Autor segons l'article: Maya-Lopez, Armando; Casino, Fran; Solanas, Agusti
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Alkhoury, Nadine / Casino Cembellín, Francisco José / Solanas Gómez, Agustín
Paraules clau: Traveling salesman problem Statistical disclosure control Microaggregation Location privacy Graph theory Data privacy traveling salesman problem statistical disclosure control privacy location privacy graph theory data-oriented microaggregation data privacy algorithm
Resum: The collection of personal data is exponentially growing and, as a result, individual privacy is endangered accordingly. With the aim to lessen privacy risks whilst maintaining high degrees of data utility, a variety of techniques have been proposed, being microaggregation a very popular one. Microaggregation is a family of perturbation methods, in which its principle is to aggregate personal data records (i.e., microdata) in groups so as to preserve privacy through k-anonymity. The multivariate microaggregation problem is known to be NP-Hard; however, its univariate version could be optimally solved in polynomial time using the Hansen-Mukherjee (HM) algorithm. In this article, we propose a heuristic solution to the multivariate microaggregation problem inspired by the Traveling Salesman Problem (TSP) and the optimal univariate microaggregation solution. Given a multivariate dataset, first, we apply a TSP-tour construction heuristic to generate a Hamiltonian path through all dataset records. Next, we use the order provided by this Hamiltonian path (i.e., a given permutation of the records) as input to the Hansen-Mukherjee algorithm, virtually transforming it into a multivariate microaggregation solver we call Multivariate Hansen-Mukherjee (MHM). Our intuition is that good solutions to the TSP would yield Hamiltonian paths allowing the Hansen-Mukherjee algorithm to find good solutions to the multivariate microaggregation problem. We have tested our method with well-known benchmark datasets. Moreover, with the aim to show the usefulness of our approach to protecting location privacy, we have tested our solution with real-life trajectories datasets, too. We have compared the results of our algorithm with those of the best performing solutions, and we show that our proposal reduces the information loss resulting from the microaggregation. Overall, results suggest that transforming the multivariate microaggregation problem into its univariate counterpart by ordering microdata records with a proper Hamiltonian path and applying an optimal univariate solution leads to a reduction of the perturbation error whilst keeping the same privacy guarantees.
Àrees temàtiques: Visual arts and performing arts Physics and astronomy (miscellaneous) Multidisciplinary sciences Modeling and simulation Mathematics, interdisciplinary applications Mathematics (miscellaneous) Mathematics (all) Matemática / probabilidade e estatística General mathematics Engineering (miscellaneous) Computer science (miscellaneous) Ciência da computação Chemistry (miscellaneous) Arts and humanities (miscellaneous) Architecture Applied mathematics
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: franciscojose.casino@urv.cat nadine.alkhoury@estudiants.urv.cat nadine.alkhoury@estudiants.urv.cat nadine.alkhoury@estudiants.urv.cat nadine.alkhoury@estudiants.urv.cat agusti.solanas@urv.cat
Identificador de l'autor: 0000-0003-4296-2876 0000-0002-4881-6215
Data d'alta del registre: 2024-10-12
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Symmetry-Basel. 13 (6): 916-
Referència de l'ítem segons les normes APA: Maya-Lopez, Armando; Casino, Fran; Solanas, Agusti (2021). Improving multivariate microaggregation through hamiltonian paths and optimal univariate microaggregation. Symmetry-Basel, 13(6), 916-. DOI: 10.3390/sym13060916
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2021
Tipus de publicació: Journal Publications
Repositori URV