A large body of literature exists for studying Location obfuscation in different contexts. However, the obfuscation functions generated by existing systems are not easily customizable by end users. Users might find it difficult to understand the parameters involved (e.g., obfuscation range and granularity of location representation) and set realistic trade-offs between privacy and utility. In this paper, we propose a new framework called, TACO, i.e., Tree-based Approach to Customizing location Obfuscation, which can generate location obfuscation functions that provide strong privacy guarantees while being easily customizable via user-specified policies. First, we develop a semantic representation of a given region using tree structure. These data structures assist users in specifying their privacy requirements using policies. Second, we design a rigorous privacy model based on Geo-Indistinguishability for TACO using this tree structure. Third, we implement enforcement techniques in TACO to translate user policies to appropriate parameters and generate a robust, customized obfuscation function for each user. Finally, we carry out experiments on real world datasets to evaluate the effectiveness of the framework under different settings.

翻译：有大量文献用于在不同背景下研究位置模糊问题。 但是,现有系统产生的模糊功能不容易由终端用户定制。 用户可能发现难以理解所涉参数( 如位置代表的模糊范围与颗粒), 难以在隐私与实用性之间做出现实的权衡。 在本文中, 我们提出了一个名为“ TACO ”的新框架, 即“ 以树为基础的定制位置模糊化方法 ”, 它可以产生位置模糊功能, 提供强有力的隐私保障, 而同时又容易通过用户指定的政策定制。 首先, 我们用树结构来开发一个特定区域的语义代表。 这些数据结构有助于用户使用政策来说明其隐私要求。 其次, 我们设计一个基于地理分解性和实用性的严格隐私模型。 第三, 我们在 TACO 中应用执行技术, 将用户政策转换为适当的参数, 并为每个用户生成一个强大、 定制的模糊功能。 最后, 我们在现实世界数据设置下进行实验, 以评价不同框架的有效性 。