(p, N)-identifiability: Anonymity under Practical Adversaries

Mimoto, Tomoaki; Kiyomoto, Shinsaku; Tanaka, Katsuya; Miyaji, Atsuko

doi:10.1109/trustcom/bigdatase/icess.2017.343

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…We consider matrix factorization as an anonymization method and rank r contributes to the accuracy of the matrix approximation. Moreover, we combine anonymization methods as same as some previous studies [28], [29]. Specifically, we propose to combine matrix factorization with another anonymization method ano, such as kanonymization and noise addition.…”

Section: Anonymization Using Matrix Factorizationmentioning

confidence: 99%

Anonymization Technique Based on SGD Matrix Factorization

Mimoto

Hidano

Kiyomoto

et al. 2020

IEICE Trans. Inf. & Syst.

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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 reidentification 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.

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Section: Anonymization Using Matrix Factorizationmentioning

confidence: 99%

Anonymization Technique Based on SGD Matrix Factorization

Mimoto

Hidano

Kiyomoto

et al. 2020

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

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Secure Data Management Technology

Mimoto

Kiyomoto

Miyaji

2020

Security Infrastructure Technology for Integrated Utilization of Big Data

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In this chapter, we introduce data anonymization techniques for several types of datasets. Data anonymity of anonymized datasets is an index for estimating the (maximum) reidentification risk from anonymized datasets and is generally defined as a quantitative index based on adversary models. The adversary models are implicitly defined according to the attributes in the datasets, use cases, and anonymization techniques. We first review existing anonymization techniques and the adversary models behind the data anonymity definitions for anonymization techniques; then, we propose a common anonymity definition and its adversary model, which is applicable to several types of anonymization techniques. Furthermore, some extensions of the definition, which is optimized for specific types of datasets, are presented in the chapter.

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(p, N)-identifiability: Anonymity under Practical Adversaries

Cited by 2 publications

References 18 publications

Anonymization Technique Based on SGD Matrix Factorization

Anonymization Technique Based on SGD Matrix Factorization

Secure Data Management Technology

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