Evidence map›Paper›PMID 37448036›Full record

ArticleSensors (Basel, Switzerland)2023

The smashHitCore Ontology for GDPR-Compliant Sensor Data Sharing in Smart Cities.

Anelia Kurteva, Tek Raj Chhetri, Amar Tauqeer, Rainer Hilscher, Anna Fensel, Kevin Nagorny, Ana Correia, Albert Zilverberg, Stefan Schestakov, Thorben Funke and 1 more

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

11 authors.

Anelia KurtevaSemantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria.ORCID 0000-0003-4512-5969
Tek Raj ChhetriSemantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria.ORCID 0000-0002-3905-7878
Amar TauqeerSemantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria.ORCID 0000-0002-3345-387X
Rainer HilscherSemantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria.ORCID 0000-0002-5004-4584
Anna FenselSemantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria.ORCID 0000-0002-1391-7104
Kevin NagornyInstitut für Angewandte Systemtechnik Bremen GmbH (ATB), 28359 Bremen, Germany.
Ana CorreiaInstitut für Angewandte Systemtechnik Bremen GmbH (ATB), 28359 Bremen, Germany.ORCID 0000-0002-2469-6546
Albert ZilverbergInstitut für Angewandte Systemtechnik Bremen GmbH (ATB), 28359 Bremen, Germany.ORCID 0000-0001-6491-1231
Stefan SchestakovL3S Research Center, Leibniz University Hannover, 30167 Hannover, Germany.
Thorben FunkeL3S Research Center, Leibniz University Hannover, 30167 Hannover, Germany.ORCID 0000-0001-6196-8312
Elena DemidovaData Science and Intelligent Systems Group (DSIS), University of Bonn, 53115 Bonn, Germany.

Funding

European Commission Horizon 2020 programme 871477
6 · The paper itself

Abstract

The adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case.

Indexed as

consentcontractsdata sharingGDPRinsuranceontologysensorssmart cities

Identifiers

PMID37448036
PMCPMC10346455

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.