InforLorV4, Main, Exploration, bibRecord, 002036

Utility-Driven Anonymization in Data Publishing

Identifieur interne : 002036 ( Main/Exploration ); précédent : 002035; suivant : 002037

Utility-Driven Anonymization in Data Publishing

Auteurs : Mingqiang Xue [Singapour] ; Panagiotis Karras [États-Unis] ; Chedy Raïssi [France] ; Hung Keng Pung [Singapour]

Source :

RBID : Hal:inria-00623578

Abstract

Privacy-preserving data publication has been studied intensely in the past years. To date, all existing approaches transform data values by random perturbation or generalization. In this paper, we introduce a radically different data anonymization methodology. Our proposal aims to maintain a certain amount of {\em patterns}, defined in terms of a set of properties of interest that hold for the original data. Such properties are represented as linear relationships among data points. We present an algorithm that generates a set of anonymized data that strictly preserves these properties, thus maintaining specified {\em patterns} in the data. Extensive experiments with real and synthetic data show that our algorithm is efficient, and produces anonymized data that affords high utility in several data analysis tasks while safeguarding privacy.

Url:

https://hal.inria.fr/inria-00623578

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 005213
to stream Hal, to step Curation: 005213
to stream Hal, to step Checkpoint: 001A86
to stream Main, to step Merge: 002079
to stream Main, to step Curation: 002036

Le document en format XML

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<front><div type="abstract" xml:lang="en">Privacy-preserving data publication has been studied intensely in the past years. To date, all existing approaches transform data values by random perturbation or generalization. In this paper, we introduce a radically different data anonymization methodology. Our proposal aims to maintain a certain amount of {\em patterns}, defined in terms of a set of properties of interest that hold for the original data. Such properties are represented as linear relationships among data points. We present an algorithm that generates a set of anonymized data that strictly preserves these properties, thus maintaining specified {\em patterns} in the data. Extensive experiments with real and synthetic data show that our algorithm is efficient, and produces anonymized data that affords high utility in several data analysis tasks while safeguarding privacy.</div>
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Utility-Driven Anonymization in Data Publishing

Utility-Driven Anonymization in Data Publishing

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Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

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