Abstract: The method delivers a degree of protection (txP3) representative of the risk of re-identification of data in the case of a correspondence search attack including a deterministic search based on an external information source and a correspondence search based on a distance. The method comprises steps of E) consolidating a set of original individuals (EDO) and a set of anonymous individuals (IA); F) identifying, in the set of original individuals, individuals at risk (IOrs) via the deterministic correspondence search; G) evaluating a degree of failure of re-identification (txP1) for the sets of original individuals and of anonymous individuals, on the basis of the correspondence search based on distance; H) computing the degree of protection as a function of a total number of individuals in the original dataset, of a number (RS) of individuals at risk identified in step B) and of the degree of failure of re-identification (txP1).

Abstract: (EN) The method of the invention provides a protection rate (txP2) representative of the risk of re-identification of data.

Abstract: The method outputs synthetic time series as an anonymized version of time series (x1 to xE) and comprises an identification (Fb3, Fb4) of K nearest neighbors with a distance calculation law (Eq3, Eq4), and a generation (Fb5) of a first synthetic time series version (xiA(t)) corresponding to a time series (xi) by a combination (Eq6) of the K nearest neighbors. In accordance with the invention, the method comprises an additional anonymization process (Fb6) aimed at temporal characteristics of the phase (Eq10), the number of measurements (n, ni, nj) and/or the measurement step (PL, PLA), the process carrying out on a first synthetic time series version (xiA(t)) a modification of at least one temporal characteristic from at least one temporal characteristic of the same type of one of the K nearest adjacent time series identified, which is selected by means of a predetermined selection law (Eq9).

Abstract: The present invention relates to a method for creating avatars from an initial sensitive data set stored in a database of a computer system, the initial data comprising attributes relating to a plurality of individuals, the method comprising: a) choosing a number {k} of nearest neighbors to be used from all the individuals in the initial data set, b) identifying, for attributes relating to a given individual, the k nearest neighbors from among the other individuals in the data set, c) generating, for at least one attribute relating to said individual, a new attribute value from quantities which are characteristic of the attribute in the identified k nearest neighbors and weighted by a coefficient, and d) creating avatar data comprising the new attribute value(s), so as to ensure the sensitive data relating to the individual are non-identifiable.