Maria Bras-Amorós

Maria Bras-Amorós

We present a new algorithm to explore or count the numerical semigroups of a given genus which uses the unleaved version of the tree of numerical semigroups. In the unleaved tree there are no leaves rather than the ones at depth equal to the genus in consideration. For exploring the unleaved tree we present a new encoding system of a numerical semigroup given by the gcd of its left elements and its shrinking, that is, the semigroup generated by its left elements divided by their gcd. We show a method to determine the right generators and strong generators of a semigroup by means of the gcd and the shrinking encoding, as well as a method to encode a semigroup from the encoding of its parent or of its predecessor sibling. With the new algorithm we obtained $n_{76}=29028294421710227$ and $n_{77}=47008818196495180$.

Josep Domingo-Ferrer, Krishnamurty Muralidhar, Maria Bras-Amorós

Anonymization for privacy-preserving data publishing, also known as statistical disclosure control (SDC), can be viewed under the lens of the permutation model. According to this model, any SDC method for individual data records is functionally equivalent to a permutation step plus a noise addition step, where the noise added is marginal, in the sense that it does not alter ranks. Here, we propose metrics to quantify the data confidentiality and utility achieved by SDC methods based on the permutation model. We distinguish two privacy notions: in our work, anonymity refers to subjects and hence mainly to protection against record re-identification, whereas confidentiality refers to the protection afforded to attribute values against attribute disclosure. Thus, our confidentiality metrics are useful even if using a privacy model ensuring an anonymity level ex ante. The utility metric is a general-purpose metric that can be conveniently traded off against the confidentiality metrics, because all of them are bounded between 0 and 1. As an application, we compare the utility-confidentiality trade-offs achieved by several anonymization approaches, including privacy models (k-anonymity and $ε$-differential privacy) as well as SDC methods (additive noise, multiplicative noise and synthetic data) used without privacy models.