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},
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
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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
1 Excerpt

Preferencebased search using example-critiquing with suggestions

  • P. Viappiani, B. Faltings, P. Pu
  • Journal of Artificial Intelligence Research 27(1…
  • 2006
1 Excerpt