Autor segons l'article: Domingo-Ferrer, Josep; Sanchez, David; Ricci, Sara; Munoz-Batista, Monica
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Domingo Ferrer, Josep / Sánchez Ruenes, David
Paraules clau: Variance Semantic similarity Security Nominal data Information-content Data splitting Data privacy Computation Cloud computing
Resum: © 2019, Springer-Verlag London Ltd., part of Springer Nature. Outsourcing data storage and computation to the cloud is appealing due to the cost savings it entails. However, when the data to be outsourced contain private information, appropriate protection mechanisms should be implemented by the data controller. Data splitting, which consists of fragmenting the data and storing them in separate clouds for the sake of privacy preservation, is an interesting alternative to encryption in terms of flexibility and efficiency. However, multivariate analyses on data split among various clouds are challenging, and they are even harder when data are nominal categorical (i.e., textual, non-ordinal), because the standard arithmetic operators cannot be used. In this article, we tackle the problem of outsourcing multivariate analyses on nominal data split over several honest-but-curious clouds. Specifically, we propose several secure protocols to outsource to multiple clouds the computation of a variety of multivariate analyses on nominal categorical data (frequency-based and semantic-based). Our protocols have been designed to outsource as much workload as possible to the clouds, in order to retain the cost-saving benefits of cloud computing while ensuring that the outsourced stay split and hence privacy-protected versus the clouds. The experiments we report on the Amazon cloud service show that by using our protocols the controller can save nearly all the runtime because it can integrate partial results received from the clouds with very little computation.
Àrees temàtiques: Software Interdisciplinar Information systems Human-computer interaction Hardware and architecture Engenharias iv Computer science, information systems Computer science, artificial intelligence Ciência da computação Astronomia / física Artificial intelligence
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 0219-1377
Adreça de correu electrònic de l'autor: david.sanchez@urv.cat josep.domingo@urv.cat
Identificador de l'autor: 0000-0001-7275-7887 0000-0001-7213-4962
Pàgina final: 2326
Data d'alta del registre: 2024-10-12
Volum de revista: 62
Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
Enllaç font original: https://link.springer.com/article/10.1007%2Fs10115-019-01424-4
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Knowledge And Information Systems. 62 (6): 2301-2326
Referència de l'ítem segons les normes APA: Domingo-Ferrer, Josep; Sanchez, David; Ricci, Sara; Munoz-Batista, Monica (2020). Outsourcing analyses on privacy-protected multivariate categorical data stored in untrusted clouds. Knowledge And Information Systems, 62(6), 2301-2326. DOI: 10.1007/s10115-019-01424-4
DOI de l'article: 10.1007/s10115-019-01424-4
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2020
Pàgina inicial: 2301
Tipus de publicació: Journal Publications