Abstract
The bulk of methods in statistical disclosure control primarily deal with individual data from a cross-sectional perspective, i.e. data where individuals are observed at one single point in time. However, nowadays longitudinal data, i.e. individuals observed over multiple periods, are increasingly collected. Such data enhance undoubtedly the possibility of statistical analysis compared to cross-sectional data, but also come with some additional layers of information that have to remain practically useful in a privacy-preserving way. Building on the recently proposed permutation paradigm as an overarching approach to data anonymization, this paper establishes a general framework for the formulation of longitudinal data anonymization and proposes some universal metrics for the assessment of disclosure risk and information loss. We illustrate the application of these new tools using an empirical example.
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Notes
- 1.
For this last case, it can also be ranked using semantic distance metrics (see above).
- 2.
In that case \( P_{T,j} \) will be the identity matrix.
- 3.
To avoid some unnecessary technical difficulties, in what follows zero values in these vectors will be assigned, without loss of generality, a infinitesimally small value ε > 0.
- 4.
To avoid confusion, it must be noted that despite similar profiles Fig. 1 and Figs. 2 and 3 cannot directly be compared. In particular, the individuals with no time rank change in Fig. 1 are not necessarily the same than in Figs. 2 and 3: in the latter, individuals contributing to the flat portion of the curves at zero may have moved through time, but anonymization in t and t + 1 in fact didn’t alter their moves.
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Ruiz, N. (2018). A General Framework and Metrics for Longitudinal Data Anonymization. In: Domingo-Ferrer, J., Montes, F. (eds) Privacy in Statistical Databases. PSD 2018. Lecture Notes in Computer Science(), vol 11126. Springer, Cham. https://doi.org/10.1007/978-3-319-99771-1_15
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