
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
Abhradeep Thakurta
Kunal Talwar
Ananth Raghunathan
Ilya Mironov
Vitaly Feldman
ACM-SIAM Symposium on Discrete Algorithms (SODA) (2019)
Reducing Permission Requests in Mobile Apps
Giles Hogben
Martin Pelikan
Proceedings of ACM Internet Measurement Conference (IMC) (2019)
Scalable Private Learning with PATE
Kunal Talwar
Ananth Raghunathan
Ilya Mironov
International Conference on Learning Representations (ICLR) (2018)
A General Approach to Adding Differential Privacy to Iterative Training Procedures
Steve Chien
Ilya Mironov
Galen Andrew
NIPS (2018)
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
Nicolas Papernot
Kunal Talwar
Ian Goodfellow
Proceedings of the International Conference on Learning Representations (2017)
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
Kunal Talwar
Li Zhang
Ian Goodfellow
Ilya Mironov
Proceedings of 30th IEEE Computer Security Foundations Symposium (CSF) (2017), pp. 1-6
Prochlo: Strong Privacy for Analytics in the Crowd
Andrea Bittau
Ananth Raghunathan
Julien Tinnes
Ilya Mironov
David Lie
Bernhard Seefeld
Ushasree Kode
Proceedings of the Symposium on Operating Systems Principles (SOSP) (2017), pp. 441-459
Data-driven software security: Models and methods
IEEE Computer Security Foundations Symposium (2016)