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.
Settore INF/01 - Informatica
Multi-Owner data Sharing for Analytics and Integration respecting Confidentiality and Owner control (MOSAICrOWN)
Machine Learning-based, Networking and Computing Infrastructure Resource Management of 5G and beyond Intelligent Networks (MARSAL)
PRIN201719SDECA_01 - High quality Open data Publishing and Enrichment (HOPE) - DE CAPITANI DI VIMERCATI, SABRINA - PRIN2017 - PRIN bando 2017 - 2019
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/869834
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