User-controllable learning of location privacy policies with gaussian mixture models

  title={User-controllable learning of location privacy policies with gaussian mixture models},
  author={Justin Cranshaw and Jonathan Mugan and Norman M. Sadeh},
  booktitle={AAAI 2011},
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… CONTINUE READING
Highly Cited
This paper has 39 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 17 extracted citations

SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices

2017 IEEE Symposium on Security and Privacy (SP) • 2017
View 1 Excerpt

Privacy Dynamics: Learning Privacy Norms for Social Software

2016 IEEE/ACM 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) • 2016
View 3 Excerpts


Publications referenced by this paper.
Showing 1-10 of 14 references

Location-sharing technologies: Privacy risks and controls

J. Tsai, P. Kelley, L. Cranor, N. Sadeh
ISJLP 6(2):119–151. • 2010
View 1 Excerpt

MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering

C. Fraley, A. E. Raftery
Technical Report 504, Department of Statistics, University of Washington. • 2010
View 1 Excerpt

Preferencebased search using example-critiquing with suggestions

P. Viappiani, B. Faltings, P. Pu
Journal of Artificial Intelligence Research 27(1):465–503. • 2006
View 1 Excerpt

Generative versus discriminative methods for object recognition

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) • 2005
View 1 Excerpt

Similar Papers

Loading similar papers…