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

  • Kiyomoto S
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``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.




Kiyomoto, S. (2016). Data Value Estimation for Privacy-Preserving Big/Personal Data Businesses (pp. 149–158).

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