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Outsourcing analyses on privacy-protected multivariate categorical data stored in untrusted clouds

  • Identification data

    Identifier: imarina:6090483
    Authors:
    Domingo-Ferrer, JosepSanchez, DavidRicci, SaraMunoz-Batista, Monica
    Abstract:
    © 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.
  • Others:

    Author, as appears in the article.: Domingo-Ferrer, Josep; Sanchez, David; Ricci, Sara; Munoz-Batista, Monica
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Domingo Ferrer, Josep / Sánchez Ruenes, David
    Keywords: Variance Semantic similarity Security Nominal data Information-content Data splitting Data privacy Computation Cloud computing
    Abstract: © 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.
    Thematic Areas: 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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 0219-1377
    Author's mail: david.sanchez@urv.cat josep.domingo@urv.cat
    Author identifier: 0000-0001-7275-7887 0000-0001-7213-4962
    Last page: 2326
    Record's date: 2024-10-12
    Journal volume: 62
    Papper version: info:eu-repo/semantics/acceptedVersion
    Link to the original source: https://link.springer.com/article/10.1007%2Fs10115-019-01424-4
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Knowledge And Information Systems. 62 (6): 2301-2326
    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
    Article's DOI: 10.1007/s10115-019-01424-4
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2020
    First page: 2301
    Publication Type: Journal Publications
  • Keywords:

    Artificial Intelligence,Computer Science, Artificial Intelligence,Computer Science, Information Systems,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software
    Variance
    Semantic similarity
    Security
    Nominal data
    Information-content
    Data splitting
    Data privacy
    Computation
    Cloud computing
    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
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