Corpus ID: 10744615

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

@inproceedings{Cranshaw2011UserControllableLO,
  title={User-Controllable Learning of Location Privacy Policies With Gaussian Mixture Models},
  author={Justin Cranshaw and J. Mugan and N. Sadeh},
  booktitle={AAAI},
  year={2011}
}
  • Justin Cranshaw, J. Mugan, N. Sadeh
  • Published in AAAI 2011
  • Computer Science
  • 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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    References

    SHOWING 1-10 OF 15 REFERENCES
    Understanding and capturing people’s privacy policies in a mobile social networking application
    • 324
    • PDF
    Privacy wizards for social networking sites
    • 441
    • PDF
    Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs
    • 172
    • PDF
    Location privacy in pervasive computing
    • 584
    • PDF
    Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
    • B. Gedik, L. Liu
    • Computer Science
    • IEEE Transactions on Mobile Computing
    • 2008
    • 832
    • PDF
    Learning travel recommendations from user-generated GPS traces
    • 364
    • PDF
    Location-Sharing Technologies: Privacy Risks and Controls
    • 171
    • PDF
    Getting to know you: learning new user preferences in recommender systems
    • 568
    • PDF