动态授予隐私权的可行性：使移动隐私与用户偏好保持一致
The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences
摘要：
当前的智能电话操作系统通过在首次使用时询问的基础上提示用户来调节应用许可。先前的研究表明，这种方法是无效的，因为它没有考虑上下文：应用程序首次请求访问数据的情况可能与随后请求访问的情况有很大不同。我们进行了一项纵向的131人的实地研究，以分析用户隐私决策背后的背景，以规范对敏感资源的访问。我们构建了一个分类器，通过检测上下文何时发生变化来代表用户做出隐私决策，并在必要时根据用户过去的决策和行为推断隐私偏好。我们的目标是在没有进一步用户干预的情况下自动授予适当的资源请求，拒绝不适当的请求，并且仅在系统不确定用户偏好时提示用户。我们表明，我们的方法可以准确地预测用户的隐私决策的96.8%的时间，这是一个四倍的出错率降低相比，目前的系统。
英文摘要：
Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective because it fails to account for context: the circumstances under which an application first requests access to data may be vastly different than the circumstances under which it subsequently requests access. We performed a longitudinal 131-person field study to analyze the contextuality behind user privacy decisions to regulate access to sensitive resources. We built a classifier to make privacy decisions on the user's behalf by detecting when context has changed and, when necessary, inferring privacy preferences based on the user's past decisions and behavior. Our goal is to automatically grant appropriate resource requests without further user intervention, deny inappropriate requests, and only prompt the user when the system is uncertain of the user's preferences. We show that our approach can accurately predict users' privacy decisions 96.8% of the time, which is a four-fold reduction in error rate compared to current systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
