--- title: "Who cites Membership Inference Attacks — 73 citing papers" description: "73 papers of the Elamur Research library cite «Membership Inference Attacks against Language Models via Neighbourhood Comparison»…" canonical: "https://research.elamur.ai/free/en/papers/mattern-2023-membership-inference-attacks-against-language/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/mattern-2023-membership-inference-attacks-against-language/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Membership Inference Attacks against Language…](https://md.research.elamur.ai/free/en/papers/mattern-2023-membership-inference-attacks-against-language/) / cited by # Who cites «Membership Inference Attacks against Language Models via Neighbourhood Comparison» 73 papers of our library cite it (of the 141,840 whose bibliographies we read) · 4 with a page here, the most cited first · [← back to «Membership Inference Attacks against Language Models via Neighbourhood Comparison»](https://md.research.elamur.ai/free/en/papers/mattern-2023-membership-inference-attacks-against-language/) 1. [**Do Membership Inference Attacks Work on Large Language Models? arXiv:2402.07841**Michael Duan, Anshuman Suri, Niloofar Mireshghallah, et al.*2024 · 80 citations*](https://md.research.elamur.ai/free/en/papers/duan-2024-membership-inference-attacks-work-large/) **[EL-MIA: Quantifying Membership Inference Risks of Sensitive Entities in LLMs](https://md.research.elamur.ai/free/en/papers/satvaty-2026-el-mia-quantifying-membership-inference/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ali Satvaty, Suzan Verberne, Fatih Turkmen*2026* 69 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/mattern-2023-membership-inference-attacks-against-language/) — Membership Inference Attacks against Language Models via Neighbourhood Comparison