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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/16235

Title: Anonymization Technique based on SGD Matrix Factrization
Authors: Mimoto, Tomoaki
Hidano, Seira
Kiyomoto, Shinsaku
Miyaji, Atsuko
Keywords: time-sequence data
anonymization
matrix factorization
privacy and utility
Issue Date: 2020-02-01
Publisher: 電子情報通信学会
Magazine name: IEICE Transactions on Information and Systems
Volume: E103-D
Number: 2
Start page: 299
End page: 308
DOI: 10.1587/transinf.2019INP0013
Abstract: Time-sequence data is high dimensional and contains a lot of information, which can be utilized in various fields, such as insurance, finance, and advertising. Personal data including time-sequence data is converted to anonymized datasets, which need to strike a balance between both privacy and utility. In this paper, we consider low-rank matrix factorization as one of anonymization methods and evaluate its efficiency. We convert time-sequence datasets to matrices and evaluate both privacy and utility. The record IDs in time-sequence data are changed at regular intervals to reduce re-identification risk. However, since individuals tend to behave in a similar fashion over periods of time, there remains a risk of record linkage even if record IDs are different. Hence, we evaluate the re-identification and linkage risks as privacy risks of time-sequence data. Our experimental results show that matrix factorization is a viable anonymization method and it can achieve better utility than existing anonymization methods.
Rights: Copyright (C)2020 IEICE. Tomoaki Mimoto, Seira Hidano, Shinsaku Kiyomoto, and Atsuko Miyaji, IEICE Transactions on Information and Systems, E103-D(2), 2020, 299-308. https://www.ieice.org/jpn/trans_online/
URI: http://hdl.handle.net/10119/16235
Material Type: publisher
Appears in Collections:b10-1. 雑誌掲載論文 (Journal Articles)

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