Abstract
K-Anonymity algorithms are used as essential methods to protect end users’ data privacy. However, state-of-art K-Anonymity algorithms have shortcomings such as lacking generalization and suppression value priority standard. Moreover, the complexity of these algorithms are usually high. Thus, a more robust and efficient K-Anonymity algorithm is needed for practical usage. In this paper, a novel K-Anonymous full domain generalization algorithm based on heap sort is presented. We first establish the k-anonymous generalization priority standard of information. Then our simulation results show the user’s data privacy can be effectively protected while generalization efficiency is also improved.
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