A Semi-Supervised Learning Approach to Differential Privacy
By Geetha Jagannathan, Claire Monteleoni, Krishnan Pillaipakkamnatt. 2013
24 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Science Applications, Representation & Self-Supervised Learning, Privacy & Security
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
- Nicolas Papernot
- Nicholas Carlini
- Kunal Talwar
- Ian J. Goodfellow
- Ú. Erlingsson
- Vitaly Feldman
- Florian Tramèr
- Dawn Song
In Computer Science Applications
- Jihun Hamm
- Nicolas Papernot
- Kunal Talwar
- Mikhail Belkin
- Ú. Erlingsson
- Yingjun Cao
- Vitaly Feldman
- Jonathan Ullman
In Representation & Self-Supervised Learning
- Nicolas Papernot
- Kunal Talwar
- Mikhail Belkin
- Ian J. Goodfellow
- Ú. Erlingsson
- Martín Abadi
- Yingjun Cao
- Jihun Hamm
In Privacy & Security
- Nicolas Papernot
- Jihun Hamm
- Kunal Talwar
- Ú. Erlingsson
- Mikhail Belkin
- Ian J. Goodfellow
- Martín Abadi
- Yingjun Cao
Citations in the corpus
- Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (2016, background): “For instance, Jagannathan et al. (2013) learned privacy-preserving random forests.”
- Learning privately from multiparty data (2016, methodology): “Using nonprivate unlabeled data, which is more general than using labeled data, was demonstrated specifically to assist learning of random forests (Jagannathan et al., 2013).”