We present an approach for enabling a distributed anonymization process over large collections of sensor data. Our approach anonymizes large datasets (which might not fit in main memory) using an arbitrary number of workers within the Spark framework. We describe how to parallelize the anonymization process through a proper partitioning of the dataset. Our experimental evaluation shows that the proposed approach is scalable and do not affect the quality of the anonymized dataset.

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. 401-403 (( convegno PerCom tenutosi a Kassel nel 2021 [10.1109/PerComWorkshops51409.2021.9431063].

Scalable Distributed Data Anonymization

S. De Capitani di Vimercati;S. Foresti;P. Samarati
2021

Abstract

We present an approach for enabling a distributed anonymization process over large collections of sensor data. Our approach anonymizes large datasets (which might not fit in main memory) using an arbitrary number of workers within the Spark framework. We describe how to parallelize the anonymization process through a proper partitioning of the dataset. Our experimental evaluation shows that the proposed approach is scalable and do not affect the quality of the anonymized dataset.
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: https://hdl.handle.net/2434/869830
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