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Content-Based Privacy for Consumer-Produced Multimedia

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Multimedia Data Mining and Analytics

Abstract

We contend that current and future advances in Internet scale Multimedia analytics , global Inference , and linking can circumvent traditional Security and Privacy barriers. We therefore are in dire need of a new research field to address this issue and come up with new solutions. We present the privacy risks, Attack vectors , details for a preliminary experiment on Account linking , and describe mitigation and educational techniques that will help address the issues.

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Notes

  1. 1.

    In many countries, this practice is possibly illegal but exists in a gray area and is seemingly routine practice. The Galleon insider trading trial [32] was based largely on the use of expert network consultants.

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Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. CNS-1065240. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We thank Michael Ellsworth for his rewording suggestions.

Author information

Authors and Affiliations

  1. International Computer Science Institute, 1947 Center Street, Suite 600, Berkeley, CA, 94704-1159, USA

    Gerald Friedland, Adam Janin, Howard Lei, Jaeyoung Choi & Robin Sommer

Authors
  1. Gerald Friedland
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  2. Adam Janin
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  3. Howard Lei
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  4. Jaeyoung Choi
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  5. Robin Sommer
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Corresponding author

Correspondence to Gerald Friedland .

Editor information

Editors and Affiliations

  1. IBM Corp., Durham, North Carolina, USA

    Aaron K. Baughman

  2. Nokia Inc., Sunnyvale, California, USA

    Jiang Gao

  3. Google Inc., Mountain View, California, USA

    Jia-Yu Pan

  4. 4i, Inc., Carlsbad, California, USA

    Valery A. Petrushin

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Friedland, G., Janin, A., Lei, H., Choi, J., Sommer, R. (2015). Content-Based Privacy for Consumer-Produced Multimedia. In: Baughman, A., Gao, J., Pan, JY., Petrushin, V. (eds) Multimedia Data Mining and Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-14998-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-14998-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14997-4

  • Online ISBN: 978-3-319-14998-1

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