Full Citation
Title: Final Versions of Tools for Data Sanitisation and Computation
Citation Type: Miscellaneous
Publication Year: 2021
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Abstract: This document reports on the implementation efforts associated with the development of two tools supporting novel techniques for data sanitization. The proposed approaches specifically focus on scenarios where different parties aim at collaborating to anonymize a dataset or to compute (in a privacy preserving manner) statistics over private data. The document first illustrates an anonymization approach that operates in a distributed scenario. The proposal specifically extends the Mondrian algorithm to leverage the presence of multiple workers to improve scalability and enable the efficient anonymization of large data collections. The developed tool is able to compute a k-anonymous and `-diverse version of the dataset relying on an arbitrary number of workers, without affecting information loss. The tool has received the Best Artifact Award at the IEEE PerCom 2021 Conference. The document then presents a novel solution for computing the differentially private median over the union of two private datasets. The performance of the developed solution outperforms existing approaches, while maintaining utility comparable to computations performed in a centralized scenario.
Url: https://mosaicrown.eu/wp-content/uploads/2021/10/D5.4.pdf
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Authors: Paraboschi, Stefano; Facchinetti, Dario; Oldani, Gianluca; Rossi, Matthew
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Data Collections: IPUMS USA
Topics: Methodology and Data Collection
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