Full Citation

Title: Artifact: Scalable Distributed Data Anonymization

Citation Type: Miscellaneous

Publication Year: 2021

ISBN:

ISSN:

DOI: 10.18128/D010.V10.0

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Abstract: We describe the artifact, publicly available at [1], that implements the proposal in [2], and the reproduction of the experimental results. It is an extended and distributed version of the Mondrian anonymization algorithm. Our solution anonymizes large datasets by partitioning data among workers in a distributed setting. It provides parallel execution on a dynamically chosen number of workers, limiting their interaction and data exchange.

Url: https://spdp.di.unimi.it/papers/percom2021-artifact.pdf

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Authors: De Capitani Di Vimercati, Sabrina; Facchinetti, Dario; Foresti, Sara; Oldani, Gianluca; Paraboschi, Stefano; Rossi, Matthew; Samarati, Pierangela

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Data Collections: IPUMS USA

Topics: Population Data Science

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