|
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.
|
SBE: Medium: Towards Personalized Privacy Assistants