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The risk of re-identification remains high even in country-scale location datasets.

ABSTRACT: Although anonymous data are not considered personal data, recent research has shown how individuals can often be re-identified. Scholars have argued that previous findings apply only to small-scale datasets and that privacy is preserved in large-scale datasets. Using 3 months of location data, we (1) show the risk of re-identification to decrease slowly with dataset size, (2) approximate this decrease with a simple model taking into account three population-wide marginal distributions, and (3) prove that unicity is convex and obtain a linear lower bound. Our estimates show that 93% of people would be uniquely identified in a dataset of 60M people using four points of auxiliary information, with a lower bound at 22%. This lower bound increases to 87% when five points are available. Taken together, our results show how the privacy of individuals is very unlikely to be preserved even in country-scale location datasets.

SUBMITTER: Farzanehfar A

PROVIDER: S-EPMC7961185 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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The risk of re-identification remains high even in country-scale location datasets.

Farzanehfar Ali A Houssiau Florimond F de Montjoye Yves-Alexandre YA

Patterns (New York, N.Y.) 20210312 3

Although anonymous data are not considered personal data, recent research has shown how individuals can often be re-identified. Scholars have argued that previous findings apply only to small-scale datasets and that privacy is preserved in large-scale datasets. Using 3 months of location data, we (1) show the risk of re-identification to decrease slowly with dataset size, (2) approximate this decrease with a simple model taking into account three population-wide marginal distributions, and (3) p ...[more]

PMID: 33748793

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