k-Anonymity and l-diversity are two well-known privacy metrics that guarantee protection of the respondents of a dataset by obfuscating information that can disclose their identities and sensitive information. Existing solutions for enforcing them implicitly assume to operate in a centralized scenario, since they require complete visibility over the dataset to be anonymized, and can therefore have limited applicability in anonymizing large datasets. In this paper, we propose a solution that extends Mondrian (an efficient and effective approach designed for achieving k-anonymity) for enforcing both k-anonymity and l-diversity over large datasets in a distributed manner, leveraging the parallel computation of multiple workers. Our approach efficiently distributes the computation among the workers, without requiring visibility over the dataset in its entirety. Our data partitioning limits the need for workers to exchange data, so that each worker can independently anonymize a portion of the dataset. We implemented our approach providing parallel execution on a dynamically chosen number of workers. The experimental evaluation shows that our solution provides scalability, while not affecting the quality of the resulting anonymization.

Scalable Distributed Data Anonymization for Large DatasetsIn: IEEE TRANSACTIONS ON BIG DATA. - ISSN 2332-7790. - (2022). [Epub ahead of print] [10.1109/TBDATA.2022.3207521]

Scalable Distributed Data Anonymization for Large Datasets

S. De Capitani di Vimercati
Primo
;
S. Foresti;G. Livraga;P. Samarati
Ultimo
2022

Abstract

k-Anonymity and l-diversity are two well-known privacy metrics that guarantee protection of the respondents of a dataset by obfuscating information that can disclose their identities and sensitive information. Existing solutions for enforcing them implicitly assume to operate in a centralized scenario, since they require complete visibility over the dataset to be anonymized, and can therefore have limited applicability in anonymizing large datasets. In this paper, we propose a solution that extends Mondrian (an efficient and effective approach designed for achieving k-anonymity) for enforcing both k-anonymity and l-diversity over large datasets in a distributed manner, leveraging the parallel computation of multiple workers. Our approach efficiently distributes the computation among the workers, without requiring visibility over the dataset in its entirety. Our data partitioning limits the need for workers to exchange data, so that each worker can independently anonymize a portion of the dataset. We implemented our approach providing parallel execution on a dynamically chosen number of workers. The experimental evaluation shows that our solution provides scalability, while not affecting the quality of the resulting anonymization.
Distributed data anonymization; Mondrian; k-Anonymity; l-Diversity; Apache Spark
Settore INF/01 - Informatica
H20_RIA19PSAMA_01 - Multi-Owner data Sharing for Analytics and Integration respecting Confidentiality and Owner control (MOSAICrOWN) - SAMARATI, PIERANGELA - H20_RIA - Horizon 2020_Research & Innovation Action/Innovation Action - 2019
HE_GC22PSAMA_01 - Green responsibLe privACy preservIng dAta operaTIONs - SAMARATI, PIERANGELA - Horizon Europe Global Challenge-RIA/IA/CSA - 2022
PRIN201719SDECA_01 - High quality Open data Publishing and Enrichment (HOPE) - DE CAPITANI DI VIMERCATI, SABRINA - PRIN2017 - PRIN bando 2017 - 2019
H20_RIA21PSAMA_01 - Machine Learning-based, Networking and Computing Infrastructure Resource Management of 5G and beyond Intelligent Networks (MARSAL) - SAMARATI, PIERANGELA - H20_RIA - Horizon 2020_Research & Innovation Action/Innovation Action - 2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/940404
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