Exact and Efficient Bayesian Inference for Privacy Risk Quantification-Reference-Cited by-同舟云学术

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Exact and Efficient Bayesian Inference for Privacy Risk Quantification

Published:2023 Issue: Volume: Page:263-281
ISSN:0302-9743
Container-title:Software Engineering and Formal Methods
language:
Short-container-title:

Author:

Rønneberg Rasmus C.,Pardo Raúl,Wąsowski Andrzej

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-47115-5_15

Reference38 articles.

1. Statistics Denmark. www.dst.dk/en Accessed 23 June 2023

2. Statistics New Zealand. www.stats.govt.nz/ Accessed 23 June 2023

3. US Census Bureau. www.census.gov/ Accessed 23 June 2023

4. Alvim, M.S., Chatzikokolakis, K., McIver, A., Morgan, C., Palamidessi, C., Smith, G.: The Science of Quantitative Information Flow. Springer, Cham (2020)

5. Article 29 Data Protection Working Party: Opinion 05/2014 on Anonymisation Techniques (2014). www.pdpjournals.com/docs/88197.pdf

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1. Exact and Efficient Bayesian Inference for Privacy Risk Quantification;Software Engineering and Formal Methods;2023

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