---
title: "Who cites Privacy Risk in Machine Learning: — 86 citing papers"
description: "86 papers of the Elamur Research library cite «Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting», found in their…"
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Privacy Risk in Machine Learning: Analyzing the…](https://md.research.elamur.ai/free/en/papers/yeom-2017-privacy-risk-machine-learning-analyzing/) / cited by

# Who cites «Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting»

86 papers of our library cite it (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · [← back to «Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting»](https://md.research.elamur.ai/free/en/papers/yeom-2017-privacy-risk-machine-learning-analyzing/)

1. [**Extracting Training Data from Large Language Models arXiv:2012.07805**Nicholas Carlini, Florian Tramer, Eric Wallace, et al.*2020 · 955 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2020-extracting-training-data-large-language/)
2. [**The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks arXiv:1802.08232**Nicholas Carlini, Chang Liu, Ulfar Erlingsson, et al.*2018 · 692 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2018-secret-sharer-evaluating-testing-unintended/)
3. **[Membership Inference Attacks From First Principles](https://md.research.elamur.ai/free/en/papers/carlini-2021-membership-inference-attacks-first-principles/) [Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy](https://md.research.elamur.ai/free/en/venues/proceedings-ieee-symposium-on-security-and-privacy-proceedings-of-the-ieee/)**Nicholas Carlini, Steve Chien, Milad Nasr, et al.*2021 · 584 citations*
4. **[Security and Privacy Challenges of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/das-2024-security-privacy-challenges-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu*2024 · 424 citations*
5. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
6. [**Scalable Extraction of Training Data from (Production) Language Models arXiv:2311.17035**Milad Nasr, Nicholas Carlini, Jonathan Hayase, et al.*2023 · 211 citations*](https://md.research.elamur.ai/free/en/papers/nasr-2023-scalable-extraction-training-data-production/)
7. [**Deduplicating Training Data Mitigates Privacy Risks in Language Models arXiv:2202.06539**Nikhil Kandpal, Eric Wallace, Colin Raffel*2022 · 172 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-deduplicating-training-data-mitigates-privacy/)
8. **[Membership Inference Attacks against Language Models via Neighbourhood Comparison](https://md.research.elamur.ai/free/en/papers/mattern-2023-membership-inference-attacks-against-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, et al.*2023 · 165 citations*
9. **[Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks](https://md.research.elamur.ai/free/en/papers/mireshghallah-2022-quantifying-privacy-risks-masked-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, et al.*2022 · 146 citations*
10. [**Do Membership Inference Attacks Work on Large Language Models? arXiv:2402.07841**Michael Duan, Anshuman Suri, Niloofar Mireshghallah, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/duan-2024-membership-inference-attacks-work-large/)
11. **[Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?](https://md.research.elamur.ai/free/en/papers/hisamoto-2020-membership-inference-attacks-sequence-sequence/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Sorami Hisamoto, Matt Post, Kevin Duh*2020 · 82 citations*
12. **[Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy Maximization](https://md.research.elamur.ai/free/en/papers/zhang-2025-get-confused-cautiously-textual-sequence/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Zhaohan Zhang, Ziquan Liu, Ioannis Patras*2025 · 7 citations*
13. **[Detecting Training Data of Large Language Models via Expectation Maximization](https://md.research.elamur.ai/free/en/papers/kim-2026-detecting-training-data-large-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Gyuwan Kim, Yang Li, Evangelia Spiliopoulou, et al.*2026 · 5 citations*
14. **[Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning](https://md.research.elamur.ai/free/en/papers/zhai-2026-maximizing-local-entropy-where-it/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Naixin Zhai, Pengyang Shao, Binbin Zheng, et al.*2026 · 2 citations*
15. **[Neural Breadcrumbs: Membership Inference Attacks on LLMs Through Hidden State and Attention Pattern Analysis](https://md.research.elamur.ai/free/en/papers/makhija-2026-neural-breadcrumbs-membership-inference-attacks/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Disha Makhija, Manoj Ghuhan Arivazhagan, Vinayshekhar Bannihatti Kumar, et al.*2026 · 2 citations*
16. **[Hey, That's My Data! Token-Only Dataset Inference in Large Language Models](https://md.research.elamur.ai/free/en/papers/xiong-2026-hey-that-s-my-data/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chen Xiong, Zihao Wang, Rui Zhu, et al.*2026 · 1 citation*
17. **[The Model's Language Matters: A Comparative Privacy Analysis of LLMs](https://md.research.elamur.ai/free/en/papers/mishra-2026-model-s-language-matters-comparative/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Abhishek Kumar Mishra, Antoine Boutet, Lucas Magnana*2026 · 1 citation*
18. **[Black-Box Membership Inference Attacks for Video Training Data in Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-black-box-membership-inference-attacks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinrui Wang, Zhenfeng Gao, Wendan Wang, et al.*2026*
19. **[CheckMIABench: Firm Foundations For Membership Inference Attacks on Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-checkmiabench-firm-foundations-membership-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jeffrey George Wang, Jason Wang, Marvin Li, et al.*2026*
20. **[Detecting Non-Membership in LLM Training Data via Rank Correlations](https://md.research.elamur.ai/free/en/papers/shetty-2026-detecting-non-membership-llm-training/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Pranav Shetty, Mirazul Haque, Zhiqiang Ma, et al.*2026*
21. **[Fast-MIA: Efficient and Scalable Membership Inference for LLMs](https://md.research.elamur.ai/free/en/papers/takahashi-2026-fast-mia-efficient-scalable-membership/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hiromu Takahashi, Shotaro Ishihara*2026*
22. **[LLMSurgeon: Diagnosing Data Mixture of Large Language Models](https://md.research.elamur.ai/free/en/papers/luo-2026-llmsurgeon-diagnosing-data-mixture-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaxin Luo, Jiacheng Cui, Xiaohan Zhao, et al.*2026*
23. **[PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection](https://md.research.elamur.ai/free/en/papers/liu-2026-pdr-plug-play-positional-decay/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinhan Liu, Yibo Yang, Ruiying Lu, et al.*2026*
24. **[SEAD: A Surrogate-free Label-only Membership Inference Attack against Pre-trained LLMs with Semantic-Aware Density](https://md.research.elamur.ai/free/en/papers/yi-2026-sead-surrogate-free-label-only/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Biao Yi, Jiahao Li, Yiming Li, et al.*2026*
25. **[SLIM: Stealthy Low-Coverage Black-Box Watermarking via Latent-Space Confusion Zones](https://md.research.elamur.ai/free/en/papers/wu-2026-slim-stealthy-low-coverage-black/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hengyu WU, Yang Cao*2026*

61 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/yeom-2017-privacy-risk-machine-learning-analyzing/) — Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting