Sciweavers

Share
AAAI
2011

User-Controllable Learning of Location Privacy Policies With Gaussian Mixture Models

7 years 5 months ago
User-Controllable Learning of Location Privacy Policies With Gaussian Mixture Models
With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applications that continuously track the location of users. However, serious privacy concerns arise as people start to widely adopt these applications. Users will need to maintain policies to determine under which circumstances to share their location. Specifying these policies however, is a cumbersome task, suggesting that machine learning might be helpful. In this paper, we present a user-controllable method for learning location sharing policies. We use a classifier based on multivariate Gaussian mixtures that is suitably modified so as to restrict the evolution of the underlying policy to favor incremental and therefore human-understandable changes as new data arrives. We evaluate the model on real location-sharing policies collected from a live location-sharing social network, and we show that our method can learn policies in a user-controllable setting that are just as accurate as policie...
Justin Cranshaw, Jonathan Mugan, Norman M. Sadeh
Added 12 Dec 2011
Updated 12 Dec 2011
Type Journal
Year 2011
Where AAAI
Authors Justin Cranshaw, Jonathan Mugan, Norman M. Sadeh
Comments (0)
books