Articles producció científica> Enginyeria Informàtica i Matemàtiques

Multi-Dimensional Randomized Response

  • Dades identificatives

    Identificador: imarina:9139056
    Autors:
    Domingo-Ferrer, JosepSoria-Comas, Jordi
    Resum:
    IEEE In our data world, a host of not necessarily trusted controllers gather data on individual subjects. To preserve her privacy and, more generally, her informational self-determination, the individual has to be empowered by giving her agency on her own data. Maximum agency is afforded by local anonymization, that allows each individual to anonymize her own data before handing them to the data controller. Randomized response (RR) is a local anonymization approach able to yield multi-dimensional full sets of anonymized microdata that are valid for exploratory analysis and machine learning. This is so because an unbiased estimate of the distribution of the true data of individuals can be obtained from their pooled randomized data. Furthermore, RR offers rigorous privacy guarantees. The main weakness of RR is the curse of dimensionality when applied to several attributes: as the number of attributes grows, the accuracy of the estimated true data distribution quickly degrades. We propose several complementary approaches to mitigate the dimensionality problem. First, we present two basic protocols, separate RR on each attribute and joint RR for all attributes, and discuss their limitations. Then we introduce an algorithm to form clusters of attributes so that attributes in different clusters can be viewed as independent and joint RR can be performed within each cluster. After that, we introduce an adjustment algorithm for the randomized data set that repairs some of the accuracy loss due to assuming independence between attributes when using RR separately on each attribute or due to assuming independence between clusters in cluster-wise RR. We also present empirical work to illustrate the proposed methods.
  • Altres:

    Autor segons l'article: Domingo-Ferrer, Josep; Soria-Comas, Jordi
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Domingo Ferrer, Josep / SORIA COMAS, JORGE
    Paraules clau: Randomized response Protocols Privacy preserving data publishing Privacy Phase change random access memory Multivariate data Local anonymization Estimation Differential privacy Data privacy Curse of dimensionality Clustering algorithms randomized response protocols privacy preserving data publishing phase change random access memory multivariate data local anonymization estimation differential privacy data privacy curse of dimensionality clustering algorithms
    Resum: IEEE In our data world, a host of not necessarily trusted controllers gather data on individual subjects. To preserve her privacy and, more generally, her informational self-determination, the individual has to be empowered by giving her agency on her own data. Maximum agency is afforded by local anonymization, that allows each individual to anonymize her own data before handing them to the data controller. Randomized response (RR) is a local anonymization approach able to yield multi-dimensional full sets of anonymized microdata that are valid for exploratory analysis and machine learning. This is so because an unbiased estimate of the distribution of the true data of individuals can be obtained from their pooled randomized data. Furthermore, RR offers rigorous privacy guarantees. The main weakness of RR is the curse of dimensionality when applied to several attributes: as the number of attributes grows, the accuracy of the estimated true data distribution quickly degrades. We propose several complementary approaches to mitigate the dimensionality problem. First, we present two basic protocols, separate RR on each attribute and joint RR for all attributes, and discuss their limitations. Then we introduce an algorithm to form clusters of attributes so that attributes in different clusters can be viewed as independent and joint RR can be performed within each cluster. After that, we introduce an adjustment algorithm for the randomized data set that repairs some of the accuracy loss due to assuming independence between attributes when using RR separately on each attribute or due to assuming independence between clusters in cluster-wise RR. We also present empirical work to illustrate the proposed methods.
    Àrees temàtiques: Interdisciplinar Information systems Engineering, electrical & electronic Computer science, information systems Computer science, artificial intelligence Computer science applications Computational theory and mathematics Ciência da computação
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: josep.domingo@urv.cat
    Identificador de l'autor: 0000-0001-7213-4962
    Data d'alta del registre: 2024-10-12
    Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
    Enllaç font original: https://ieeexplore.ieee.org/document/9298881
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Ieee Transactions On Knowledge And Data Engineering. 34 (10): 4933-4946
    Referència de l'ítem segons les normes APA: Domingo-Ferrer, Josep; Soria-Comas, Jordi (2022). Multi-Dimensional Randomized Response. Ieee Transactions On Knowledge And Data Engineering, 34(10), 4933-4946. DOI: 10.1109/TKDE.2020.3045759
    DOI de l'article: 10.1109/TKDE.2020.3045759
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2022
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Computational Theory and Mathematics,Computer Science Applications,Computer Science, Artificial Intelligence,Computer Science, Information Systems,Engineering, Electrical & Electronic,Information Systems
    Randomized response
    Protocols
    Privacy preserving data publishing
    Privacy
    Phase change random access memory
    Multivariate data
    Local anonymization
    Estimation
    Differential privacy
    Data privacy
    Curse of dimensionality
    Clustering algorithms
    randomized response
    protocols
    privacy preserving data publishing
    phase change random access memory
    multivariate data
    local anonymization
    estimation
    differential privacy
    data privacy
    curse of dimensionality
    clustering algorithms
    Interdisciplinar
    Information systems
    Engineering, electrical & electronic
    Computer science, information systems
    Computer science, artificial intelligence
    Computer science applications
    Computational theory and mathematics
    Ciência da computação
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