sPLINK: A Federated, Privacy-Preserving Tool as a Robust Alternative to Meta-Analysis in Genome-Wide Association Studies

Nasirigerdeh, Reza; Torkzadehmahani, Reihaneh; Matschinske, Julian; Frisch, Tobias; List, Markus; Späth, Julian; Weiß, Stefan; Völker, Uwe; Wenke, Nina K.; Kacprowski, Tim; Baumbach, Jan

doi:10.1101/2020.06.05.136382

Cited by 19 publications

(28 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, as opposed to the centralized approach, accuracy of results from a meta-analysis that combines the summary statistics or results of local analysis can be affected by cross-study heterogeneity. This can lead to inaccurate and misleading conclusions [ 27 ].…”

Section: Privacy and Security Issues Of Current Medical Data-sharing mentioning

confidence: 99%

Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis

Scheibner¹,

Raisaro²,

Troncoso-Pastoriza³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data utility. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely upon bespoke data sharing contracts. The lengthy process and administration induced by these contracts increases the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between 2 novel advanced privacy-enhancing technologies—homomorphic encryption and secure multiparty computation (defined together as multiparty homomorphic encryption). These privacy-enhancing technologies provide a mathematical guarantee of privacy, with multiparty homomorphic encryption providing a performance advantage over separately using homomorphic encryption or secure multiparty computation. We argue multiparty homomorphic encryption fulfills legal requirements for medical data sharing under the European Union’s General Data Protection Regulation which has set a global benchmark for data protection. Specifically, the data processed and shared using multiparty homomorphic encryption can be considered anonymized data. We explain how multiparty homomorphic encryption can reduce the reliance upon customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research while offering additional incentives for health care and research institutes to employ common data interoperability standards.

show abstract

Section: Privacy and Security Issues Of Current Medical Data-sharing mentioning

confidence: 99%

Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis

Scheibner¹,

Raisaro²,

Troncoso-Pastoriza³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Similar to existing federated-learning solutions, FAMHE enables a large number of data providers to keep their data locally stored under their control and to effectively collaborate in order to perform large-scale analyses. However, contrary to most federated-learning solutions 2,3,3,6,15,16 , FAMHE also protects data confidentiality and does not require the addition of any noise to the results. As other proposed solutions based on advanced cryptography [17][18][19][20][21]37 , FAMHE does not reveal intermediate values to any party.…”

Section: /19mentioning

confidence: 99%

“…To alleviate this trust assumption, federated learning solutions 2,3,6 have been proposed. In these solutions, the data providers keep their data locally and share only aggregates or training model updates with a central server.…”

Section: Related Workmentioning

confidence: 99%

“…Several open-source software platforms have been recently developed to provide access to FA distributed algorithms in a way that is packaged for end-users 3,6,7 , e.g., DataSHIELD 6 is an open-source distributed data analysis and machine learning (ML) platform based on the open-source software R. However, none of these platforms addresses the problem of indirect data leakages that stem from the use of "vanilla" federated learning; hence their adoption poses important questions about the actual advantage in terms of eased compliance to regulations over more conventional data-analysis platforms that rely on data centralization [8][9][10] , as partial aggregates and model updates can still be considered personal identifying data 4,[11][12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

“…Federated 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 . FA enables different healthcare providers to collaboratively perform statistical analyses and develop machine learning models without exchanging the underlying datasets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption

Froelicher¹,

Troncoso-Pastoriza²,

Raisaro³

et al. 2021

Preprint

View full text Add to dashboard Cite

In biomedical research, real-world evidence, which is emerging as an indispensable complement of clinical trials, relies on access to large quantities of patient data that typically reside at separate healthcare institutions. Conventional approaches for centralizing those data are often not feasible due to privacy and security requirements. As a result, more privacy-friendly solutions based on federated analytics are emerging. They enable to simultaneously analyse medical data distributed across a group of connected institutions. However, these techniques do not inherently protect patients' privacy as they require institutions to share intermediate results that can reveal patient-level information. To address this issue, state-of-the-art solutions use additional privacy-preserving measures based on data obfuscation, which often introduce noise in the computation of the final result that can become too inaccurate for precision medicine use cases. We propose FAMHE, a modular system based on multiparty homomorphic encryption, that enables the privacy-preserving execution of federated analytics workflows yielding exact results and without leaking any intermediate information. To demonstrate the maturity of our approach, we reproduce the results of two published state-of-the-art centralized biomedical studies, and we demonstrate that FAMHE enables the efficient, privacy-preserving and decentralized execution of analyses that range from low computational complexity, such as Kaplan-Meier overall survival curves used in oncology, to high computational complexity, such as genome-wide association studies on millions of variants.

show abstract

Artificial Intelligence in Medicine and Privacy Preservation

Ziller¹,

Passerat–Palmbach²,

Trask³

et al. 2021

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

sPLINK: A Federated, Privacy-Preserving Tool as a Robust Alternative to Meta-Analysis in Genome-Wide Association Studies

Cited by 19 publications

References 33 publications

Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis

Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis

Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption

Artificial Intelligence in Medicine and Privacy Preservation

Contact Info

Product

Resources

About