Abstract
In the last few years, Official Statistics have been deeply impacted by the development of smart technologies. This paper summarizes the architectural achievements and the main methodological aspects of the ESSnet (European Statistical System network project) on Smart Surveys, launched at the beginning of 2020. The main goal of the ESSnet is to deliver preparatory work for the development of a European platform, to share und re-use methods and tools for smart data processing. More precisely, the project aims at implementing and testing a common framework for (trusted) smart surveys, through the design of a reference architecture and the development of methodological and technical capabilities within the European Statistical System (ESS). Further, the use of innovative data sources forces National Statistical Institutes (NSIs) to face new challenges, e.g., access to data owned by public and private parties, data processing across multiple NSIs. Privacy preserving technologies are exploited in this paper with the aim to understand their impact on both the architectural framework and technical requirements of the platform.
The expected benefits of developing a shared infrastructure are the decrease of respondent burden, the modernization of statistical processes, as well as the harmonization and enrichment of the statistical output.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
ArchiMate is an open and independent language for architectural modelling, compliant with Enterprise Architecture standard and available from: https://www.archimatetool.com/.
- 2.
For the definition of paradata, see: https://en.wikipedia.org/wiki/Paradata.
- 3.
In order to implement the use case, we used on open-source federated learning framework named “Flower” [13].
References
Ricciato, F., Wirthmann, A., Giannakouris, K., Reis, F., Skaliotis, M.: Trusted smart statistics: motivations and principles. Stat. J. IAOS 35 (2019). https://ec.europa.eu/eurostat/cros/system/files/sji190584.pdf
ESSnet on Smart Surveys (2020–2021). https://ec.europa.eu/eurostat/cros/content/essnet-smart-surveys_en
Biemer, P.P., de Leeuw, E., Eckman S., Edwards B., Kreuter T., Lyberg L.E., Tucker N.C., West, B.T. (eds.) Total Survey Error in Practice. John Wiley & Sons, Inc., Hoboken, New Jersey (2017)
Keusch, F., Struminskaya, B., Antoun, C., Couper, M.P., Kreuter, F.: Willingness to participate in passive mobile data collection. Pub. Opin. Quart. 83, 210–235 (2019)
Struminskaya, B., Lugtig, P., Keusch, F., Hӧhne, J.K.: Augmenting surveys with data from sensors and apps: opportunities and challenges. Soc. Sci. Comput. Rev. 1–13 (2020). https://doi.org/10.1177/0894439320979951
Generic Statistical Business Process Model (GSBPM) v. 5.1. January (2019). Available from: https://statswiki.unece.org/display/GSBPM/GSBPM+v5.1
Generic Statistical Information Model (GSIM) v. 1.2 March (2021). Availabel from: https://statswiki.unece.org/display/gsim/GSIM+v1.2+documents
ESSnet on Big Data II, Work Package F, Deliverable F1. (2018–2021). https://ec.europa.eu/eurostat/cros/sites/crosportal/files/WPF_Deliverable_F1_BREAL_Big_Data_REference_Architecture_and_Layers_v.03012020.pdf
ESS Enterprise Architecture Reference Framework (EARF), September (2015). Available from: https://ec.europa.eu/eurostat/cros/content/ess-enterprise-architecture-reference-framework_en
Scannapieco, M., Bogdanovits, F., Gallois, F., Fischer, K.G., Paulussen, R., Quaresma, S., et al.: BREAL. Big Data Reference Architecture and Layers. Application layer and Information layer (2021). Version 2021-03-31. Edited by EUROSTAT
Ricciato, F., Giannakouris, K, Wirthmann, A., Hahn, M.: Trusted Smart Surveys: a possible application of Privacy Enhancing Technologies in Official Statistics. SIS (2020). https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Docenti/Universit%C3%A0/Pearson-SIS-2020-atti-convegno.pdf
Ricciato, F., Bujnowska, A., Wirthmann, A., Hahn, M., Barredo-Capelot, E.: A reflection on privacy and data confidentiality in official statistics. In: ISI World Statistics Congress (2019). https://www.bis.org/ifc/events/isi_wsc_62/ips177_paper3.pdf
Beutel, D.J., Topal, T., Mathur, A., Qiu, X., Parcollet, T., Lane, N.D.: Flower: a friendly federated learning research framework (2020). arXiv preprint arXiv:2007.14390
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bruno, M., Inglese, F., Ruocco, G. (2022). Trusted Smart Surveys: Architectural and Methodological Challenges Related to New Data Sources. In: Salvati, N., Perna, C., Marchetti, S., Chambers, R. (eds) Studies in Theoretical and Applied Statistics . SIS 2021. Springer Proceedings in Mathematics & Statistics, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-031-16609-9_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-16609-9_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16608-2
Online ISBN: 978-3-031-16609-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)