Explore an advanced permission mechanism for Android called SmarPer in this IEEE conference talk. Learn how contextual information and machine learning methods can be used to predict runtime permission decisions, addressing limitations in current mobile platform permission systems. Discover the implementation of SmarPer and its data collection campaign involving 8,521 runtime permission decisions from 41 participants. Examine the effectiveness of a Bayesian linear regression model in achieving an 80% correct classification rate, representing a significant improvement over static permission policies. Investigate the concept of data obfuscation as an alternative to binary allow/deny decisions, offering users a balance between privacy and utility. Gain insights into the potential for automating permission management in smartphones based on real-world permission decision patterns.