Privacy with Good Taste

Pardo, Raúl; Rafnsson, Willard; Steinhorn, Gregor; Lavrov, Denis; Lumley, Thomas; Probst, Christian W.; Ziedins, Ilze; Wąsowski, Andrzej

doi:10.1007/978-3-031-25734-6_7

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…A proposed response has emerged in the form of guidance, authored by cybersecurity researchers and focused on the development of privacy risk evaluation tools (26). The application of probabilistic programming to quantify indirect data leakage using tools such as "Privug" offer solutions for both privacy researchers, and data controllers, to conduct analysis in order to make informed decisions when anonymising data (27,28). In a similar fashion, a recent publication from the European Union Agency for Cybersecurity details risks associated with medical imaging data for diagnosis (29).…”

Section: Risk V Utilitymentioning

confidence: 99%

Dicing with data: the risks, benefits, tensions and tech of health data in the iToBoS project

Aspell,

Goldsteen,

Renwick

2024

Front. Digit. Health

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This paper will discuss the European funded iToBoS project, tasked by the European Commission to develop an AI diagnostic platform for the early detection of skin melanoma. The paper will outline the project, provide an overview of the data being processed, describe the impact assessment processes, and explain the AI privacy risk mitigation methods being deployed. Following this, the paper will offer a brief discussion of some of the more complex aspects: (1) the relatively low population clinical trial study cohort, which poses risks associated with data distinguishability and the masking ability of the applied anonymisation tools, (2) the project's ability to obtain informed consent from the study cohort given the complexity of the technologies, (3) the project's commitment to an open research data strategy and the additional privacy risk mitigations required to protect the multi-modal study data, and (4) the ability of the project to adequately explain the outputs of the algorithmic components to a broad range of stakeholders. The paper will discuss how the complexities have caused tension which are reflective of wider tensions in the health domain. A project level solution includes collaboration with a melanoma patient network, as an avenue for fair and representative qualification of risks and benefits with the patient stakeholder group. However, it is unclear how scalable this process is given the relentless pursuit of innovation within the health domain, accentuated by the continued proliferation of artificial intelligence, open data strategies, and the integration of multi-modal data sets inclusive of genomics.

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Section: Risk V Utilitymentioning

confidence: 99%

Dicing with data: the risks, benefits, tensions and tech of health data in the iToBoS project

Aspell,

Goldsteen,

Renwick

2024

Front. Digit. Health

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Assessing Privacy Vulnerabilities in Genetic Data Sets: Scoping Review

Thomas,

Mackes,

Preuss-Dodhy

et al. 2024

JMIR Bioinform Biotech

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Background Genetic data are widely considered inherently identifiable. However, genetic data sets come in many shapes and sizes, and the feasibility of privacy attacks depends on their specific content. Assessing the reidentification risk of genetic data is complex, yet there is a lack of guidelines or recommendations that support data processors in performing such an evaluation. Objective This study aims to gain a comprehensive understanding of the privacy vulnerabilities of genetic data and create a summary that can guide data processors in assessing the privacy risk of genetic data sets. Methods We conducted a 2-step search, in which we first identified 21 reviews published between 2017 and 2023 on the topic of genomic privacy and then analyzed all references cited in the reviews (n=1645) to identify 42 unique original research studies that demonstrate a privacy attack on genetic data. We then evaluated the type and components of genetic data exploited for these attacks as well as the effort and resources needed for their implementation and their probability of success. Results From our literature review, we derived 9 nonmutually exclusive features of genetic data that are both inherent to any genetic data set and informative about privacy risk: biological modality, experimental assay, data format or level of processing, germline versus somatic variation content, content of single nucleotide polymorphisms, short tandem repeats, aggregated sample measures, structural variants, and rare single nucleotide variants. Conclusions On the basis of our literature review, the evaluation of these 9 features covers the great majority of privacy-critical aspects of genetic data and thus provides a foundation and guidance for assessing genetic data risk.

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Privacy with Good Taste

Cited by 2 publications

References 21 publications

Dicing with data: the risks, benefits, tensions and tech of health data in the iToBoS project

Dicing with data: the risks, benefits, tensions and tech of health data in the iToBoS project

Assessing Privacy Vulnerabilities in Genetic Data Sets: Scoping Review

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