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Multi-Domains Personalized Local Differential Privacy Frequency Estimation Mechanism for Utility Optimization

COMPUTERS & SECURITY(2025)

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Abstract
Local Differential Privacy (LDP) has garnered considerable attention in recent years because it does not rely on trusted third parties and has low interactivity and high operational efficiency. However, current LDP frequency estimation mechanisms aggregate data using different privacy budgets within the same domain of attribute values, overlooking the aggregation requirements across different domains of attribute values. This limits the potential for enhancing the data utility under fixed privacy budgets and meeting user preferences in multiple domains of attribute values and privacy budgets. To address this issue, we define a Multi-Domains Personalized Local Differential Privacy (MDPLDP) model that allows users to freely choose domains of attribute values and privacy budgets according to their privacy preferences. Furthermore, based on the MDPLDP model, two new frequency estimation mechanisms are proposed: MDPLDP-Generalized Randomized Response and MDPLDP-basic Randomized Aggregatable Privacy-Preserving Ordinal Response. These mechanisms support cross-domains data aggregation and optimize data utility by adjusting the domains of attribute values and increasing privacy budgets. Theoretical analysis reveals that these new mechanisms have lower estimation errors than the traditional LDP mechanisms. Experiments on real and synthetic datasets demonstrate that the proposed mechanisms effectively reduce estimation errors and enhance the utility of data-frequency estimation.
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Key words
Privacy protection,Multi-domains,Personalized local differential privacy,Frequency estimation,Utility optimization
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Xue Qiao, Gansu University of Political Science and Law,Wang Jian
2021

被引用23 | 浏览

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要点】:本文提出了一种多域个性化本地差分隐私模型(MDPLDP),并基于此模型提出了两种新的频率估计机制,以优化数据效用并满足用户在不同属性值域和隐私预算下的偏好。

方法】:通过允许用户根据隐私偏好自由选择属性值域和隐私预算,并基于MDPLDP模型,提出MDPLDP-Generalized Randomized Response和MDPLDP-basic Randomized Aggregatable Privacy-Preserving Ordinal Response两种新的频率估计机制。

实验】:在真实和合成数据集上进行实验,结果表明,所提出的新机制能有效降低估计误差并提高数据频率估计的效用。数据集名称未在摘要中明确提及。