This webpage is an attempt to assemble a ranking of top-cited security papers from the 2020s. The ranking has been created based on citations of papers published at top security conferences. More details are available here.
Top-cited papers from 2024 ⌄
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Zichen Gui, Kenneth G. Paterson, Sikhar Patranabis, and Bogdan Warinschi: SWiSSSE: System-Wide Security for Searchable Symmetric Encryption. Proceedings on Privacy Enhancing Technologies (PoPETS), 2024
Zengrui Liu, Umar Iqbal, and Nitesh Saxena: Opted Out, Yet Tracked: Are Regulations Enough to Protect Your Privacy? Proceedings on Privacy Enhancing Technologies (PoPETS), 2024
Nerla Jean-Louis, Yunqi Li, Yan Ji, Harjasleen Malvai, Thomas Yurek, Sylvain Bellemare, and Andrew Miller: SGXonerate:Finding (and Partially Fixing) Privacy Flaws in TEE-based Smart Contract Platforms Without Breaking the TEE. Proceedings on Privacy Enhancing Technologies (PoPETS), 2024
Emily Wenger, Xiuyu Li, Ben Y. Zhao, and Vitaly Shmatikov: Data Isotopes for Data Provenance in DNNs. Proceedings on Privacy Enhancing Technologies (PoPETS), 2024
Seny Kamara, Abdelkarim Kati, Tarik Moataz, Jamie DeMaria, Andrew Park, and Amos Treiber: MAPLE: MArkov Process Leakage attacks on Encrypted Search. Proceedings on Privacy Enhancing Technologies (PoPETS), 2024
François Hublet, David A. Basin, and Srdan Krstic: User-Controlled Privacy: Taint, Track, and Control. Proceedings on Privacy Enhancing Technologies (PoPETS), 2024
Sayan Biswas and Catuscia Palamidessi: PRIVIC: A privacy-preserving method for incremental collection of location data. Proceedings on Privacy Enhancing Technologies (PoPETS), 2024
Sebastian Zimmeck, Eliza Kuller, Chunyue Ma, Bella Tassone, and Joe Champeau: Generalizable Active Privacy Choice: Designing a Graphical User Interface for Global Privacy Control. Proceedings on Privacy Enhancing Technologies (PoPETS), 2024
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, and Eric Wallace: Extracting Training Data from Diffusion Models. USENIX Security Symposium, 2023
Maurice Weber, Xiaojun Xu, Bojan Karlas, Ce Zhang, and Bo Li: RAB: Provable Robustness Against Backdoor Attacks. IEEE Symposium on Security and Privacy (S&P), 2023
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, and Nicolas Papernot: When the Curious Abandon Honesty: Federated Learning Is Not Private. IEEE European Symposium on Security and Privacy (EuroS&P), 2023
Hammond Pearce, Benjamin Tan, Baleegh Ahmad, Ramesh Karri, and Brendan Dolan-Gavitt: Examining Zero-Shot Vulnerability Repair with Large Language Models. IEEE Symposium on Security and Privacy (S&P), 2023
Yi Zeng, Minzhou Pan, Hoang Anh Just, Lingjuan Lyu, Meikang Qiu, and Ruoxi Jia: Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information. ACM Conference on Computer and Communications Security (CCS), 2023
Mahimna Kelkar, Soubhik Deb, Sishan Long, Ari Juels, and Sreeram Kannan: Themis: Fast, Strong Order-Fairness in Byzantine Consensus. ACM Conference on Computer and Communications Security (CCS), 2023
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, and Santiago Zanella Béguelin: Analyzing Leakage of Personally Identifiable Information in Language Models. IEEE Symposium on Security and Privacy (S&P), 2023
Liyi Zhou, Xihan Xiong, Jens Ernstberger, Stefanos Chaliasos, Zhipeng Wang, Ye Wang, Kaihua Qin, Roger Wattenhofer, Dawn Song, and Arthur Gervais: SoK: Decentralized Finance (DeFi) Attacks. IEEE Symposium on Security and Privacy (S&P), 2023
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramèr: Membership Inference Attacks From First Principles. IEEE Symposium on Security and Privacy (S&P), 2022
Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, and Konrad Rieck: Dos and Don'ts of Machine Learning in Computer Security. USENIX Security Symposium, 2022
Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma, and Yang Zhang: Dynamic Backdoor Attacks Against Machine Learning Models. IEEE European Symposium on Security and Privacy (EuroS&P), 2022
Kaihua Qin, Liyi Zhou, and Arthur Gervais: Quantifying Blockchain Extractable Value: How dark is the forest? IEEE Symposium on Security and Privacy (S&P), 2022
Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri: Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions. IEEE Symposium on Security and Privacy (S&P), 2022
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, and Daniel Ramage: Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. IEEE Symposium on Security and Privacy (S&P), 2022
Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, Vincent Bindschaedler, and Reza Shokri: Enhanced Membership Inference Attacks against Machine Learning Models. ACM Conference on Computer and Communications Security (CCS), 2022
Jinyuan Jia, Yupei Liu, and Neil Zhenqiang Gong: BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning. IEEE Symposium on Security and Privacy (S&P), 2022
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel: Extracting Training Data from Large Language Models. USENIX Security Symposium, 2021
Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot: Machine Unlearning. IEEE Symposium on Security and Privacy (S&P), 2021
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong: FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping. Network and Distributed System Security Symposium (NDSS), 2021
Lorenzo Grassi, Dmitry Khovratovich, Christian Rechberger, Arnab Roy, and Markus Schofnegger: Poseidon: A New Hash Function for Zero-Knowledge Proof Systems. USENIX Security Symposium, 2021
Virat Shejwalkar and Amir Houmansadr: Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning. Network and Distributed System Security Symposium (NDSS), 2021
Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A. Gunter, and Bo Li: Detecting AI Trojans Using Meta Neural Analysis. IEEE Symposium on Security and Privacy (S&P), 2021
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong: Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. USENIX Security Symposium, 2020
Jianbo Chen, Michael I. Jordan, and Martin J. Wainwright: HopSkipJumpAttack: A Query-Efficient Decision-Based Attack. IEEE Symposium on Security and Privacy (S&P), 2020
Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy, and Ling Liu: Data Poisoning Attacks Against Federated Learning Systems. European Symposium on Research in Computer Security (ESORICS), 2020
Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca Ada Popa: Delphi: A Cryptographic Inference Service for Neural Networks. USENIX Security Symposium, 2020
Philip Daian, Steven Goldfeder, Tyler Kell, Yunqi Li, Xueyuan Zhao, Iddo Bentov, Lorenz Breidenbach, and Ari Juels: Flash Boys 2.0: Frontrunning in Decentralized Exchanges, Miner Extractable Value, and Consensus Instability. IEEE Symposium on Security and Privacy (S&P), 2020
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, and Nicolas Papernot: High Accuracy and High Fidelity Extraction of Neural Networks. USENIX Security Symposium, 2020
James Henry Bell, Kallista A. Bonawitz, Adrià Gascón, Tancrède Lepoint, and Mariana Raykova: Secure Single-Server Aggregation with (Poly)Logarithmic Overhead. ACM Conference on Computer and Communications Security (CCS), 2020
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz: GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models. ACM Conference on Computer and Communications Security (CCS), 2020
Jo Van Bulck, Daniel Moghimi, Michael Schwarz, Moritz Lipp, Marina Minkin, Daniel Genkin, Yuval Yarom, Berk Sunar, Daniel Gruss, and Frank Piessens: LVI: Hijacking Transient Execution through Microarchitectural Load Value Injection. IEEE Symposium on Security and Privacy (S&P), 2020