Achieving Privacy Preservation Constraints in Missing-Value Datasets

Abstract

Privacy violation issues must be taken into consideration when datasets are released for public use. To address these issues, there are various anonymization models to be proposed, e.g., k-anonymity, l-diversity, and t-closeness. However, these anonymization models generally propose to address privacy violation issues in datasets which are assumed that all attributes of them must be completed. Thus, these anonymization models could be insufficient to address privacy violation issues in such a dataset which is allowed to collect missing-values, e.g., rating datasets and trajectory datasets. Therefore, a new appropriate privacy preservation model for missing-value datasets is proposed by this work. With the proposed model, aside from privacy preservation, the data utility is also maintained as much as possible. Moreover, a suitable data utility metric for missing-value datasets is also presented by this work. Furthermore, the proposed model is shown that it is an NP-Complete problem by reduction from the X3C problem.

An Anatomization Model for Farmer Data Collections

“…For this reason, if survey framer data set collections, farmer data sets, are released to the data analyst, they could lead to be privacy violation issues. To address these issues, there are several privacy preservation models have been proposed, e.g., k-Anonymity [10,11,17] and its extended privacy preservation models such as l-Diversity [8], LKC-Privacy [2], and Anatomy [3,24].…”

“…With k-Anonymity [10,11,17], it is a well-known privacy preservation model. It is presented by Sweeney in 2002.…”

“…To achieve k-Anonymity constraints, before data sets are released for public use, all explicit identifier values of users are available in released data sets to be removed. Moreover, the unique quasi-identifier values of users are generalized [10,11,17] or suppressed [11] to be at least k indistinguishable tuples. For this reason, after data sets are satisfied by k-Anonymity constraints, they can guarantee that all possible re-identification conditions to them always have at least k tuples which are satisfied.…”

“…For this situation, Table 1 seems not to have any concern of privacy violation issues that must be addressed when it is released for public use, because it does not collect any explicit identifier of farmers such as name, surname, SSN, and citizen ID. Unfortunately, in [2,3,8,[10][11][12][13][14][15][16][17]24], and [9], the authors demonstrate that Table 1 still has the concern of privacy violation issues that must be addressed when it is released for public use.…”

“…With data sets which are highly the dimension of quasi-identifier attributes, there are a well-known privacy preservation model which is proposed, i.e., LKC-Privacy [2]. However, LKC-Privacy is generally proposed for addressing privacy violation issues in trajectory data sets [2,11]. For this reason, LKC-Privacy can also be an insufficient privacy preservation model for farmer data sets.…”