Privacy-Constrained Relational Data Perturbation: An Empirical Evaluation


Deokyeon Jang, Minsoo Kim, Yon Dohn Chung, Journal of Information Processing Systems Vol. 20, No. 4, pp. 524-534, Aug. 2024  

https://doi.org/10.3745/JIPS.04.0316
Keywords: Anonymization, Data Perturbation, data privacy, Personal Data Protection
Fulltext:

Abstract

The release of relational data containing personal sensitive information poses a significant risk of privacy breaches. To preserve privacy while publishing such data, it is important to implement techniques that ensure protection of sensitive information. One popular technique used for this purpose is data perturbation, which is popularly used for privacy-preserving data release due to its simplicity and efficiency. However, the data perturbation has some limitations that prevent its practical application. As such, it is necessary to propose alternative solutions to overcome these limitations. In this study, we propose a novel approach to preserve privacy in the release of relational data containing personal sensitive information. This approach addresses an intuitive, syntactic privacy criterion for data perturbation and two perturbation methods for relational data release. Through experiments with synthetic and real data, we evaluate the performance of our methods.


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Cite this article
[APA Style]
Jang, D., Kim, M., & Chung, Y. (2024). Privacy-Constrained Relational Data Perturbation: An Empirical Evaluation. Journal of Information Processing Systems, 20(4), 524-534. DOI: 10.3745/JIPS.04.0316.

[IEEE Style]
D. Jang, M. Kim, Y. D. Chung, "Privacy-Constrained Relational Data Perturbation: An Empirical Evaluation," Journal of Information Processing Systems, vol. 20, no. 4, pp. 524-534, 2024. DOI: 10.3745/JIPS.04.0316.

[ACM Style]
Deokyeon Jang, Minsoo Kim, and Yon Dohn Chung. 2024. Privacy-Constrained Relational Data Perturbation: An Empirical Evaluation. Journal of Information Processing Systems, 20, 4, (2024), 524-534. DOI: 10.3745/JIPS.04.0316.