Achieving Privacy Preservation Constraints based on K-Anonymity in conjunction with Adjacency Matrix and Weighted Graphs
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Abstract
A well-known privacy preservation model is k-anonymity. It is simple and widely applied in several real-life systems. To achieve k-anonymity constraints in datasets, all explicit identifiers of users are removed. Furthermore, the unique quasi-identifiers of users are distorted by their less specific values to be at least k indistinguishable tuples. For this reason, after datasets are satisfied by k-anonymity constraints, they can guarantee that all possible query conditions to them always have at least k tuples that are satisfied. Aside from achieving privacy preservation constraints, the data utility and the complexity of data transformation are serious issues that must also be considered when datasets are released. Therefore, both privacy preservation models are proposed in this work. They are based on k-anonymity constraints in conjunction with the weighted graph of correlated distortion tuples and the adjacency matrix of tuple distances. The proposed models aim to preserve data privacy in datasets. Moreover, the data utility and data transform complexities are also considered in the privacy preservation constraint of the proposed models. Furthermore, we show that the proposed data transformation technique is more efficient and effective by using extensive experiments.
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