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We evaluate the uniqueness of a mobility trace as follows.
As a first analysis, we evaluate the uniqueness of a single spatial and spatio-temporal point.
In this section, we show how to evaluate the uniqueness of GPS mobility traces and we propose a technique for the identification of people by means of mobility data.
We then evaluate the uniqueness of a human mobility trace as the percentage of subsets (S_{n}(M)) that matches exactly one trace, i.e., (m(S_{n}(M))=1).
Moreover, we evaluate the uniqueness of network complexity measures numerically.
The optimization was started 10000 times from random initial values to evaluate the uniqueness of the optimal flux distribution.
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Secondly, we have evaluated the uniqueness of GPS mobility traces using three popular datasets.
The focus of this work is different: in fact, we are interested in evaluating the uniqueness of GPS information and the extent to which an unseen set of points can be linked to the underlying generating trajectory, thus revealing the identity of a user.
In this paper and in [84], we evaluated the uniqueness of some information-theoretic measures for real and synthetic chemical structures.
The second concept is the development of a conservative statistical testing approach that does not require knowledge of the true distribution of errors or an expensive permutation test to evaluate the uniqueness and significance of alignments.
A Monte Carlo simulation was performed to evaluate the uniqueness estimators on six clinically relevant data sets.
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