Matthew Jagielski

2.6k total citations · 1 hit paper
19 papers, 621 citations indexed

About

Matthew Jagielski is a scholar working on Artificial Intelligence, Computer Networks and Communications and Control and Systems Engineering. According to data from OpenAlex, Matthew Jagielski has authored 19 papers receiving a total of 621 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 2 papers in Computer Networks and Communications and 2 papers in Control and Systems Engineering. Recurrent topics in Matthew Jagielski's work include Privacy-Preserving Technologies in Data (11 papers), Adversarial Robustness in Machine Learning (10 papers) and Cryptography and Data Security (5 papers). Matthew Jagielski is often cited by papers focused on Privacy-Preserving Technologies in Data (11 papers), Adversarial Robustness in Machine Learning (10 papers) and Cryptography and Data Security (5 papers). Matthew Jagielski collaborates with scholars based in United States, Switzerland and Mexico. Matthew Jagielski's co-authors include Cristina Nita-Rotaru, Alina Oprea, Chang Liu, Battista Biggio, Bo Li, Nicholas Carlini, Florian Tramèr, Shinichi Shiraishi, Chung‐Wei Lin and Reza Shokri and has published in prestigious journals such as Journal of Cryptology, Repository for Publications and Research Data (ETH Zurich) and arXiv (Cornell University).

In The Last Decade

Matthew Jagielski

18 papers receiving 602 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

2018 456 citations Matthew Jagielski, Alina Oprea et al. UNICA IRIS Institutional Research Information System (University of Cagliari) profile →

Peers

Countries citing papers authored by Matthew Jagielski

Since Specialization

Citations

This map shows the geographic impact of Matthew Jagielski's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthew Jagielski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Jagielski more than expected).

Fields of papers citing papers by Matthew Jagielski

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew Jagielski. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthew Jagielski. The network helps show where Matthew Jagielski may publish in the future.

Co-authorship network of co-authors of Matthew Jagielski

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Jagielski. A scholar is included among the top collaborators of Matthew Jagielski based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matthew Jagielski. Matthew Jagielski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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19 of 19 papers shown

#	Work	Indexed citations
1	Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training 2025 ·(unknown), Matthew Jagielski, Katherine Lee, (unknown), David A. Smith, Christopher A. Choquette-Choo	1
2	Differentially Private Prototypes for Imbalanced Transfer Learning 2025 ·Proceedings of the AAAI Conference on Artificial Intelligence ·(unknown), Matthew Jagielski, Adam Dziedzic, Franziska Boenisch	0
3	Poisoning Web-Scale Training Datasets is Practical 2024 ·Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, (unknown), (unknown), Hyrum S. Anderson, Andreas Terzis, Kurt Thomas, Florian Tramèr	26
4	Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models 2024 ·(unknown), (unknown), Natalia Ponomareva, Alexey Kurakin, Matthew Jagielski, Milad Nasr	3
5	Noise Masking Attacks and Defenses for Pretrained Speech Models 2024 ·Matthew Jagielski, Om Thakkar, Lun Wang	2
6	How to Combine Membership-Inference Attacks on Multiple Updated Machine Learning Models 2023 ·Proceedings on Privacy Enhancing Technologies ·Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan Ullman, Roxana Geambasu	1
7	Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy 2023 ·Repository for Publications and Research Data (ETH Zurich) ·Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, (unknown), Nicholas Carlini	11
8	SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning 2023 ·(unknown), Matthew Jagielski, Alina Oprea	2
9	SNAP: Efficient Extraction of Private Properties with Poisoning 2023 ·(unknown), (unknown), Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan Ullman	9
10	Network-Level Adversaries in Federated Learning 2022 ·(unknown), Matthew Jagielski, (unknown), Yuxuan Wang, Alina Oprea, Cristina Nita-Rotaru	8
11	Subverting Fair Image Search with Generative Adversarial Perturbations 2022 ·arXiv (Cornell University) ·Avijit Ghosh, Matthew Jagielski, Christo Wilson	3
12	Truth Serum 2022 ·Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security ·Florian Tramèr, Reza Shokri, (unknown), (unknown), Matthew Jagielski, Sanghyun Hong, Nicholas Carlini	32
13	Secure Communication Channel Establishment: TLS 1.3 (over TCP Fast Open) versus QUIC 2021 ·Journal of Cryptology ·Shan Chen, Samuel Jero, Matthew Jagielski, Alexandra Boldyreva, Cristina Nita-Rotaru	5
14	Auditing Differentially Private Machine Learning: How Private is Private SGD? 2020 ·Neural Information Processing Systems ·Matthew Jagielski, Jonathan Ullman, Alina Oprea	3
15	High-Fidelity Extraction of Neural Network Models. 2019 ·arXiv (Cornell University) ·Matthew Jagielski, Nicholas Carlini, David Berthelot, А.В. Куракин, Nicolas Papernot	12
16	On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning Attacks. 2018 ·arXiv (Cornell University) ·Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, Fabio Roli	2
17	Threat Detection for Collaborative Adaptive Cruise Control in Connected Cars 2018 ·Matthew Jagielski, (unknown), Chung‐Wei Lin, Cristina Nita-Rotaru, Shinichi Shiraishi	35
18	Network and system level security in connected vehicle applications 2018 ·(unknown), Matthew Jagielski, Bowen Zheng, Chung‐Wei Lin, Eunsuk Kang, Shinichi Shiraishi, Cristina Nita-Rotaru, Qi Zhu	10
19	Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning breakdown → 2018 ·UNICA IRIS Institutional Research Information System (University of Cagliari) ·Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, Bo Li	456

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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