Evidence map›Paper›PMID 38948408›Full record

ArticleData in brief2024

Evaluation and utilisation of privacy enhancing technologies-A data spaces perspective.

J M Auñón, D Hurtado-Ramírez, L Porras-Díaz, B Irigoyen-Peña, S Rahmian, Y Al-Khazraji, J Soler-Garrido, A Kotsev

Abstract read
In one paragraph

Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

J M AuñónGMV, Department of Artificial Intelligence and Big Data, Isaac Newton 11, Tres Cantos, Madrid 28760, Spain.
D Hurtado-RamírezGMV, Department of Artificial Intelligence and Big Data, Isaac Newton 11, Tres Cantos, Madrid 28760, Spain.
L Porras-DíazGMV, Department of Artificial Intelligence and Big Data, Isaac Newton 11, Tres Cantos, Madrid 28760, Spain.
B Irigoyen-PeñaGMV, Department of Artificial Intelligence and Big Data, Isaac Newton 11, Tres Cantos, Madrid 28760, Spain.
S RahmianGMV, Department of Artificial Intelligence and Big Data, Isaac Newton 11, Tres Cantos, Madrid 28760, Spain.
Y Al-KhazrajiGMV, Department of Artificial Intelligence and Big Data, Isaac Newton 11, Tres Cantos, Madrid 28760, Spain.
J Soler-GarridoEuropean Commission, Joint Research Centre (JRC), Algorithmic Transparency Unit, Inca Garcilaso 3, Seville 41092, Spain.
A KotsevEuropean Commission, Joint Research Centre (JRC), Digital Economy Unit, Via Enrico Fermi 2749, Ispra 21027, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Data sharing has facilitated the digitisation of society. We can access our bank accounts or make an appointment with our doctor anytime and anywhere. To achieve this, we have to share certain information, whether personal, professional, etc. This may seem like a minor cost for an individual user, but actually the data economy as the backbone of a digital transformation that is reshaping all aspects of human life. However, one of the major concerns arises regarding what happens to such individual data; once shared, control over it is often lost. For that reason, users and companies are reluctant to share their data. The European Union, through its European Strategy for Data, is establishing a policy and legal framework for establishing a single market for data in Europe by improving the trust and fairness of the data economy. Data spaces are a commitment to sharing data in a reliable and secure way, but this endeavour should, of course, not be at the expense of privacy rights. In recent years, Privacy-Enhancing Technologies (PETs) have emerged to achieve data sharing and privacy preservation that can address the requirements of data spaces around sensitive citizen and business data. In this work, we review existing PETs and assess their relevance, technological maturity, and applicability in the context of common European data spaces. Finally, we illustrate the benefits of secure data sharing via Federated Learning in a healthcare use case, where the preservation of privacy is a primer requirement and is therefore to be guaranteed.

Indexed as

Data spacesDifferential privacyFederated learningHomomorphic encryptionPrivacy preservation

Identifiers

PMID38948408
PMCPMC11214200

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.