SBE: Medium: Towards Personalized Privacy Assistants

无论是在智能手机上、在浏览器上还是在社交网络上，人们都面临着越来越多难以管理的隐私设置。需要的是一种新的、更具伸缩性的范例，使他们能够重新控制其数据的收集和使用。人们在智能手机上下载的移动应用程序尤其如此。这些应用程序被证明可以收集和分享各种各样的敏感数据，但用户无法跟上。如果所有用户对移动应用程序的数据收集和共享做法都有相同的感受，那么将应用程序预先配置为只允许他们喜欢的做法是很容易的。不幸的是，先前的研究表明情况并非如此。该项目正在研究新技术，旨在显著简化隐私设置的配置过程，例如与移动应用程序相关的设置。拥有近2亿美国智能手机用户，每个人的设备上平均有近50个移动应用程序，该项目可能会对普通美国人的隐私产生重大影响。具体地说，这项研究利用隐私偏好建模、机器学习和对话技术的最新进展来开发个性化隐私助手，能够学习人们的隐私偏好并代表他们半自动地配置许多隐私设置。研究人员正在评估不同配置的个性化隐私助手，特别是旨在评估它们对隐私决策和用户行为影响的人体受试者实验。正在评估的配置在与用户对话的方式和频率、使用机器学习来驱动这些对话的方式以及配置隐私设置的自动化程度方面有所不同。人体实验着眼于各种因素，包括不同技术配置对用户隐私设置舒适度的影响，他们的总体意识和控制感，以及短期和长期行为影响。其他重要因素包括用户负担、中断频率和总体用户满意度。

Whether it is on their smartphones, in their browsers or on social networks, people are confronted with an increasingly unmanageable number of privacy settings. What is needed is a new, more scalable paradigm that empowers them to regain control over the collection and use of their data. This is particularly the case for mobile apps people download on their smartphones. These apps have been shown to collect and share a wide variety of sensitive data, with users unable to keep up. If all users felt the same way about the data collection and sharing practices of mobile apps, it would easy to have the apps pre-configured to only allow for those practices with which they are comfortable. Unfortunately, prior research has shown that this is not the case. This project is studying novel technology intended to significantly simplify the process of configuring privacy settings such as those associated with mobile apps. With close to 200 million US smartphone users, each with an average of nearly 50 mobile apps on their devices, this project could have a significant impact on the privacy of everyday Americans. Specifically, this research harnesses recent advances in privacy preference modeling, machine learning and dialogue technologies to develop personalized privacy assistants capable of learning people's privacy preferences and of semi-automatically configuring many privacy settings on their behalf. The researchers are evaluating different configurations of personalized privacy assistants, focusing in particular on human subject experiments intended to evaluate their impact on privacy decision making and user behavior. Configurations being evaluated differ in the style and frequency of dialogues with users, the way in which machine learning is used to drive these dialogues and the level of automation in configuring privacy settings. Human subject experiments look at factors that include the impact of different configurations of the technologies on the level of comfort users have with their privacy settings, their overall awareness and sense of control, and both short-term and long-term behavioral effects. Other important factors include user burden, frequency of interruptions and overall user satisfaction.