Úlfar Erlingsson
Úlfar Erlingsson has worked across many areas of computer systems, security, privacy and machine learning. He is currently focused on the security of Cloud software.
Authored Publications
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Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Vitaly Feldman
Ilya Mironov
Ananth Raghunathan
Kunal Talwar
Abhradeep Thakurta
ACM-SIAM Symposium on Discrete Algorithms (SODA) (2019)
Reducing Permission Requests in Mobile Apps
Martin Pelikan
Giles Hogben
Proceedings of ACM Internet Measurement Conference (IMC) (2019)
Scalable Private Learning with PATE
Ilya Mironov
Ananth Raghunathan
Kunal Talwar
International Conference on Learning Representations (ICLR) (2018)
A General Approach to Adding Differential Privacy to Iterative Training Procedures
Galen Andrew
Ilya Mironov
Steve Chien
NIPS (2018)
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
Ian Goodfellow
Ilya Mironov
Kunal Talwar
Li Zhang
Proceedings of 30th IEEE Computer Security Foundations Symposium (CSF) (2017), pp. 1-6
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
Nicolas Papernot
Ian Goodfellow
Kunal Talwar
Proceedings of the International Conference on Learning Representations (2017)
Prochlo: Strong Privacy for Analytics in the Crowd
Andrea Bittau
Ilya Mironov
Ananth Raghunathan
David Lie
Ushasree Kode
Julien Tinnes
Bernhard Seefeld
Proceedings of the Symposium on Operating Systems Principles (SOSP) (2017), pp. 441-459
Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries
Giulia Fanti
Vasyl Pihur
Proceedings on Privacy Enhancing Technologies (PoPETS), issue 3, 2016 (2016)
