Adversarially Learned Representations for Information Obfuscation and Inference
By Martín Bertrán, Natalia Martínez, Afroditi Papadaki, Qiang Qiu, Miguel Raul Dias Rodrigues, Galen Reeves, Guillermo Sapiro. 2019
60 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Abhishek Singh
- Vivek Sharma
- Praneeth Vepakomma
- Ramesh Raskar
- Ayush Chopra
- Ethan Garza
- Bashir Sadeghi
- Emily Zhang
Citations in the corpus
- DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks (2020, methodology): “Baselines For attacks on sensitive attributes, we baseline with ARL based methods [29, 24, 23] which are state-ofthe-art on attribute leakage.”
- Adversarial representation learning for synthetic replacement of private attributes (2020, methodology): “Similar evaluation method are used in Roy & Boddeti (2019) and Bertran et al. (2019).”
- On the Global Optima of Kernelized Adversarial Representation Learning (2019, background): “of approaches [6, 28, 3, 26, 20, 23, 1] have used and argued [18] for explicit adversarial networks as a proxy to mimic an unknown adversary seeking to extract the sensitive attributes from the encoded data.”
- Obfuscation via Information Density Estimation (2019, methodology): “For example, in [1], an optimization problem similar to the privacy funnel [36] is formulated”