Abstract
Differential Privacy (DP) is well-known for its strong privacy guarantee. In this paper, we show that when there are correlations among attributes in the dataset, only relying on DP is not sufficient to defend against the "attribute linkage attack", which is a well-known privacy attack aiming at deducing participant's attribute information. Our contributions are 1 we show that the attribute linkage attack can be initiated with high probability even when data are protected under DP, 2 we propose an enhanced DP standard called "APL-Free ∈-DP", 3 by leveraging on topology theory, we design an algorithm "APLKiller" which satisfies this standard. Finally, experiments show that our algorithm not only eliminates the attribute linkage attack, but also achieves better data utility.
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