🔗 Who cites «Membership Inference Attacks From First Principles»

82 papers of our library cite it (of the 149,439 whose bibliographies we read) · 23 with a page here, the most cited first · ← back to «Membership Inference Attacks From First Principles»

1. Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' AlignmentarXiv:2308.05374Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li2023 · 223 citations
2. Scalable Extraction of Training Data from (Production) Language ModelsarXiv:2311.17035Milad Nasr, Nicholas Carlini, Jonathan Hayase, et al.2023 · 211 citations
3. Deduplicating Training Data Mitigates Privacy Risks in Language ModelsarXiv:2202.06539Nikhil Kandpal, Eric Wallace, Colin Raffel2022 · 172 citations
4. Membership Inference Attacks against Language Models via Neighbourhood ComparisonFindings of the Association for Computational LinguisticsJustus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, et al.2023 · 165 citations
5. Quantifying Privacy Risks of Masked Language Models Using Membership Inference AttacksConference on Empirical Methods in Natural Language Processing (EMNLP)Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, et al.2022 · 146 citations
6. Do Membership Inference Attacks Work on Large Language Models?arXiv:2402.07841Michael Duan, Anshuman Suri, Niloofar Mireshghallah, et al.2024 · 87 citations
7. Machine Unlearning of Pre-trained Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jin Yao, Eli Chien, Minxin Du, et al.2024 · 86 citations
8. Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement LearningConference on Empirical Methods in Natural Language Processing (EMNLP)Wenbin Hu, Haoran Li, Huihao Jing, et al.2025 · 7 citations
9. Detecting Training Data of Large Language Models via Expectation MaximizationConference of the European Chapter of the Association for Computational Linguistics (EACL)Gyuwan Kim, Yang Li, Evangelia Spiliopoulou, et al.2026 · 5 citations
10. Neural Breadcrumbs: Membership Inference Attacks on LLMs Through Hidden State and Attention Pattern AnalysisConference of the European Chapter of the Association for Computational Linguistics (EACL)Disha Makhija, Manoj Ghuhan Arivazhagan, Vinayshekhar Bannihatti Kumar, et al.2026 · 2 citations
11. Do LLMs Really Memorize Personally Identifiable Information? Revisiting PII Leakage with a Cue-Controlled Memorization FrameworkAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaoyu Luo, Yiyi Chen, Qiongxiu Li, et al.2026 · 1 citation
12. Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Zongyu Wu, Minhua Lin, Zhiwei Zhang, et al.2026 · 1 citation
13. The Model's Language Matters: A Comparative Privacy Analysis of LLMsFindings of the Association for Computational LinguisticsAbhishek Kumar Mishra, Antoine Boutet, Lucas Magnana2026 · 1 citation
14. Black-Box Membership Inference Attacks for Video Training Data in Multimodal Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jinrui Wang, Zhenfeng Gao, Wendan Wang, et al.2026
15. CheckMIABench: Firm Foundations For Membership Inference Attacks on Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jeffrey George Wang, Jason Wang, Marvin Li, et al.2026
16. Fast-MIA: Efficient and Scalable Membership Inference for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Hiromu Takahashi, Shotaro Ishihara2026
17. LLMSurgeon: Diagnosing Data Mixture of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yaxin Luo, Jiacheng Cui, Xiaohan Zhao, et al.2026
18. Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and BenchmarksFindings of the Association for Computational LinguisticsNobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu, et al.2026
19. PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data DetectionAnnual Meeting of the Association for Computational Linguistics (ACL)Jinhan Liu, Yibo Yang, Ruiying Lu, et al.2026
20. PII Jailbreaking in LLMs via Activation Steering Reveals Personal Information LeakageWorkshop on Trustworthy NLPKrishna Kanth Nakka, Xue Jiang, Dmitrii Usynin, et al.2026
21. Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data GenerationFindings of the Association for Computational LinguisticsQian Ma, Sarah Rajtmajer2026
22. SEAD: A Surrogate-free Label-only Membership Inference Attack against Pre-trained LLMs with Semantic-Aware DensityFindings of the Association for Computational LinguisticsBiao Yi, Jiahao Li, Yiming Li, et al.2026
23. SLIM: Stealthy Low-Coverage Black-Box Watermarking via Latent-Space Confusion ZonesFindings of the Association for Computational LinguisticsHengyu WU, Yang Cao2026

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