In this paper, we address privacy issues related to ranked retrieval model in web databases, each of which takes private attributes as part of input in the ranking function. Many web databases keep private attributes invisible to public and believe that the adversary is unable to reveal the private attribute values from query results. However, prior research (Rahman et al. in Proc VLDB Endow 8:1106–17, 2015) studied the problem of rank-based inference of private attributes over web databases. They found that one can infer the value of private attributes of a victim tuple by issuing well-designed queries through a top-k query interface. To address the privacy issue, in this paper, we propose a novel privacy-preserving framework. Our framework protects private attributes’ privacy not only under inference attacks but also under arbitrary attack methods. In particular, we classify adversaries into two widely existing categories: domain-ignorant and domain-expert adversaries. Then, we develop equivalent set with virtual tuples (ESVT) for domain-ignorant adversaries and equivalent set with true tuples (ESTT) for domain-expert adversaries. The ESVT and the ESTT are the primary parts of our privacy-preserving framework. To evaluate the performance, we define a measurement of privacy guarantee for private attributes and measurements for utility loss. We prove that both ESVT and ESTT achieve the privacy guarantee. We also develop heuristic algorithms for ESVT and ESTT, respectively, under the consideration of minimizing utility loss. We demonstrate the effectiveness of our techniques through theoretical analysis and extensive experiments over real-world dataset.

中文翻译：

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用于排名检索模型的隐私保护框架

在本文中，我们解决了与Web数据库中排名检索模型相关的隐私问题，每个隐私模型都将私有属性作为排名功能输入的一部分。许多Web数据库使私有属性对公众不可见，并认为对手无法从查询结果中揭示私有属性值。然而，先前的研究（Rahman等人，Proc VLDB Endow 8：1106–17，2015）研究了基于排序的网络数据库私有属性推断问题。他们发现，可以通过top-k查询界面发出精心设计的查询，从而推断出受害者元组的私有属性的值。为了解决隐私问题，本文提出了一种新颖的隐私保护框架。我们的框架不仅在推断攻击下而且在任意攻击方法下都保护私有属性的隐私。特别是，我们将对手分为两个广泛存在的类别：领域无知者和领域专家。然后，我们为不了解领域的对手开发了带有虚拟元组（ESVT）的等效集，为领域专家提供了带有真实元组（ESTT）的等效集。ESVT和ESTT是我们隐私保护框架的主要部分。为了评估性能，我们定义了针对私有属性的隐私保证度量和针对公用事业损失的度量。我们证明ESVT和ESTT均达到了隐私保证。考虑到最小化公用事业损失，我们还分别为ESVT和ESTT开发了启发式算法。