Predicting User Privacy Preferences based on Dynamic Interpersonal Relationships and Content Sensitivity Analysis

基于动态人际关系和内容敏感性分析预测用户隐私偏好

基本信息

项目摘要

An increasing number of users are registered in online social media and contribute a tremendous amount of personal user-generated content. Sharing content can however endanger users' privacy and have serious consequences if such media are accessible to an inappropriate audience. Examples include job dismissals or loss of health-insurance benefits. To protect their privacy, users can manually control the release and access of their content using existing solutions. However, the current state-of-art has repeatedly been demonstrated to be inefficient in appropriately supporting users in this task. Both the commonly observed substantial number of users' contacts and the high sharing frequency make the manual selection of access control rules a cumbersome and time-consuming process. Due to the lack of intuitiveness of existing solutions, users often do not modify default sharing settings and renounce in updating them over time. This regularly results in sharing more content than desired with an unsuited audience, hence putting the users' privacy at stake. In contrast, other users refrain from sharing content online by fear of selecting an inappropriate audience due to the complexity of existing solutions. It hence potentially reduces the benefits drawn by the users from their online social network. The goal of this proposal is therefore to allow users to simultaneously take advantage of online social networks while better protecting their privacy. We aim at addressing the shortcomings of currently deployed solutions and providing the foundations for a novel form of access control mechanisms. A promising approach that we will explore in this project is to dynamically suggest sharing settings tailored to the users. We will examine how accurate suggestions can be generated based on features of interpersonal relationships between users and their social contacts and the sensitivity of the content to be published with regards to the users' privacy. To cater for the usability of our approach, we will follow a user-centric approach and develop a solution that is easy to use and comprehend by non-expert users in order to foster its acceptance at large scale. We will hence investigate and develop innovative methods to autonomously infer both the nature and the strengths of the users' social relationships from their communication patterns and the degree of content sensitivity based on its characteristics, respectively. The developed methods will respect users' privacy by preferring local processing of readily available personal data on their devices. In our project, we will hence adopt the novel approach of mining users' data to help them in better protecting their privacy, by providing them more fine-granular access control mechanisms while simultaneously reducing the associated configuration burden.
越来越多的用户在在线社交媒体上注册,贡献了大量的个人用户生成的内容。然而,如果不适当的受众可以访问此类媒体,共享内容可能会危及用户的隐私,并产生严重后果。这些例子包括解雇或失去医疗保险福利。为了保护他们的隐私,用户可以使用现有解决方案手动控制其内容的发布和访问。然而,目前的技术水平一再被证明在适当地支持这项任务中的用户方面效率低下。通常观察到的大量用户联系人和高共享频率都使得手动选择访问控制规则成为一个繁琐和耗时的过程。由于现有解决方案缺乏直观性,用户通常不修改默认共享设置,并放弃随着时间的推移进行更新。这通常会导致与不合适的受众分享比预期更多的内容,从而将用户的隐私置于危险之中。相比之下,其他用户则不愿在网上分享内容,因为他们担心由于现有解决方案的复杂性而选择了不合适的受众。因此,它可能会减少用户从他们的在线社交网络中获得的好处。因此,这项提议的目标是允许用户同时利用在线社交网络,同时更好地保护他们的隐私。我们的目标是解决目前部署的解决方案的缺点,并为新形式的访问控制机制提供基础。我们将在这个项目中探索的一种很有前途的方法是动态建议根据用户定制的共享设置。我们将研究如何根据用户与其社交联系之间的人际关系的特点以及即将发布的内容对用户隐私的敏感性来生成准确的建议。为了满足我们方法的可用性,我们将遵循以用户为中心的方法,开发一个易于使用和非专业用户理解的解决方案,以促进其大规模接受。因此,我们将研究和开发创新的方法,分别从用户的沟通模式和基于其特征的内容敏感程度来自主推断用户社会关系的性质和优势。开发的方法将尊重用户的隐私,更喜欢在他们的设备上对随时可用的个人数据进行本地处理。因此,在我们的项目中,我们将采用挖掘用户数据的新方法,通过为他们提供更细粒度的访问控制机制,同时减少相关的配置负担,帮助他们更好地保护自己的隐私。

项目成果

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Professorin Dr.-Ing. Delphine Reinhardt其他文献

Professorin Dr.-Ing. Delphine Reinhardt的其他文献

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{{ truncateString('Professorin Dr.-Ing. Delphine Reinhardt', 18)}}的其他基金

PrivacyON: Bridging the Gap between Society and the Privacy Ecosystem
PrivacyON:弥合社会与隐私生态系统之间的差距
  • 批准号:
    505982147
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants

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Collaborative Research: SaTC: CORE: Medium: Compliance as a Service (CaSe): A Reflective Approach to Enforcing User Privacy Regulations
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User-Level Local Differential Privacy
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