safety of healthcare systems;
Safety and security in the future of AI.

Dr. Fiammetta Marulli
Dr. Laura Verde
Guest Editors

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Research

25 pages, 1913 KiB

Open AccessArticle

Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data

by Joceline Ziegler, Bjarne Pfitzner, Heinrich Schulz, Axel Saalbach and Bert Arnrich

Sensors 2022, 22(14), 5195; https://doi.org/10.3390/s22145195 - 11 Jul 2022

Cited by 12 | Viewed by 3632

Abstract

Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of

0.94

on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets

ε \in {1, 3, 6, 10}

. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of

0.94

for

ε = 6

. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of

0.76

in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training. Full article

(This article belongs to the Special Issue Next Generation of Secure and Resilient Healthcare Data Processing)

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