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Anonymization of Sensitive Quasi-Identifiers for l-diversity and t-closeness Sei, Yuichi Okumura, Hiroshi Takenouchi, Takao Ohsuga, Akihiko © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is not the published version. Please cite only the published version. privacy data mining l-diversity t-closeness A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice. In this paper, we refer to these attributes as sensitive QIDs and we propose novel privacy models, namely, (l1, ..., lq)-diversity and (t1, ..., tq)-closeness, and a method that can treat sensitive QIDs. Our method is composed of two algorithms: an anonymization algorithm and a reconstruction algorithm. The anonymization algorithm, which is conducted by data holders, is simple but effective, whereas the reconstruction algorithm, which is conducted by data analyzers, can be conducted according to each data analyzer’s objective. Our proposed method was experimentally evaluated using real data sets. IEEE 2019-07 eng journal article AM https://uec.repo.nii.ac.jp/records/8922 10.1109/TDSC.2017.2698472 https://doi.org/10.1109/TDSC.2017.2698472 1545-5971 IEEE Transactions on Dependable and Secure Computing 16 4 580 593 https://uec.repo.nii.ac.jp/record/8922/files/1-TDSC.pdf application/pdf 4.1 MB 2019-01-21