Privacy-Constrained Relational Data Perturbation: An Empirical Evaluation
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초록
The release of relational data containing personal sensitive information poses a significant risk of privacybreaches. To preserve privacy while publishing such data, it is important to implement techniques that ensureprotection of sensitive information. One popular technique used for this purpose is data perturbation, which ispopularly used for privacy-preserving data release due to its simplicity and efficiency. However, the dataperturbation has some limitations that prevent its practical application. As such, it is necessary to proposealternative solutions to overcome these limitations. In this study, we propose a novel approach to preserveprivacy in the release of relational data containing personal sensitive information. This approach addresses anintuitive, syntactic privacy criterion for data perturbation and two perturbation methods for relational datarelease. Through experiments with synthetic and real data, we evaluate the performance of our methods.
Abstract
The release of relational data containing personal sensitive information poses a significant risk of privacybreaches. To preserve privacy while publishing such data, it is important to implement techniques that ensureprotection of sensitive information. One popular technique used for this purpose is data perturbation, which ispopularly used for privacy-preserving data release due to its simplicity and efficiency. However, the dataperturbation has some limitations that prevent its practical application. As such, it is necessary to proposealternative solutions to overcome these limitations. In this study, we propose a novel approach to preserveprivacy in the release of relational data containing personal sensitive information. This approach addresses anintuitive, syntactic privacy criterion for data perturbation and two perturbation methods for relational datarelease. Through experiments with synthetic and real data, we evaluate the performance of our methods.
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