Artifact: Scalable Distributed Data Anonymization
Sabrina De Capitani di Vimercati, Dario Facchinetti, Sara Foresti, Gianluca Oldani, Stefano Paraboschi, Matthew Rossi, Pierangela Samarati
In Proceedings of 19th IEEE International Conference on Pervasive Computing and Communications (PerCom)
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.
@InProceedings {dffoprs-percom21-artifact,
author = {S. {De Capitani di Vimercati} and D. Facchinetti and S. Foresti and G. Oldani and S. Paraboschi and M. Rossi and P. Samarati},
booktitle = {Proc. of the 19th International Conference on Pervasive Computing and Communications (PerCom 2021)},
title = {Artifact: Scalable Distributed Data Anonymization},
month = {March},
day = {22-26},
year = {2021}
}