The risk of re-identification versus the need to identify individuals in rare disease research

Hansson, Mats; Lochmüller, Hanns; Rieß, Olaf; Schaefer, Franz; Orth, Michael; Rubinstein, Yaffa; Molster, Caron; Dawkins, Hugh; Taruscio, Domenica; Paz, Manuel Posada de la; Woods, Simon

doi:10.1038/ejhg.2016.52

Cited by 51 publications

(32 citation statements)

References 39 publications

(36 reference statements)

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“…In cases of patients with rare diseases, they sometimes happen to be registered with multiple projects at the same time or at different times. When performing a cross‐sectional analysis or a unification of study resources, such as registry, reidentification is necessary . In previous cases, reidentification was performed by using unique IDs .…”

Section: Discussionmentioning

confidence: 99%

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National platform for Rare Diseases Data Registry of Japan

Furusawa

Yamaguchi

Yagishita

et al. 2019

Learning Health Systems

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Introduction In Japan, there are approximately 300 projects conducting research on rare diseases supported by the Ministry of Health, Labour and Welfare of Japan (MHLW) and the Japan Agency for Medical Research and Development (AMED). Diverse data, including clinical, genomic, and sample‐related data, are generated by these projects. However, at present, such data are managed individually by each project. This makes it difficult for third parties to ascertain the data generated by projects. Methods Again this background, at the beginning of 2017, the AMED started the National Platform for Rare Diseases Data Registry of Japan (RADDAR‐J), whose mission is to construct a cross‐sectional data integration platform incorporating projects supported by the AMED and MHLW. RADDAR‐J promotes data sharing by the projects in accordance with the data‐sharing policy established by the AMED, which classifies data sharing into three categories based on the strategies used to protect the rights of researchers while promoting data sharing. RADDAR‐J integrates and analyzes data shared by each project to add value to the resources and promote secondary use by third parties while protecting the rights of the researchers who shared their data. The platform is designed to provide incentives to projects that shared their data by supporting registry construction or genomic analysis to promote data sharing. RADDAR‐J also has the function of data identification to securely integrate data originating from the same person. RADDAR‐J accelerates clinical research by encouraging each project to utilize a central ethics committee. Results/Conclusion The use of the platform by projects is expected to lead to streamlined data collection, improved quality assurance, improved access to data, and promotion of joint research and the secondary use of shared data. These benefits will accelerate research into diagnosis and treatment technologies and will hopefully lead to improved quality of life for patients with rare diseases.

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Section: Discussionmentioning

confidence: 99%

“…When performing a cross-sectional analysis or a unification of study resources, such as registry, reidentification is necessary. 16 In previous cases, reidentification was performed by using unique IDs. 17 In Japan, there is the My Number System,18 in which each citizen is provided with a unique number.…”

Section: Reidentificationmentioning

confidence: 99%

National platform for Rare Diseases Data Registry of Japan

Furusawa

Yamaguchi

Yagishita

et al. 2019

Learning Health Systems

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“…Moreover, efforts to make data anonymous are not always successful. Studies show the precariousness of de-identification efforts in large data sets, for example, in the reidentification of individuals with rare diseases in human genome research (Hansson et al, 2016) and the identification of personal information from Netflix data (Narayanan & Shmatikov, 2008). Given computational advances and the variety and volume of data recorded that is of a personal nature, the combination of publicly available data sets may also put individuals at risk.…”

Section: What Is Being Researched? New Data Sourcesmentioning

confidence: 99%

Qualitative Research Ethics in the Big Data Era

Hesse

Glenna

Hinrichs

et al. 2018

American Behavioral Scientist

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This article examines the developments that have motivated this special issue on Qualitative Research Ethics in the Big Data Era. The article offers a broad overview of many pressing challenges and opportunities that the Big Data era raises particularly for qualitative research. Big Data has introduced to the social sciences new data sources, new research methods, new researchers, and new forms of data storage that have immediate and potential effects on the ethics and practice of qualitative research. Drawing from a literature review and insights gathered at a National Science Foundation-funded workshop in 2016, we present five principles for qualitative researchers and their institutions to consider in navigating these emerging research landscapes. These principles include (a) valuing methodological diversity; (b) encouraging research that accounts for and retains context, specificity, and marginalized and overlooked populations; (c) pushing beyond legal concerns to address often messy ethical dilemmas; (d) attending to regional and disciplinary differences; and (e) considering the entire lifecycle of research, including the data afterlife in archives or in open-data facilities.

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“…The advantage of this approach is that the researcher then has complete access to the anonymised genotypes and phenotypes, with only the identities of the samples being redacted; there is then no technical limitation as to the genetic analysis that can be performed. However, this carries certain risks because a data breach cannot be ruled out, and even if the data are anonymised, comparing anonymous genotypes with those of genotyped relatives might still reveal genetic relationships (Hansson et al 2016). In the clinical field, methods such as the random time-shifting of anonymised patient records (Hripcsak et al 2016) offer some protection whilst not being cryptographically secure.…”

Section: Introductionmentioning

confidence: 99%

Private Genomes and Public SNPs: Homomorphic encryption of genotypes and phenotypes for shared quantitative genetics

Mott

Fischer

Prins

et al. 2020

Preprint

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Sharing human genotype and phenotype data presents a challenge because of privacy concerns, but is essential in order to discover otherwise inaccessible genetic associations. Here we present a method of homomorphic encryption that obscures individuals' genotypes and phenotypes and is suited to quantitative genetic association analysis. Encrypted ciphertext and unencrypted plaintext are interchangeable from an analytical perspective. This allows one to store ciphertext on public web services and share data across multiple studies, while maintaining privacy. The encryption method uses as its key a highdimensional random linear orthogonal transformation that leaves the likelihood of quantitative trait data unchanged under a linear model with normally distributed errors. It also preserves linkage disequilibrium between genetic variants and associations between variants and phenotypes. It scrambles relationships between individuals: encrypted genotype dosages closely resemble Gaussian deviates, and in fact can be replaced by quantiles from a Gaussian with only negligible effects on accuracy. Standard likelihoodbased inferences are unaffected by orthogonal encryption. These include the use of mixed linear models to control for unequal relatedness between individuals, the estimation of heritability, and the inclusion of covariates when testing for association. Orthogonal transformations can also be applied in a modular fashion that permits multi-party federated mega-analyses. Under this scheme any number of parties first agree to share a common set of genotype sites and covariates prior to encryption. Each party then privately encrypts and shares their own ciphertext, and analyses the other parties' ciphertexts. In the absence of private variants, or knowledge of the key, we show that it is infeasible to decrypt ciphertext using existing brute-force or noise reduction attacks. Therefore, we present the method as a challenge to the community to determine its security.

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The risk of re-identification versus the need to identify individuals in rare disease research

Cited by 51 publications

References 39 publications

National platform for Rare Diseases Data Registry of Japan

National platform for Rare Diseases Data Registry of Japan

Qualitative Research Ethics in the Big Data Era

Private Genomes and Public SNPs: Homomorphic encryption of genotypes and phenotypes for shared quantitative genetics

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