Privacy-Preserving Federated Data Analysis: Data Sharing, Protection, and Bioethics in Healthcare

Choudhury, Ananya; Sun, Chang; Dekker, André; Dumontier, Michel; Soest, Johan van

doi:10.1007/978-3-030-83047-2_8

Cited by 6 publications

(2 citation statements)

References 46 publications

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“…Pseudonymization can protect a user's sensitive attributes, such as age, gender, and location, as well as his/her explicit and implicit preferences. Several studies have been conducted on this matter [115][116][117].…”

Section: Pseudonymizationmentioning

confidence: 99%

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

et al. 2023

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Big data is a rapidly growing field, and new developments are constantly emerging to address various challenges. One such development is the use of federated learning for recommendation systems (FRSs). An FRS provides a way to protect user privacy by training recommendation models using intermediate parameters instead of real user data. This approach allows for cooperation between data platforms while still complying with privacy regulations. In this paper, we explored the current state of research on FRSs, highlighting existing research issues and possible solutions. Specifically, we looked at how FRSs can be used to protect user privacy while still allowing organizations to benefit from the data they share. Additionally, we examined potential applications of FRSs in the context of big data, exploring how these systems can be used to facilitate secure data sharing and collaboration. Finally, we discuss the challenges associated with developing and deploying FRSs in the real world and how these challenges can be addressed.

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Section: Pseudonymizationmentioning

confidence: 99%

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

et al. 2023

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“…Federated Learning (FL) allows collaborative development of artificial intelligence models using large datasets, without the need to share individual subject-level data 1,2,3,4 . In FL, partial models trained on separate datasets are shared, but not the data itself, hence a global model is derived from the collective set of partial models.…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Federated Deep Learning Infrastructure for ARGOS: ARtificial intelligence for Gross tumor vOlume Segmentation. (Preprint)

Choudhury,

Volmer,

Martin

et al. 2024

Preprint

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BACKGROUND Accurate delineation of the gross tumor volume (GTV) is crucial in radiotherapy for dose calculation and precise imaging-guided treatment of lung cancer patients. Conventionally, this task has been performed manually by radiation oncologists, which can be subjective and vary among clinicians. Deep learning has enabled automated GTV segmentation, with the potential to revolutionize the radiotherapy workflow by improving efficiency and consistency, ultimately enhancing patient outcomes while reducing clinician workload. However, the adoption of deep learning based GTV segmentation tools is hindered by the challenges of data privacy and the need for large, diverse datasets across multiple institutions. Federated learning (FL) offers a promising solution, allowing collaborative development of AI models without the need to share individual subject-level data. OBJECTIVE The objective is to introduce an innovative federated learning infrastructure called the Personal Health Train (PHT) that includes the procedural, technical, and governance components needed to implement federated learning on real-world healthcare data, including training deep learning neural networks. The study aims to apply this federated deep learning infrastructure to the use case of gross tumor volume (GTV) segmentation on chest CT images of lung cancer patients, and present the results from a proof-of-concept experiment. METHODS The PHT framework addresses the challenges of data privacy concerns of data sharing by keeping data close to the source, and instead sending analysis to the data. Technologically, PHT requires three interdependent components: "tracks" (protected communication channels), "trains" (containerized software applications), and "stations" (institutional data repositories), which are supported by the open source "Vantage6" software. The study applies this federated deep learning infrastructure to the use case of GTV segmentation on chest CT images of lung cancer patients, with the introduction of an additional component called the Secure Aggregations Server, where the model averaging is done in a trusted and inaccessible environment. RESULTS In this paper we demonstrated the feasibility of executing deep learning algorithms in a federated manner using PHT and presented the results from a proof-of-concept study. The infrastructure linked 12 hospitals across 8 nations, covering 4 continents, demonstrating the scalability and global reach of the proposed approach. In the entire execution and training of the deep learning algorithm, no data has been shared outside the hospital. CONCLUSIONS The findings of the proof-of-concept study, as well as the implications and limitations of the infrastructure and the results, are discussed. The application of federated deep learning to unstructured medical imaging data, facilitated by the PHT framework and Vantage6 platform, represents a significant advancement in the field. The proposed infrastructure addresses the challenges of data privacy and enables collaborative model development, paving the way for the widespread adoption of deep learning-based tools in the medical domain and beyond. The introduction of the Secure Aggregation Server implied that data leakage problems in federated learning can be prevented by careful design decisions of the infrastructure. CLINICALTRIAL ARtificial Intelligence for Gross Tumour vOlume Segmentation (ARGOS) ClinicalTrials.gov ID NCT05775068 Sponsor Maastricht Radiation Oncology Information provided by Andre Dekker, Maastricht Radiation Oncology (Responsible Party) https://clinicaltrials.gov/study/NCT05775068

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Exploring Comprehensive Privacy Solutions for Enhancing Recommender System Security and Utility

Gupta,

Shinde

2024

Communication and Intelligent Systems

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Privacy-Preserving Federated Data Analysis: Data Sharing, Protection, and Bioethics in Healthcare

Cited by 6 publications

References 46 publications

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

Privacy Preserving Federated Deep Learning Infrastructure for ARGOS: ARtificial intelligence for Gross tumor vOlume Segmentation. (Preprint)

Exploring Comprehensive Privacy Solutions for Enhancing Recommender System Security and Utility

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