Estimating the success of re-identifications in incomplete datasets using generative models

Estimating the success of re-identifications in incomplete datasets using generative models. Rocher, L., Hendrickx, J. M., & Montjoye, Y. d. Nature Communications, 10(1):1–9, July, 2019.

Paper doi abstract bibtex

Anonymization has been the main means of addressing privacy concerns in sharing medical and socio-demographic data. Here, the authors estimate the likelihood that a specific person can be re-identified in heavily incomplete datasets, casting doubt on the adequacy of current anonymization practices.

@article{rocher_estimating_2019,
	title = {Estimating the success of re-identifications in incomplete datasets using generative models},
	volume = {10},
	copyright = {2019 The Author(s)},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-019-10933-3},
	doi = {10.1038/s41467-019-10933-3},
	abstract = {Anonymization has been the main means of addressing privacy concerns in sharing medical and socio-demographic data. Here, the authors estimate the likelihood that a specific person can be re-identified in heavily incomplete datasets, casting doubt on the adequacy of current anonymization practices.},
	language = {en},
	number = {1},
	urldate = {2019-08-20},
	journal = {Nature Communications},
	author = {Rocher, Luc and Hendrickx, Julien M. and Montjoye, Yves-Alexandre de},
	month = jul,
	year = {2019},
	pages = {1--9},
}

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