International transfers of personal data for health research following Schrems II: a problem in need of a solution

Hallinan, Dara; Bernier, Alexander; Cambon‐Thomsen, Anne; Crawley, Francis P.; Dimitrova, Diana; Medeiros, Cláudia Bauzer; Nilsonne, Gustav; Parker, Simon C.; Pickering, J. Brian; Rennes, Stéphanie

doi:10.1038/s41431-021-00893-y

Cited by 17 publications

(16 citation statements)

References 10 publications

(6 reference statements)

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“…127 However, if the data are deemed to be personal data, transfers to countries without adequacy decisions, including the United States, become challenging and unclear at present due to the recent ''Schrems II'' judgment. 128 Due to challenges with ensuring GDPR compliance during global sharing of sensitive healthcare and research data, alternative models for data sharing have been considered. For example, in congruence with a declaration signed by 21 European countries to transnationally share data on at least one million human genomes by the end of 2022, large-scale initiatives, such as the European Genome-Phenome Archive, are shifting toward federated approaches.…”

Section: Ethical Issues Pertaining To Data Usagementioning

confidence: 99%

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Routine omics collection is a golden opportunity for European human research in space and analog environments

et al. 2022

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Section: Ethical Issues Pertaining To Data Usagementioning

confidence: 99%

Section: Ethical and Legal Considerations For Human Space Omicsmentioning

confidence: 99%

Routine omics collection is a golden opportunity for European human research in space and analog environments

et al. 2022

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“…This change has meant that that normative frameworks are examined with greater scrutiny. An examination of the European Commission's adequacy decisions over time shows that pre-Schrems decisions were less exigent (e.g., Canada, Uruguay) than post-Schrems (e.g., Japan, UK) [28]. The more exacting the standard that informs a determination of adequacy, the closer the analysis begins to approximate a point-by-point analysis, the consequence of which is a reduced chance of a determination of adequacy.…”

Section: Step 2: Assess If Foreign Normative Framework Is Functionally Equivalentmentioning

confidence: 99%

International Coordination of Research Ethics Review: An Adequacy Model

Thorogood

Beauvais

2021

Philosophies

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International direct-to-participant (DTP) genomics research involves the use of mobile technology to recruit, consent, and study participants remotely. This model can facilitate research across broad geographies and many countries, but must also comply with the norms of multiple recruitment jurisdictions, with each jurisdiction typically requiring at least one local research ethics review. Each additional research ethics review increases bureaucratic hurdles without necessarily strengthening the protection of participants’ rights and interests. For DTP genomic research, obtaining a review may in fact be impossible in the absence of a local research partner. This paper proposes an “adequacy” approach, inspired by data protection law, to coordinate the regulation and oversight of international DTP genomics research. This involves one country voluntarily assessing whether another country’s research ethics reviews are equivalent to its own, in terms of objectives and effectiveness. Ethics-approved projects led by researchers from countries recognized as adequate are deemed to comply with local norms, eliminating the need for a duplicative local review. Adequacy preserves the sovereignty of countries to determine their own regulatory aims and which other countries to trust. It therefore provides a voluntary, incremental path towards greater global coordination of health research oversight.

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“…The European GDPR rules are more strict than those in the US Health Insurance Portability and Accountability Act (HIPAA) rules for health data exchange [5]. EU demands that health data protection in a third country is essentially equivalent to that in the EU, which is not the case with the US HIPAA system [6]. All health data transfers require to ensure that informed consent is received from each individual, which makes most transatlantic collaboration impossible, if not planned in advance.…”

Section: Introductionmentioning

confidence: 99%

DEEPFAKE Image Synthesis for Data Augmentation

et al. 2022

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Deepfake videos and images have increased in recent years at exponential rates, and refer to the transfer of features of interest from source image (or video) to target image (or video), such that the target modality appears to animate the source almost close to the real. The forensic professionals, policy makers, and the public alike are interested in the role of Deepfakes is playing to confuse viewers in spreading misinformation by undermining the truth. In the past decade, computer vision has made significant advances using the latest state-of-art-methods of image processing techniques. Supervised Deep learning models produce super-human results in a variety of computer vision and machine learning applications. The field of medical imaging is scarce in terms of a reliable data set that is extensive enough to train distinct models. One way to tackle this problem is to use a Generative Adversarial Network to synthesise DEEPFAKE images to augment the data. DEEPFAKE images can be a useful means in various applications like translating to different useful and sometimes malicious modalities, unbalanced datasets or increasing the amount of datasets. In this paper the data scarcity has been addressed by using Progressive Growing Generative Adversarial Networks (PGGAN). However, PGGAN consists of convolution layer that suffers from the training-related issues. PGGAN requires a large number of convolution layers in order to obtain highresolution image training, which makes training a difficult task. In this work, a subjective self-attention layer has been added with convolution layers for efficient feature learning and the use of spectral normalization for training stabilization -the two tasks resulting into what is referred to as Enhanced-GAN. The Enhanced-GAN performance is compared to PGGAN performance using the parameters of AM Score and Mode Score. In addition, the strength of Enhanced-GAN and PGGAN synthesis is evaluated using the U-net model for segmentation tasks. Dice Coefficient metrics show that U-net trained on Enhanced-GAN DEEPFAKE data optimized with real data performs better than PGGAN DEEPFAKE data with real data.

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International transfers of personal data for health research following Schrems II: a problem in need of a solution

Cited by 17 publications

References 10 publications

Routine omics collection is a golden opportunity for European human research in space and analog environments

Routine omics collection is a golden opportunity for European human research in space and analog environments

International Coordination of Research Ethics Review: An Adequacy Model

DEEPFAKE Image Synthesis for Data Augmentation

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