Data Anonymity in Multi-Party Service Model

Kiyomoto, Shinsaku; Fukushima, Kazuhide; Miyake, Yutaka

doi:10.1007/978-3-642-27189-2_3

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2012

2020

Publication Types

Select...

Other2

Book1

Relationship

Self Cite1

Independent2

Authors

Journals

Cited by 3 publications

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Secure Data Management Technology

Mimoto

Kiyomoto

Miyaji

2020

Security Infrastructure Technology for Integrated Utilization of Big Data

Self Cite

View full text Add to dashboard Cite

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.

show abstract