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Author Cranshaw, Justin ♦ Mugan, Jonathan ♦ Sadeh, Norman
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Multivariate Gaussian Mixture ♦ Human-understandable Change ♦ User-controllable Learning ♦ Gaussian Mixture Model ♦ Generative Modeling Approach ♦ Subsequent Surge ♦ User-controllable Method ♦ Location Privacy Policy ♦ Machine Learning ♦ Serious Pri-vacy Concern ♦ Underlying Policy ♦ Cumbersome Task ♦ User-controllable Setting ♦ Real Location-sharing Policy ♦ Live Location-sharing Social Network ♦ New Data Ar-rives ♦ Semi-supervised Setting
Description With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applications that con-tinuously track the location of users. However, serious pri-vacy concerns arise as people start to widely adopt these ap-plications. Users will need to maintain policies to determine under which circumstances to share their location. Specify-ing these policies however, is a cumbersome task, suggest-ing 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 ar-rives. 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 policies that do not evolve incrementally. Additionally, we highlight the strength of the generative modeling approach we take, by showing how our model easily extends to the semi-supervised setting.
In Proc. of the AAAI
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2011-01-01