Calculation of Risk Probabilities for the Cell Key Method

Enderle, Tobias; Giessing, Sarah; Tent, Reinhard

doi:10.1007/978-3-030-57521-2_11

Cited by 2 publications

(3 citation statements)

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“…During the project work, considerable efforts were made to provide more flexible and accessible SDC tools [21] but also to assess disclosure risks vs. utility and improve the general methodology [30,32,31]. Also the findings of this paper (see section 5) suggest that the ESS recommended CK method stands as a good practice, if applied correctly and consistently, featuring in particular superior utility properties in various output scenarios with still small and controllable disclosure risks.…”

Section: A Ess Recommendations For Harmonised 2021 Eu Census Protectionmentioning

confidence: 90%

“…17 Note finally that such an attack cannot be "aimed" at specific observations of interest; it is limited to wherever extreme noise patterns happen to occur. Targeted attacks must pursue other strategies, 16 There are more disclosive patterns when true 0s are not perturbed [31], but these require very specific true count patterns combined with a specific noise pattern drawn; such patterns become very unlikely for moderately large E, as suggested in [31]. 17 The counting in section 3.3 does not include the 1 km 2 grid output (cf.…”

Section: Utility-driven Parametrisation: Bounded Noisementioning

confidence: 99%

“…Within the legal framework for EU censuses 31 , statistical confidentiality and SDC measures are under the responsibility of the national statistical authorities of the Member States, so that confidential data must not be submitted to Eurostat. 32 This means that Eurostat will not receive, maintain or process any confidential data (or personal data in GDPR sense) related to the upcoming 2021 EU census round.…”

Section: A Ess Recommendations For Harmonised 2021 Eu Census Protectionmentioning

confidence: 99%

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Differential privacy and noisy confidentiality concepts for European population statistics

Bach¹

2020

Preprint

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The paper aims to give an overview of various approaches to statistical disclosure control based on random noise that are currently being discussed for official population statistics and censuses. A particular focus is on a stringent delineation between different concepts influencing the discussion: we separate clearly between risk measures, noise distributions and output mechanisms-putting these concepts into scope and into relation with each other.After recapitulating differential privacy as a risk measure, the paper also remarks on utility and risk aspects of some specific output mechanisms and parameter setups, with special attention on static outputs that are rather typical in official population statistics. In particular, it is argued that unbounded noise distributions, such as plain Laplace, may jeopardise key unique census features without a clear need from a risk perspective. On the other hand, bounded noise distributions, such as the truncated Laplace or the cell key method, can be set up to keep unique census features while controlling disclosure risks in census-like outputs.Finally, the paper analyses some typical attack scenarios to constrain generic noise parameter ranges that suggest a good risk/utility compromise for the 2021 EU census output scenario. The analysis also shows that strictly differentially private mechanisms would be severely constrained in this scenario.

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Section: A Ess Recommendations For Harmonised 2021 Eu Census Protectionmentioning

confidence: 90%