Privacy-preserving data quality assessment for federated health
data networks

J 2026

Privacy-preserving data quality assessment for federated health data networks

TOMÁŠIK, Radovan; Tobias KUSSEL; Zdenka DUDOVÁ; Radoslava KACOVÁ; Roman HRSTKA et al.

Základní údaje

Originální název

Privacy-preserving data quality assessment for federated health data networks

Autoři

TOMÁŠIK, Radovan ; Tobias KUSSEL; Zdenka DUDOVÁ; Radoslava KACOVÁ; Roman HRSTKA; Martin LABLANS a Petr HOLUB

Vydání

BMC MEDICAL INFORMATICS AND DECISION MAKING, LONDON, BMC, 2026, 1472-6947

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.800 v roce 2024

Označené pro přenos do RIV

Ano

Organizační jednotka

Fakulta informatiky

DOI

https://doi.org/10.1186/s12911-025-03328-6

UT WoS

001694448500001

Klíčová slova anglicky

Differential privacy; Data quality; Federated data; Medical informatics; BBMRI; CQL

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 25. 3. 2026 11:50, Mgr. Eva Špillingová

Anotace

V originále

BackgroundAssessing data quality in federated health data systems presents unique challenges, particularly when data custodians cannot expose raw data due to privacy regulations. Traditional quality assessment approaches often require centralised access, which conflicts with the principles of data sovereignty and confidentiality.MethodsIn this study, we evaluate the utility of federated data quality assessment with differential privacy techniques to safeguard sensitive health data. The aim is to develop tooling and demonstrate a proof-of-concept implementation over a synthetic dataset of observational medical data.ResultsWe present a privacy-preserving framework for evaluating data quality in federated environments using differential privacy. Our approach enables individual data providers to compute local quality metrics and share only aggregated, privacy-protected results. We implement a proof-of-concept that supports predefined quality checks across different data models and demonstrate how meaningful insights into data quality can be obtained without compromising sensitive information.ConclusionThis work demonstrates that differential privacy can be effectively applied to enable federated quality assessment in health data networks without compromising individual privacy. By implementing a proof-of-concept system over synthetic health data, we show that it is possible to obtain meaningful quality metrics in a decentralised setting.

Návaznosti

EH23_015/0008196, projekt VaV

Název: Rozvoj a modernizace národní sítě biobank (BBMRInv)

LM2023033, projekt VaV

Název: Síť českých biobank

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, BBMRI.cz - Síť českých biobank

90233, velká výzkumná infrastruktura

Název: BBMRI.cz IV

Citovat

TOMÁŠIK, Radovan; Tobias KUSSEL; Zdenka DUDOVÁ; Radoslava KACOVÁ; Roman HRSTKA; Martin LABLANS a Petr HOLUB. Privacy-preserving data quality assessment for federated health data networks. BMC MEDICAL INFORMATICS AND DECISION MAKING. LONDON: BMC, 2026, roč. 26, č. 1, s. 1-11. ISSN 1472-6947. Dostupné z: https://doi.org/10.1186/s12911-025-03328-6.

@article{2550897,
   author = {Tomášik, Radovan and Kussel, Tobias and Dudová, Zdenka and Kacová, Radoslava and Hrstka, Roman and Lablans, Martin and Holub, Petr},
   article_location = {LONDON},
   article_number = {1},
   doi = {https://doi.org/10.1186/s12911-025-03328-6},
   keywords = {Differential privacy; Data quality; Federated data; Medical informatics; BBMRI; CQL},
   language = {eng},
   issn = {1472-6947},
   journal = {BMC MEDICAL INFORMATICS AND DECISION MAKING},
   title = {Privacy-preserving data quality assessment for federated health data networks},
   url = {https://link.springer.com/article/10.1186/s12911-025-03328-6},
   volume = {26},
   year = {2026}
}

TY  - JOUR
ID  - 2550897
AU  - Tomášik, Radovan - Kussel, Tobias - Dudová, Zdenka - Kacová, Radoslava - Hrstka, Roman - Lablans, Martin - Holub, Petr
PY  - 2026
TI  - Privacy-preserving data quality assessment for federated health data networks
JF  - BMC MEDICAL INFORMATICS AND DECISION MAKING
VL  - 26
IS  - 1
SP  - 1-11
EP  - 1-11
PB  - BMC
SN  - 14726947
KW  - Differential privacy
KW  - Data quality
KW  - Federated data
KW  - Medical informatics
KW  - BBMRI
KW  - CQL
UR  - https://link.springer.com/article/10.1186/s12911-025-03328-6
N2  - BackgroundAssessing data quality in federated health data systems presents unique challenges, particularly when data custodians cannot expose raw data due to privacy regulations. Traditional quality assessment approaches often require centralised access, which conflicts with the principles of data sovereignty and confidentiality.MethodsIn this study, we evaluate the utility of federated data quality assessment with differential privacy techniques to safeguard sensitive health data. The aim is to develop tooling and demonstrate a proof-of-concept implementation over a synthetic dataset of observational medical data.ResultsWe present a privacy-preserving framework for evaluating data quality in federated environments using differential privacy. Our approach enables individual data providers to compute local quality metrics and share only aggregated, privacy-protected results. We implement a proof-of-concept that supports predefined quality checks across different data models and demonstrate how meaningful insights into data quality can be obtained without compromising sensitive information.ConclusionThis work demonstrates that differential privacy can be effectively applied to enable federated quality assessment in health data networks without compromising individual privacy. By implementing a proof-of-concept system over synthetic health data, we show that it is possible to obtain meaningful quality metrics in a decentralised setting.
ER  -

TOMÁŠIK, Radovan; Tobias KUSSEL; Zdenka DUDOVÁ; Radoslava KACOVÁ; Roman HRSTKA; Martin LABLANS a Petr HOLUB. Privacy-preserving data quality assessment for federated health data networks. \textit{BMC MEDICAL INFORMATICS AND DECISION MAKING}. LONDON: BMC, 2026, roč.~26, č.~1, s.~1-11. ISSN~1472-6947. Dostupné z: https://doi.org/10.1186/s12911-025-03328-6.

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