A Probabilistic Perspective on Re-Identifiability

Koot, M.R.; Mandjes, Michel; Noordende, G.J. van 't; Laat, C.T.A.M. de

doi:10.1080/08898480.2013.816222

Cited by 5 publications

(8 citation statements)

References 8 publications

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“…Population uniqueness is a commonly used measure of re-identification risk [31-36]. In cases where the disclosed data set is a sample, the population uniqueness must be estimated.…”

Section: Discussionmentioning

confidence: 99%

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Estimating the re-identification risk of clinical data sets

Dankar

Emam

Neisa

et al. 2012

BMC Med Inform Decis Mak

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BackgroundDe-identification is a common way to protect patient privacy when disclosing clinical data for secondary purposes, such as research. One type of attack that de-identification protects against is linking the disclosed patient data with public and semi-public registries. Uniqueness is a commonly used measure of re-identification risk under this attack. If uniqueness can be measured accurately then the risk from this kind of attack can be managed. In practice, it is often not possible to measure uniqueness directly, therefore it must be estimated.MethodsWe evaluated the accuracy of uniqueness estimators on clinically relevant data sets. Four candidate estimators were identified because they were evaluated in the past and found to have good accuracy or because they were new and not evaluated comparatively before: the Zayatz estimator, slide negative binomial estimator, Pitman’s estimator, and mu-argus. A Monte Carlo simulation was performed to evaluate the uniqueness estimators on six clinically relevant data sets. We varied the sampling fraction and the uniqueness in the population (the value being estimated). The median relative error and inter-quartile range of the uniqueness estimates was measured across 1000 runs.ResultsThere was no single estimator that performed well across all of the conditions. We developed a decision rule which selected between the Pitman, slide negative binomial and Zayatz estimators depending on the sampling fraction and the difference between estimates. This decision rule had the best consistent median relative error across multiple conditions and data sets.ConclusionThis study identified an accurate decision rule that can be used by health privacy researchers and disclosure control professionals to estimate uniqueness in clinical data sets. The decision rule provides a reliable way to measure re-identification risk.

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“…Population uniqueness is a commonly used measure of re-identification risk [31-36]. In cases where the disclosed data set is a sample, the population uniqueness must be estimated.…”

Section: Discussionmentioning

confidence: 99%

“…The uniqueness of individuals in the population is often used as a measure of re-identification risk [31-36]. In commentary in the Federal Register about the de-identification standards in the Health Insurance Portability and Accountability Act (HIPAA), HHS referred only to uniqueness as the re-identification risk measure [37,38].…”

Section: Introductionmentioning

confidence: 99%

Estimating the re-identification risk of clinical data sets

Dankar

Emam

Neisa

et al. 2012

BMC Med Inform Decis Mak

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“…which is solved for = , cf. (12). This directly implies that the decay rate under consideration equals 2D( ) = 2ζ, which implies logarithmic efficiency.…”

Section: Importance Samplingmentioning

confidence: 94%

“…In previous studies, we have developed techniques to assess the fraction of uniquely identifiable individuals, see [12,13]. Neglecting people older than 100 years, this means that the number of possible outcomes is 100 × 12 = 1200.…”

Section: The Impact Of Non-uniformitymentioning

confidence: 99%

“…The current population pyramid of the city of Amsterdam, for instance, shows a peak around 1980 with about 2% of the population was born that year, whereas just about 8% of the population was born before 1940. In previous studies, we have developed techniques to assess the fraction of uniquely identifiable individuals, see [12,13]. Our objective here is to determine the probability that within a sampled group of 120 persons, everyone has a unique year/month combination.…”

Section: The Impact Of Non-uniformitymentioning

confidence: 99%

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Generalized Birthday Problems in the Large-Deviations Regime

Mandjes¹,

Eurandom

2013

Prob. Eng. Inf. Sci.

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This paper considers generalized birthday problems, in which there are d classes of possible outcomes. A fraction f i of the N possible outcomes has probabilitySampling k times (with replacements), the objective is to determine (or approximate) the probability that all outcomes are different, the so-called uniqueness probability (or: no-coincidence probability). Although it is trivial to explicitly characterize this probability for the case d = 1, the situation with multiple classes is substantially harder to analyze.Parameterizing k ≡ aN , it turns out that the uniqueness probability decays essentially exponentially in N , where the associated decay rate ζ follows from a variational problem. Only for small d this can be solved in closed form. Assuming α i is of the form 1 + ϕ i ε, the decay rate ζ can be written as a power series in ε; we demonstrate how to compute the corresponding coefficients explicitly. Also, a logarithmically efficient simulation procedure is proposed. The paper concludes with a series of numerical experiments, showing that (i) the proposed simulation approach is fast and accurate, (ii) assuming all outcomes equally likely would lead to estimates for the uniqueness probability that can be orders of magnitude off, and (iii) the power-series based approximations work remarkably well.

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