TWC SBE: TTP Option: Small: A User-Tailored Approach to Privacy Decision Support

许多调查发现，互联网用户希望限制正在收集的关于他们的个人数据，以及控制他们的数据的使用。美国现有和拟议的法规以个人数据收集者的“透明度和控制”义务的形式赋予用户此类权利：应告知用户请求个人数据的理由，以便他们能够做出明智的决定是否披露其数据。在过去几年中，透明度和控制模式显然“行不通”。“它使用户负担过重，因为必须在很短的时间内做出大量的决定。此外，最近的研究表明，当用户不得不在数据披露的直接利益与未来不确定和不具体的隐私威胁之间进行权衡时，他们无法做出理性的决策。基于实证研究，本研究有助于预测哪些隐私决策将符合用户的偏好，并生成个性化的隐私选择的默认设置以及披露的理由，最适合用户的预测决策。用户可以覆盖任何预测，这种修正将随着时间的推移修改预测算法。因此，所提出的方法提供了“现实的授权”：它允许互联网用户作出自己的隐私决定，如果他们愿意，也帮助他们克服有限理性的限制，首先代表他们作出个性化的默认决定。研究设计中使用的多种方法相互借鉴，代表了一种严格而系统的方法，可以深入了解设计和开发智能默认隐私设置。该项目可以通过提供对预测个人上下文隐私偏好的系统性理解，对隐私文献产生变革性影响。

Numerous surveys find that Internet users want to limit the personal data that is being collected about them, as well as control the usage of their data. Existing and proposed regulation in the U.S. accords users such rights, in the form of a "transparency and control" obligation on personal data collectors: users should be informed about the rationale of requests for personal data so that they can make an informed decision on whether or not to disclose their data. In the past few years, it became apparent that the transparency and control paradigm "does not work." It overburdens users by the sheer amount of decisions that must be made in a very short period of time. Recent research moreover shows that users are not cognitively able to make rational decisions when they have to trade off immediate benefits of data disclosure with uncertain and unspecific privacy threats sometimes in the future. Based on empirical studies, this research helps predict what privacy decisions would be consistent with users' preferences, and generate personalized default settings for privacy choices as well as rationales for disclosure that best suits users' predicted decision-making. Users can override any of the predictions, and such corrections will modify the prediction algorithm over time. The proposed approach thus affords "realistic empowerment": it allows Internet users to make their own privacy decisions if they wish, and also helps them overcome the limits of their bounded rationality by first making personalized default decisions on behalf of them. The multiple methods used in the research design build upon one another, representing a rigorous and systematic approach to develop an in-depth understanding of designing and developing smart default privacy settings. This project can have a transformative impact on the privacy literature by providing a systematic understanding on predicting individuals' contextual privacy preferences.