Scalable Distributed Data Anonymization for Large Datasets Article Swipe
Sabrina De Capitani di Vimercati
,
Dario Facchinetti
,
Sara Foresti
,
Giovanni Livraga
,
Gianluca Oldani
,
Stefano Paraboschi
,
Matthew Rossi
,
Pierangela Samarati
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tbdata.2022.3207521
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tbdata.2022.3207521
-Anonymity and -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 -anonymity) for enforcing both -anonymity and -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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tbdata.2022.3207521
- https://ieeexplore.ieee.org/ielx7/6687317/7153538/09894678.pdf
- OA Status
- hybrid
- Cited By
- 10
- References
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- Related Works
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- OpenAlex ID
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- OpenAlex ID
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https://openalex.org/W4296436747Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tbdata.2022.3207521Digital Object Identifier
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Scalable Distributed Data Anonymization for Large DatasetsWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-19Full publication date if available
- Authors
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Sabrina De Capitani di Vimercati, Dario Facchinetti, Sara Foresti, Giovanni Livraga, Gianluca Oldani, Stefano Paraboschi, Matthew Rossi, Pierangela SamaratiList of authors in order
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https://doi.org/10.1109/tbdata.2022.3207521Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6687317/7153538/09894678.pdfDirect link to full text PDF
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hybridOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6687317/7153538/09894678.pdfDirect OA link when available
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Notation, Anonymity, Computer science, Visibility, Theoretical computer science, Algorithm, Information retrieval, Mathematics, Computer security, Arithmetic, Physics, OpticsTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 5, 2024: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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