Data mining (DM) and machine learning (ML) applications in medical diagnostic systems are budding. Data privacy is essential in these systems as healthcare data are highly sensitive. The proposed ...work first discusses various privacy and security challenges in these systems. To address these next, we discuss different privacy‐preserving (PP) computation techniques in the context of DM and ML for secure data evaluation and processing. The state‐of‐the‐art applications of these systems in healthcare are analyzed at various stages such as data collection, data publication, data distribution, and output phases regarding PPDM and input, model, training, and output phases in the context of PPML. Furthermore, PP federated learning is also discussed. Finally, we present open challenges in these systems and future research directions.
This article is categorized under:
Application Areas > Health Care
Technologies > Machine Learning
Commercial, Legal, and Ethical Issues > Security and Privacy
Data mining (DM) and machine learning (ML) applications in medical diagnostic systems are budding. Data privacy is extremely essential in these systems as healthcare data are highly sensitive. The proposed work first discusses various privacy and security challenges in these systems. To address these next, we discussed different privacy‐preserving (PP) computation techniques in the context of DM and ML for secure data evaluation and processing. The state‐of‐the‐art applications of these systems in healthcare are analyzed at various stages such as data collection, data publication, data distribution, and output phases regarding PPDM, and input, model, training, and output phases in the context of PPML. Furthermore, PP federated learning is also discussed.
The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential ...for research and practice. This review explores several key issues that have arisen around big data. First, we propose a taxonomy of sources of big data to clarify terminology and identify threads common across some subtypes of big data. Next, we consider common public health research and practice uses for big data, including surveillance, hypothesis-generating research, and causal inference, while exploring the role that machine learning may play in each use. We then consider the ethical implications of the big data revolution with particular emphasis on maintaining appropriate care for privacy in a world in which technology is rapidly changing social norms regarding the need for (and even the meaning of) privacy. Finally, we make suggestions regarding structuring teams and training to succeed in working with big data in research and practice.
Aim: Today, data banks contain unpredictable data. Together with the advances in data science, large data offer the potential to better
understand the causes of diseases. This potential results from ...the processing, analysis or modeling of machine learning algorithms.
Various data sets stored in different institutions are not always shared directly due to privacy and legal concerns. This problem limits the
full use of large data in health research. Federated learning is aimed at developing artificial intelligence systems based on both high
accuracy and data privacy. Materials and Methods: In this study, a federated learning approach was proposed in order to access any data and develop machine
learning applications without sharing personal information within the scope of data privacy. Firstly, the structure of the Federated learner
has been studied. It was then determined how federated learning should be used in machine learning models in different health
applications. Results: In federated learning, the model is trained on local computers and its updates are transferred to a central server. The updated
model is then transferred to local models. In this way, the central model is trained without seeing the data. Conclusion: It is necessary to make machine learning models in which confidentiality is applied with data obtained from health. For this,
federated learning must be integrated into traditional machine learning applications. Thus, high performance is envisaged to be achieved
with big data where data confidentiality is adopted.
When applied to healthcare, machine learning ushers in a new age of data-driven medical practice that holds great promise for better patient outcomes and individualized treatment. However, this ...evolution isn't without significant difficulties, such as the difficulty of striking a balance between patient confidentiality and data use. In this study, we use -Differential Privacy as a privacy-protecting technique and a number of machine learning models to quantify the value of the data collected. Our research demonstrates a subtle trade-off, where more stringent privacy safeguards often result in less useful data, and vice versa. We stress the need for ethical frameworks, patient permission, and flexible privacy restrictions as means of negotiating this space. Achieving responsible and successful machine learning-enabled healthcare calls for a number of future steps, including optimization of privacy settings, adoption of federated learning, data ownership through blockchain, validations in the actual world, and extensive ethical advice.
This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an ...architecture for executing classification algorithms in a federated manner, enabling collaborative model-building among health data owners without sharing their datasets.
Utilizing an agent-based architecture, a privacy-preserving federated ML algorithm was developed to create a global predictive model from various local models. This involved formally defining the algorithm in two steps: data preparation and federated model training on FAIR health data and constructing the architecture with multiple components facilitating algorithm execution. The solution was validated by five healthcare organizations using their specific health datasets.
Five organizations transformed their datasets into Health Level 7 Fast Healthcare Interoperability Resources via a common FAIRification workflow and software set, thereby generating FAIR datasets. Each organization deployed a Federated ML Agent within its secure network, connected to a cloud-based Federated ML Manager. System testing was conducted on a use case aiming to predict 30-day readmission risk for chronic obstructive pulmonary disease patients and the federated model achieved an accuracy rate of 87%.
The paper demonstrated a practical application of privacy-preserving federated ML among five distinct healthcare entities, highlighting the value of FAIR health data in machine learning when utilized in a federated manner that ensures privacy protection without sharing data.
This solution effectively leverages FAIR datasets from multiple healthcare organizations for federated ML while safeguarding sensitive health datasets, meeting legislative privacy and security requirements.
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Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, ...and the increase of noncommunicable diseases. However, data centralization for big data raises privacy and regulatory concerns.Covered topics include (1) an introduction to privacy of patient data and distributed learning as a potential solution to preserving these data, a description of the legal context for patient data research, and a definition of machine/deep learning concepts; (2) a presentation of the adopted review protocol; (3) a presentation of the search results; and (4) a discussion of the findings, limitations of the review, and future perspectives.Distributed learning from federated databases makes data centralization unnecessary. Distributed algorithms iteratively analyze separate databases, essentially sharing research questions and answers between databases instead of sharing the data. In other words, one can learn from separate and isolated datasets without patient data ever leaving the individual clinical institutes.Distributed learning promises great potential to facilitate big data for medical application, in particular for international consortiums. Our purpose is to review the major implementations of distributed learning in health care.
It is becoming more and more important for healthcare providers to protect the integrity and security of sensitive medical data as they use cloud computing for data processing and storage. This work ...explores the field of machine learning algorithms that are secure and privacy-preserving when applied to healthcare information in cloud environments. We investigate sophisticated cryptography, federated learning, and differentiating privacy techniques using an interpretive philosophy and a method based on deduction. Our results highlight the computational expense associated with cryptographic protocols, while also revealing their nuanced performance and potential for enabling secure calculations. Federated learning is shown to be effective in collaborative model training, providing a workable approach to privacy-preserving data analysis over-dispersed healthcare datasets. Differential privacy systems require careful parameter calibration because they demonstrate a delicate balance between data value and privacy preservation.
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