March networks evidence reviewer encryption
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However, none of these platforms address the aforementioned problem of indirect privacy leakages that stem from their use of “vanilla” federated learning.
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For example, DataSHIELD 7 is a distributed data analysis and a machine-learning (ML) platform based on the open-source software R. Several open-source software platforms have recently been developed to provide users streamlined access to FA algorithms 3, 7, 8. We note that limited data interoperability across different healthcare providers is another potential challenge in deploying FA this, in practice, can be surmounted by harmonizing the data across institutions before performing the analysis. Our work focuses on overcoming this key limitation of existing FA approaches. Indeed, despite patient-level data not being transferred between the institutions engaging in FA, it has been shown that the model updates (or partial aggregates) themselves can, under certain circumstances, leak sensitive personal information about the underlying individuals, thus leading to re-identification, membership inference, and feature reconstruction 5, 6.
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This is in large part due to the unresolved privacy issues of FA, related to the sharing of model updates or partial data aggregates in cleartext. The adoption of FA in the medical sector, despite its potential, has been slower than expected. Such advances are particularly important in the context of rare diseases or medical conditions, where the number of affected patients in a single institution is often not sufficient to identify meaningful statistical patterns with enough statistical power. These opportunities can facilitate the development and validation of artificial intelligence algorithms that yield more accurate, unbiased, and generalizable clinical recommendations, as well as accelerate novel discoveries. FA offers opportunities for exploiting large and diverse volumes of data distributed across multiple institutions. In this way, each healthcare provider can define its own data governance and maintain control over the access to its patient-level data.
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Only aggregated results or model updates are transferred. FA enables different healthcare providers to collaboratively perform statistical analyses and to develop machine-learning models, without exchanging the underlying datasets. Moreover, stringent data protection and privacy regulations (e.g., General Data-Protection Regulation (GDPR) 1) strongly restrict the transfer of personal data, including even pseudonymized data, across jurisdictions.įederated analytics (FA) is emerging as a new paradigm that seeks to address the data governance and privacy issues related to medical-data sharing 2, 3, 4. The challenges are not due to the technical hurdles of transporting high volumes of heterogeneous data across organizations but to the legal and regulatory barriers that make the transfer of patient-level data outside a healthcare provider complex and time-consuming. Today, however, medical data are scattered across many institutions, which renders centralized access and aggregation of such data challenging, if not impossible. A key requirement for fully realizing the potential of precision medicine is to make large amounts of medical data interoperable and widely accessible to researchers.