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.
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].
Artifact: Scalable Distributed Data Anonymization
S. De Capitani di Vimercati;S. Foresti;P. Samarati
2021
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.
| File | Dimensione | Formato | |
|---|---|---|---|
|
dffoprs-percom2021-artifact.pdf
accesso aperto
Tipologia:
Pre-print (manoscritto inviato all'editore)
Dimensione
317.32 kB
Formato
Adobe PDF
|
317.32 kB | Adobe PDF | Visualizza/Apri |
|
Artifact_Scalable_Distributed_Data_Anonymization.pdf
accesso riservato
Tipologia:
Publisher's version/PDF
Dimensione
106.35 kB
Formato
Adobe PDF
|
106.35 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
