Data di Pubblicazione:
2021
Citazione:
Artifact: Scalable Distributed Data Anonymization / S. De Capitani di Vimercati, D. Facchinetti, S. Foresti, G. Oldani, S. Paraboschi, M. Rossi, P. Samarati - In: 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)[s.l] : IEEE, 2021. - ISBN 978-1-6654-4724-9. - pp. 450-451 (( convegno PerCom tenutosi a Kassel nel 2021 [10.1109/PerComWorkshops51409.2021.9431059].
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.
Tipologia IRIS:
03 - Contributo in volume
Elenco autori:
S. De Capitani di Vimercati, D. Facchinetti, S. Foresti, G. Oldani, S. Paraboschi, M. Rossi, P. Samarati
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Link al Full Text:
Titolo del libro:
2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)