Who cites «Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting»
- Extracting Training Data from Large Language ModelsarXiv:2012.07805Nicholas Carlini, Florian Tramer, Eric Wallace, et al.2020 · 955 citations
- The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural NetworksarXiv:1802.08232Nicholas Carlini, Chang Liu, Ulfar Erlingsson, et al.2018 · 692 citations
- Security and Privacy Challenges of Large Language Models: A SurveyACM Computing SurveysBadhan Chandra Das, M. Hadi Amini, Yanzhao Wu2024 · 424 citations
- 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
- Scalable Extraction of Training Data from (Production) Language ModelsarXiv:2311.17035Milad Nasr, Nicholas Carlini, Jonathan Hayase, et al.2023 · 211 citations
- Deduplicating Training Data Mitigates Privacy Risks in Language ModelsarXiv:2202.06539Nikhil Kandpal, Eric Wallace, Colin Raffel2022 · 172 citations
- 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
- 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
- Do Membership Inference Attacks Work on Large Language Models?arXiv:2402.07841Michael Duan, Anshuman Suri, Niloofar Mireshghallah, et al.2024 · 87 citations
- Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?Transactions of the Association for Computational Linguistics (TACL)Sorami Hisamoto, Matt Post, Kevin Duh2020 · 82 citations
- Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy MaximizationInternational Conference on Computational Linguistics (COLING)Zhaohan Zhang, Ziquan Liu, Ioannis Patras2025 · 7 citations
- 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
- Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM UnlearningAnnual Meeting of the Association for Computational Linguistics (ACL)Naixin Zhai, Pengyang Shao, Binbin Zheng, et al.2026 · 2 citations
- 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
- Hey, That's My Data! Token-Only Dataset Inference in Large Language ModelsFindings of the Association for Computational LinguisticsChen Xiong, Zihao Wang, Rui Zhu, et al.2026 · 1 citation
- 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
- 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
- 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
- Detecting Non-Membership in LLM Training Data via Rank CorrelationsConference of the European Chapter of the Association for Computational Linguistics (EACL)Pranav Shetty, Mirazul Haque, Zhiqiang Ma, et al.2026
- Fast-MIA: Efficient and Scalable Membership Inference for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Hiromu Takahashi, Shotaro Ishihara2026
- 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
- 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
This page in Markdown → https://md.research.elamur.ai/free/en/papers/yeom-2017-privacy-risk-machine-learning-analyzing/cited-by/