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Adversarially learned representations for information obfuscation and inference

Publication ,  Conference
Bertran, M; Martinez, N; Papadaki, A; Qiu, Q; Rodrigues, M; Reeves, G; Sapiro, G
Published in: 36th International Conference on Machine Learning, ICML 2019
January 1, 2019

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 collectors and users to design procedures that minimize sensitive information leakage. Balancing the competing objectives of providing meaningful individualized service levels and inference while obfuscating sensitive information is still an open problem. In this work, we take an information theoretic approach that is implemented as an unconstrained adversarial game between Deep Neural Networks in a principled, data-driven manner. This approach enables us to learn domain-preserving stochastic transformations that maintain performance on existing algorithms while minimizing sensitive information leakage.

Duke Scholars

Published In

36th International Conference on Machine Learning, ICML 2019

Publication Date

January 1, 2019

Volume

2019-June

Start / End Page

960 / 974
 

Citation

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Bertran, M., Martinez, N., Papadaki, A., Qiu, Q., Rodrigues, M., Reeves, G., & Sapiro, G. (2019). Adversarially learned representations for information obfuscation and inference. In 36th International Conference on Machine Learning, ICML 2019 (Vol. 2019-June, pp. 960–974).
Bertran, M., N. Martinez, A. Papadaki, Q. Qiu, M. Rodrigues, G. Reeves, and G. Sapiro. “Adversarially learned representations for information obfuscation and inference.” In 36th International Conference on Machine Learning, ICML 2019, 2019-June:960–74, 2019.
Bertran M, Martinez N, Papadaki A, Qiu Q, Rodrigues M, Reeves G, et al. Adversarially learned representations for information obfuscation and inference. In: 36th International Conference on Machine Learning, ICML 2019. 2019. p. 960–74.
Bertran, M., et al. “Adversarially learned representations for information obfuscation and inference.” 36th International Conference on Machine Learning, ICML 2019, vol. 2019-June, 2019, pp. 960–74.
Bertran M, Martinez N, Papadaki A, Qiu Q, Rodrigues M, Reeves G, Sapiro G. Adversarially learned representations for information obfuscation and inference. 36th International Conference on Machine Learning, ICML 2019. 2019. p. 960–974.

Published In

36th International Conference on Machine Learning, ICML 2019

Publication Date

January 1, 2019

Volume

2019-June

Start / End Page

960 / 974