Differential correct attribution probability for synthetic data: An exploration

Jennifer Taub; Mark Elliot; Maria Pampaka; Duncan Smith

doi:10.1007/978-3-319-99771-1_9

Differential correct attribution probability for synthetic data: An exploration

Jennifer Taub, Mark Elliot, Maria Pampaka, Duncan Smith

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Synthetic data generation has been proposed as a flexible alternative to more traditional statistical disclosure control (SDC) methods for limiting disclosure risk. Synthetic data generation is functionally distinct from standard SDC methods in that it breaks the link between the data subjects and the data such that reidentification is no longer meaningful. Therefore orthodox measures of disclosure risk assessment - which are based on reidentification - are not applicable. Research into developing disclosure assessment measures specifically for synthetic data has been relatively limited. In this paper, we develop a method called Differential Correct Attribution Probability (DCAP). Using DCAP, we explore the effect of multiple imputation on the disclosure risk of synthetic data.

Original language	English
Title of host publication	Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2018, Proceedings
Editors	Francisco Montes, Josep Domingo-Ferrer
Publisher	Springer Nature
Pages	122-137
Number of pages	16
ISBN (Print)	9783319997704
DOIs	https://doi.org/10.1007/978-3-319-99771-1_9
Publication status	Published - 2018
Event	International Conference on Privacy in Statistical Databases, PSD 2018 - Valencia, Spain Duration: 26 Sept 2018 → 28 Sept 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11126 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Privacy in Statistical Databases, PSD 2018
Country/Territory	Spain
City	Valencia
Period	26/09/18 → 28/09/18

Keywords

CART
Disclosure risk
Synthetic data

Research Beacons, Institutes and Platforms

Cathie Marsh Institute

Access to Document

10.1007/978-3-319-99771-1_9

Cite this

Taub, J., Elliot, M., Pampaka, M., & Smith, D. (2018). Differential correct attribution probability for synthetic data: An exploration. In F. Montes, & J. Domingo-Ferrer (Eds.), Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2018, Proceedings (pp. 122-137). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11126 LNCS). Springer Nature. https://doi.org/10.1007/978-3-319-99771-1_9

Taub, Jennifer ; Elliot, Mark ; Pampaka, Maria et al. / Differential correct attribution probability for synthetic data : An exploration. Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2018, Proceedings. editor / Francisco Montes ; Josep Domingo-Ferrer. Springer Nature, 2018. pp. 122-137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Differential correct attribution probability for synthetic data: An exploration",

abstract = "Synthetic data generation has been proposed as a flexible alternative to more traditional statistical disclosure control (SDC) methods for limiting disclosure risk. Synthetic data generation is functionally distinct from standard SDC methods in that it breaks the link between the data subjects and the data such that reidentification is no longer meaningful. Therefore orthodox measures of disclosure risk assessment - which are based on reidentification - are not applicable. Research into developing disclosure assessment measures specifically for synthetic data has been relatively limited. In this paper, we develop a method called Differential Correct Attribution Probability (DCAP). Using DCAP, we explore the effect of multiple imputation on the disclosure risk of synthetic data.",

keywords = "CART, Disclosure risk, Synthetic data",

author = "Jennifer Taub and Mark Elliot and Maria Pampaka and Duncan Smith",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Taub, J, Elliot, M , Pampaka, M & Smith, D 2018, Differential correct attribution probability for synthetic data: An exploration. in F Montes & J Domingo-Ferrer (eds), Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11126 LNCS, Springer Nature, pp. 122-137, International Conference on Privacy in Statistical Databases, PSD 2018, Valencia, Spain, 26/09/18. https://doi.org/10.1007/978-3-319-99771-1_9

Differential correct attribution probability for synthetic data: An exploration. / Taub, Jennifer; Elliot, Mark ; Pampaka, Maria et al.
Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2018, Proceedings. ed. / Francisco Montes; Josep Domingo-Ferrer. Springer Nature, 2018. p. 122-137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11126 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Differential correct attribution probability for synthetic data

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AU - Taub, Jennifer

AU - Elliot, Mark

AU - Pampaka, Maria

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N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

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AB - Synthetic data generation has been proposed as a flexible alternative to more traditional statistical disclosure control (SDC) methods for limiting disclosure risk. Synthetic data generation is functionally distinct from standard SDC methods in that it breaks the link between the data subjects and the data such that reidentification is no longer meaningful. Therefore orthodox measures of disclosure risk assessment - which are based on reidentification - are not applicable. Research into developing disclosure assessment measures specifically for synthetic data has been relatively limited. In this paper, we develop a method called Differential Correct Attribution Probability (DCAP). Using DCAP, we explore the effect of multiple imputation on the disclosure risk of synthetic data.

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KW - Synthetic data

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Taub J, Elliot M , Pampaka M, Smith D. Differential correct attribution probability for synthetic data: An exploration. In Montes F, Domingo-Ferrer J, editors, Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2018, Proceedings. Springer Nature. 2018. p. 122-137. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-99771-1_9

Differential correct attribution probability for synthetic data: An exploration

Abstract

Publication series

Conference

Keywords

Research Beacons, Institutes and Platforms

Access to Document

Other files and links

Fingerprint

Impact on the Statistical Confidentiality Practices of Data Stewardship Organisations

Cite this

Differential correct attribution probability for synthetic data: An exploration

Abstract

Publication series

Conference

Keywords

Research Beacons, Institutes and Platforms

Access to Document

Other files and links

Fingerprint

Impacts

Impact on the Statistical Confidentiality Practices of Data Stewardship Organisations

Cite this