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01.04.2020

Flexible sensitive K-anonymization on transactions

verfasst von: Yu-Chuan Tsai, Shyue-Liang Wang, I-Hsien Ting, Tzung-Pei Hong

Erschienen in: World Wide Web | Ausgabe 4/2020

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Abstract

In recent years, privacy breaches have been a great concern on the published data. Only removing one’s personal identification information is not sufficient to protect individual’s privacy. Privacy preservation technology for published data is devoted to preventing re-identification and retaining the useful information in published data. In this work, we propose a novel algorithm to deal with sensitive and quasi-identifier items, respectively, in transactional data. The proposed algorithm maintains at least the same or a stronger privacy level for transactional data with 1/k. In numerical experiments, our proposed algorithm shows better running time and better data utility.

Vorheriger Artikel Measurement and analysis on large-scale offline mobile app dissemination over device-to-device sharing in mobile social networks

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Titel: Flexible sensitive K-anonymization on transactions
verfasst von: Yu-Chuan Tsai
Shyue-Liang Wang
I-Hsien Ting
Tzung-Pei Hong
Publikationsdatum: 01.04.2020
Verlag: Springer US
Erschienen in: World Wide Web / Ausgabe 4/2020
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI: https://doi.org/10.1007/s11280-020-00798-8

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