Skip to main content

Data Value Estimation for Privacy-Preserving Big/Personal Data Businesses

  • Kiyomoto S
N/ACitations
Citations of this article
2Readers
Mendeley users who have this article in their library.
Get full text

Abstract

``Value Proposition{''} is a key factor when designing a business model. In personalized services, data value should be estimated using a certain model of data valuation. Generally, the data value depends on the size and precision of data, and it is expected to reflect the parameter k in the size of k-anonymized data sets and its data precision. A data set is said to have k-anonymity if each record is indistinguishable from at least k - 1 other records with respect to certain identifying attributes called quasi-identifiers. The parameter k influences not only the re-identification risk of the published data but also its value. When k is large, many attributes in the published data are replaced with uncharacteristic values in order to satisfy k-anonymity. On the other hand, a small k involves a serious risk of re-identification. There is a trade-off between privacy level and data value in the generation of k-anonymized data sets. Based only on the privacy requirement for reducing the re-identification risk, many people may assent to distribution of their private data in the form of a k-anonymized table when k is large enough or where as large k as possible is chosen. In this paper, we present a model for finding an appropriate k in k-anonymization. The model suggests that an optimal k exists that is appropriate to achieve a balance between value and anonymity when personal data are published.

Cite

CITATION STYLE

APA

Kiyomoto, S. (2016). Data Value Estimation for Privacy-Preserving Big/Personal Data Businesses (pp. 149–158). https://doi.org/10.1007/978-4-431-55342-7_14

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free