BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model
BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model
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DOI：
10.29012/jpc.680
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发表时间：
2017-05
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通讯作者：
Brendan Avent;A. Korolova;David Zeber;Torgeir Hovden;B. Livshits
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作者：
Brendan Avent;A. Korolova;David Zeber;Torgeir Hovden;B. Livshits
我们提出了一种差分隐私的混合模型，该模型考虑了分别希望本地隐私模型和可信策展人模型的差分隐私保证的常规用户和选择加入用户的组合。我们证明，在该模型中，可以设计一种新型混合算法，提高所获得数据的效用，同时为用户提供所需的隐私保证。我们将该算法应用于私下计算搜索日志头部的任务，并表明与相关工作相比，混合方法在数据的实用性方面提供了显着的改进。具体来说，在两个大型搜索点击数据集（分别包含 1.75 和 16 GB）上，我们的方法在一系列隐私预算值中获得了超过 95% 的 NDCG 值。
We propose a hybrid model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model and the trusted curator model, respectively. We demonstrate that within this model, it is possible to design a new type of blended algorithm that improves the utility of obtained data, while providing users with their desired privacy guarantees. We apply this algorithm to the task of privately computing the head of the search log and show that the blended approach provides significant improvements in the utility of the data compared to related work. Specifically, on two large search click data sets, comprising 1.75 and 16 GB, respectively, our approach attains NDCG values exceeding 95% across a range of privacy budget values.