World's Best Scientists 2026 revealed!
Ulfar Erlingsson

Ulfar Erlingsson

D-Index & Metrics

Computer Science

D-Index
52
Citations
16597
World Ranking
4971
National Ranking
2312

Overview

Úlfar Erlingsson is affiliated with Lacework in the United States. Their research is situated primarily within the field of Computer Science, with a significant focus on Artificial Intelligence. The subfields they have explored include Hardware and Architecture as well as Signal Processing.

Their work covers several main topics, notably:

  • Privacy-Preserving Technologies in Data
  • Adversarial Robustness in Machine Learning
  • Internet Traffic Analysis and Secure E-voting
  • Stochastic Gradient Optimization Techniques
  • Topic Modeling
  • Security and Verification in Computing
  • Parallel Computing and Optimization Techniques

Úlfar Erlingsson has published papers in various venues, with a concentration in:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)

Recent publications include:

  • "Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation," 2020, arXiv (Cornell University)
  • "Information-Theoretic Single-Server PIR in the Shuffle Model," 2024, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)

Beyond Úlfar Erlingsson's own authored work, there is a notable coauthor network comprising:

  • Abhradeep Thakurta (4 coauthored works)
  • Shuang Song (3 coauthored works)
  • Nicolas Papernot (2 coauthored works)
  • Steve Chien (2 coauthored works)
  • Vitaly Feldman (2 coauthored works)

The research contributions emphasize privacy and security aspects within data and machine learning contexts. Some papers from coauthors linked to Úlfar Erlingsson address topics such as differential privacy and training data extraction from large language models, reflecting a broader ecosystem of privacy-aware AI research.

Best Publications

  • Control-flow integrity principles, implementations, and applications

    Martín Abadi;Mihai Budiu;Úlfar Erlingsson;Jay Ligatti

  • RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response

    Úlfar Erlingsson;Vasyl Pihur;Aleksandra Korolova

  • Control-flow integrity

    Martín Abadi;Mihai Budiu;Úlfar Erlingsson;Jay Ligatti

  • DryadLINQ: a system for general-purpose distributed data-parallel computing using a high-level language

    Yuan Yu;Michael Isard;Dennis Fetterly;Mihai Budiu

  • The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks

    Nicholas Carlini;Chang Liu;Úlfar Erlingsson;Jernej Kos

  • Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

    Nicolas Papernot;Martín Abadi;Úlfar Erlingsson;Ian J. Goodfellow

  • SASI enforcement of security policies: a retrospective

    Úlfar Erlingsson;Fred B. Schneider

  • XFI: software guards for system address spaces

    Úlfar Erlingsson;Martín Abadi;Michael Vrable;Mihai Budiu

  • SASI enforcement of security policies: a retrospective

    U. Erlingsson;F.B. Schneider

  • Enforcing forward-edge control-flow integrity in GCC & LLVM

    Caroline Tice;Tom Roeder;Peter Collingbourne;Stephen Checkoway

  • IRM enforcement of Java stack inspection

    U. Erlingsson;F.B. Schneider

  • Prochlo: Strong Privacy for Analytics in the Crowd

    Andrea Bittau;Úlfar Erlingsson;Petros Maniatis;Ilya Mironov

  • Extracting Training Data from Large Language Models

    Nicholas Carlini;Florian Tramèr;Eric Wallace;Matthew Jagielski

  • Scalable Private Learning with PATE

    Nicolas Papernot;Shuang Song;Ilya Mironov;Ananth Raghunathan

  • Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries

    Giulia C. Fanti;Vasyl Pihur;Úlfar Erlingsson

  • The inlined reference monitor approach to security policy enforcement

    Fred B. Schneider;Úlfar Erlingsson

  • Methods and systems for implementing a secure application execution environment using derived user accounts for internet content

    Ulfar Erlingsson

  • Control-Flow Integrity - Principles, Implementations, and Applications

    Martn Abadi;Mihai Budiu;Ulfar Erlingsson;Jay Ligatti

  • Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity

    Úlfar Erlingsson;Vitaly Feldman;Ilya Mironov;Ananth Raghunathan

  • Engineering Secure Software and Systems

    Úlfar Erlingsson;Roelf J. Wieringa;Nicola Zannone

  • Amplification by shuffling: from local to central differential privacy via anonymity

    Úlfar Erlingsson;Vitaly Feldman;Ilya Mironov;Ananth Raghunathan

  • The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets

    Nicholas Carlini;Chang Liu;Jernej Kos;Úlfar Erlingsson

  • A General Approach to Adding Differential Privacy to Iterative Training Procedures

    Brendan McMahan;Galen Andrew;Ilya Mironov;Nicolas Papernot

Frequent Co-Authors

Martín Abadi
Martín Abadi Google (United States)
Ilya Mironov
Ilya Mironov Google (United States)
Nicolas Papernot
Nicolas Papernot University of Toronto
Benjamin Livshits
Benjamin Livshits Imperial College London
Kunal Talwar
Kunal Talwar Apple (United States)
Abhradeep Thakurta
Abhradeep Thakurta Google (United States)
Nicholas Carlini
Nicholas Carlini Google (United States)
Michael Isard
Michael Isard Google (United States)
Frank McSherry
Frank McSherry Materialize, Inc.
Dawn Song
Dawn Song University of California, Berkeley

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