Corpus ID: 174800114

Adversarially Learned Representations for Information Obfuscation and Inference

@inproceedings{Bertrn2019AdversariallyLR,
  title={Adversarially Learned Representations for Information Obfuscation and Inference},
  author={Mart{\'i}n Bertr{\'a}n and N. Mart{\'i}nez and A. Papadaki and Q. Qiu and M. Rodrigues and G. Reeves and G. Sapiro},
  booktitle={ICML},
  year={2019}
}
  • Martín Bertrán, N. Martínez, +4 authors G. Sapiro
  • Published in ICML 2019
  • Computer Science
  • Data collection and sharing are pervasive aspects of modern society. This process can either be voluntary, as in the case of a person taking a facial image to unlock his/her phone, or incidental, such as traffic cameras collecting videos on pedestrians. An undesirable side effect of these processes is that shared data can carry information about attributes that users might consider as sensitive, even when such information is of limited use for the task. It is therefore desirable for both data… CONTINUE READING
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    References

    SHOWING 1-10 OF 40 REFERENCES
    Adversarial Image Perturbation for Privacy Protection A Game Theory Perspective
    • 77
    • PDF
    Privacy-preserving deep learning
    • Reza Shokri, Vitaly Shmatikov
    • Computer Science
    • 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)
    • 2015
    • 829
    • PDF
    Protecting Visual Secrets Using Adversarial Nets
    • 38
    • PDF
    Privacy-preserving Machine Learning through Data Obfuscation
    • 30
    • PDF
    Learning Adversarially Fair and Transferable Representations
    • 193
    • PDF
    Generative Adversarial Nets
    • 20,098
    • PDF
    Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images
    • 31
    • PDF
    Adversarial Discriminative Domain Adaptation
    • 1,743
    • PDF