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

大量调查发现，互联网用户希望限制正在收集的有关他们的个人数据，并控制他们的数据的使用。美国现有的和拟议的法规赋予用户这样的权利，形式是对个人数据收集者的“透明和控制”义务：用户应该被告知请求个人数据的理由，以便他们能够在知情的情况下决定是否披露他们的数据。在过去的几年里，很明显，透明度和控制范例“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好处与不确定和不具体的未来隐私威胁之间进行权衡时，他们在认知上没有能力做出理性的决定。在实证研究的基础上，本研究有助于预测哪些隐私决策将与用户的偏好一致，并为隐私选择生成个性化的默认设置，以及最适合用户预测决策的披露理由。用户可以覆盖任何预测，这样的校正将随着时间的推移修改预测算法。因此，建议的方法提供了“现实赋权”：它允许互联网用户根据自己的意愿做出自己的隐私决定，并帮助他们克服有限理性的限制，首先代表他们做出个性化的默认决定。研究设计中使用的多种方法相互构建，代表了一种严格而系统的方法，以深入了解设计和开发智能默认隐私设置。这个项目可以通过提供对预测个人背景隐私偏好的系统理解，对隐私文献产生变革性的影响。

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.