社会计算应用中推理控制机制的私密性和实用性

Seyed Hossein Ahmadinejad, Philip W. L. Fong, R. Safavi-Naini
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引用次数: 7

摘要

现代社会计算平台(例如,Facebook)是可扩展的。第三方开发人员部署扩展(例如,Facebook应用程序)来增强底层平台的功能。先前的研究表明,用于控制用户个人信息访问的基于权限的保护机制无法控制推断——从公共信息推断私人信息。我们设想了一种可供选择的保护模型,在该模型中,用户配置文件在发布到第三方应用程序之前要经过消毒转换。每个转换指定用户概要文件的一个备选视图。与基于许可的保护不同,这个框架解决了对推理控制的需求。本工作从三个方面为基于视图的保护奠定了理论基础。首先,现有的保护隐私的数据发布工作侧重于结构化数据(例如,表),而用户配置文件是半结构化的(例如,树)。在信息论术语中,我们定义了可应用于半结构化数据的隐私和实用目标。我们对隐私和效用的概念是高度针对性的,反映了社会计算平台的设置,用户指定他们的隐私偏好,第三方应用程序将访问集中在用户配置文件的选定组件上。其次,我们定义了一个树的代数,其中先前为结构化数据设计的净化转换(例如,泛化,噪声引入等)现在根据树操作为半结构化数据制定。第三,我们通过说明如何在我们的模型中正式和定量地评估视图(消毒转换)的隐私增强和效用保存效果来评估我们模型的有用性。据我们所知,我们的工作是第一个阐明社会计算平台中第三方应用程序的推理控制机制的精确隐私和实用目标的工作。
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Privacy and Utility of Inference Control Mechanisms for Social Computing Applications
Modern social computing platforms (e.g., Facebook) are extensible. Third-party developers deploy extensions (e.g., Facebook applications) that augment the functionalities of the underlying platforms. Previous work demonstrated that permission-based protection mechanisms, adopted to control access to users' personal information, fail to control inference - the inference of private information from public information. We envision an alternative protection model in which user profiles undergo sanitizing transformations before being released to third-party applications. Each transformation specifies an alternative view of the user profile. Unlike permission-based protection, this framework addresses the need for inference control. This work lays the theoretical foundation for view-based protection in three ways. First, existing work in privacy- preserving data publishing focuses on structured data (e.g., tables), but user profiles are semi-structured (e.g., trees). In information-theoretic terms, we define privacy and utility goals that can be applied to semi-structured data. Our notions of privacy and utility are highly targeted, mirroring the set up of social computing platforms, in which users specify their privacy preferences and third-party applications focus their accesses on selected components of the user profile. Second, we define an algebra of trees in which sanitizing transformations previously designed for structured data (e.g., generalization, noise introduction, etc) are now formulated for semi-structured data in terms of tree operations. Third, we evaluate the usefulness of our model by illustrating how the privacy enhancement and utility preservation effects of a view (a sanitizing transformation) can be formally and quantitatively assessed in our model. To the best of our knowledge, ours is the first work to articulate precise privacy and utility goals of inference control mechanisms for third-party applications in social computing platforms.
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