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Sensors
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Next Generation of Secure and Resilient Healthcare Data Processing
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Next Generation of Secure and Resilient Healthcare Data Processing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (10 January 2024) | Viewed by 3292

Special Issue Editors

Dr. Fiammetta Marulli

E-Mail Website
Guest Editor
Department of Mathematics and Physics, Università della Campania “Luigi Vanvitelli”, Viale Lincoln, 81100 Caserta, Italy
Interests: artificial intelligence; machine and deep learning; federated deep learning on cloud systems; data analytics and data science applied to Internet of Things and cyber-physical systems; natural language processing
Special Issues, Collections and Topics in MDPI journals
Dr. Laura Verde

E-Mail Website
Guest Editor
Dipartimento di Matematica e Fisica, Università degli Studi della Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
Interests: signal processing; artificial intelligence; predictive maintenance; digital twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Things
  • data mining
  • artificial intelligence
  • resilient machine
  • deep learning
  • healthcare applications

Published Papers (1 paper)

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Research

25 pages, 1913 KiB  
Article
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://0-doi-org.brum.beds.ac.uk/10.3390/s22145195 - 11 Jul 2022
Cited by 7 | Viewed by 2280
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 [...] Read more.
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∈{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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