Adversarial representation learning for synthetic replacement of private attributes
By John Martinsson, Edvin Listo Zec, Daniel Gillblad, Olof Mogren. 2020
9 citations recorded by the source corpus.
Data privacy is an increasingly important aspect of many real-world analytics tasks. Data sources that contain sensitive information may have immense potential which could be unlocked using the right privacy enhancing transformations, but current methods often fail to produce convincing output. Furthermore, finding the right balance between privacy and utility is often a tricky trade-off. In this work, we propose a novel approach for data privatization, which involves two steps: in the first step, it removes the sensitive information, and in the second step, it replaces this information with an independent random sample. Our method builds on adversarial representation learning which ensures strong privacy by training the model to fool an increasingly strong adversary. While previous methods only aim at obfuscating the sensitive information, we find that adding new random information in its place strengthens the provided privacy and provides better utility at any given level of privacy. The result is an approach that can provide stronger privatization on image data, and yet be preserving both the domain and the utility of the inputs, entirely independent of the downstream task.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Praneeth Vepakomma
- Abhishek Singh
- Ramesh Raskar
- Vivek Sharma
- Ethan Garza
- Emily Zhang
- Ayush Chopra
- Otkrist Gupta
In Computer Vision and Pattern Recognition
- Ramesh Raskar
- Praneeth Vepakomma
- Abhishek Singh
- Vivek Sharma
- Ethan Garza
- Emily Zhang
- Ayush Chopra
- Luc Van Gool
Citations in the corpus
- DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks (2020, background): “While not motivating private collaborative inference, a few recent works [23, 24] have attempted the related problem of attribute leakage [24, 25, 26, 27, 28] by focusing on adversarial representation learning (ARL).”