Journal
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
Volume 16, Issue 4, Pages 580-593Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TDSC.2017.2698472
Keywords
Privacy; data mining; l-diversity; t-closeness
Categories
Funding
- JSPS KAKENHI [26330081, 26870201, 16K12411, 17H04705]
- Telecommunications Advancement Foundation
- Grants-in-Aid for Scientific Research [17H04705, 26870201, 16K12411] Funding Source: KAKEN
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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.
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