Measuring privacy compliance using fitness metrics

S. Banescu; M. Petkovic; N. Zannone

doi:10.1007/978-3-642-32885-5_8

Measuring privacy compliance using fitness metrics

S. Banescu, M. Petkovic, N. Zannone

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

17 Citaten (Scopus)

4 Downloads (Pure)

Samenvatting

Nowadays, repurposing of personal data is a major privacy issue. Detection of data repurposing requires posteriori mechanisms able to determine how data have been processed. However, current a posteriori solutions for privacy compliance are often manual, leading infringements to remain undetected. In this paper, we propose a privacy compliance technique for detecting privacy infringements and measuring their severity. The approach quantifies infringements by considering a number of deviations from specifications (i.e., insertion, suppression, replacement, and re-ordering).

Originele taal-2	Engels
Titel	Business Process Management (10th International Conference, BPM 2012, Tallinn, Estonia, September 3-6, 2012. Proceedings)
Redacteuren	A. Barros, A. Gal, E. Kindler
Plaats van productie	Berlin
Uitgeverij	Springer
Pagina's	114-119
ISBN van geprinte versie	978-3-642-32884-8
DOI's	https://doi.org/10.1007/978-3-642-32885-5_8
Status	Gepubliceerd - 2012
Evenement	10th International Conference on Business Process Management (BPM 2012) - Tallinn, Estland Duur: 3 sep. 2012 → 6 sep. 2012 Congresnummer: 10

Publicatie series

Naam	Lecture Notes in Computer Science
Volume	7481
ISSN van geprinte versie	0302-9743

Congres

Congres	10th International Conference on Business Process Management (BPM 2012)
Verkorte titel	BPM 2012
Land/Regio	Estland
Stad	Tallinn
Periode	3/09/12 → 6/09/12
Ander	10th International Conference on Business Process Management (BPM 2012)

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10.1007/978-3-642-32885-5_8

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Banescu, S., Petkovic, M., & Zannone, N. (2012). Measuring privacy compliance using fitness metrics. In A. Barros, A. Gal, & E. Kindler (editors), Business Process Management (10th International Conference, BPM 2012, Tallinn, Estonia, September 3-6, 2012. Proceedings) (blz. 114-119). (Lecture Notes in Computer Science; Vol. 7481). Springer. https://doi.org/10.1007/978-3-642-32885-5_8

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title = "Measuring privacy compliance using fitness metrics",

abstract = "Nowadays, repurposing of personal data is a major privacy issue. Detection of data repurposing requires posteriori mechanisms able to determine how data have been processed. However, current a posteriori solutions for privacy compliance are often manual, leading infringements to remain undetected. In this paper, we propose a privacy compliance technique for detecting privacy infringements and measuring their severity. The approach quantifies infringements by considering a number of deviations from specifications (i.e., insertion, suppression, replacement, and re-ordering).",

author = "S. Banescu and M. Petkovic and N. Zannone",

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doi = "10.1007/978-3-642-32885-5_8",

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booktitle = "Business Process Management (10th International Conference, BPM 2012, Tallinn, Estonia, September 3-6, 2012. Proceedings)",

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note = "10th International Conference on Business Process Management (BPM 2012), BPM 2012 ; Conference date: 03-09-2012 Through 06-09-2012",

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Banescu, S, Petkovic, M & Zannone, N 2012, Measuring privacy compliance using fitness metrics. in A Barros, A Gal & E Kindler (uitgave), Business Process Management (10th International Conference, BPM 2012, Tallinn, Estonia, September 3-6, 2012. Proceedings). Lecture Notes in Computer Science, vol. 7481, Springer, Berlin, blz. 114-119, 10th International Conference on Business Process Management (BPM 2012), Tallinn, Estland, 3/09/12. https://doi.org/10.1007/978-3-642-32885-5_8

Measuring privacy compliance using fitness metrics. / Banescu, S.; Petkovic, M.; Zannone, N.
Business Process Management (10th International Conference, BPM 2012, Tallinn, Estonia, September 3-6, 2012. Proceedings). uitgave / A. Barros; A. Gal; E. Kindler. Berlin: Springer, 2012. blz. 114-119 (Lecture Notes in Computer Science; Vol. 7481).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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N2 - Nowadays, repurposing of personal data is a major privacy issue. Detection of data repurposing requires posteriori mechanisms able to determine how data have been processed. However, current a posteriori solutions for privacy compliance are often manual, leading infringements to remain undetected. In this paper, we propose a privacy compliance technique for detecting privacy infringements and measuring their severity. The approach quantifies infringements by considering a number of deviations from specifications (i.e., insertion, suppression, replacement, and re-ordering).

AB - Nowadays, repurposing of personal data is a major privacy issue. Detection of data repurposing requires posteriori mechanisms able to determine how data have been processed. However, current a posteriori solutions for privacy compliance are often manual, leading infringements to remain undetected. In this paper, we propose a privacy compliance technique for detecting privacy infringements and measuring their severity. The approach quantifies infringements by considering a number of deviations from specifications (i.e., insertion, suppression, replacement, and re-ordering).

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Measuring privacy compliance using fitness metrics

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