The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Information Leakage in Embedding Models
Abstract
Embeddings are functions that map raw input data to low-dimensional vector representations, while preserving important semantic information about the inputs. Pre-training embeddings on a large amount of unlabeled data and fine-tuning them for downstream tasks is now a de facto standard in achieving state of the art learning in many domains.
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“Training data extraction attacks have until now been limited to small LMs trained on academic datasets under artificial training setups (e.g., for more epochs than typical) [8, 66, 68, 73], or settings where the adversary has a priori knowledge of the secret they want to extract (e.g., a social security number) [8, 27].”
From Extracting Training Data from Large Language Models (2020) · cites this paper as [66]
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Cited by
A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly
Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100211 · Cited by 1,083
Extracting Training Data from Large Language Models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07805 · Cited by 727
A Survey of Privacy Attacks in Machine Learning
Rigaki, M., & Garcia, S. (2024) · ACM Computing Surveys · doi:10.1145/3624010 · Cited by 285
AI agents under threat: A survey of key security challenges and future pathways
Deng, Z., Guo, Y., Han, C., Ma, W., Xiong, J., Wen, S., & Xiang, Y. (2025) · ACM Computing Surveys · doi:10.1145/3716628 · Cited by 210
What Does it Mean for a Language Model to Preserve Privacy?
Brown, H., Lee, K., Mireshghallah, F., Shokri, R., & Tramèr, F. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3534642 · Cited by 165
Practical Attribute Reconstruction Attack Against Federated Learning
Chen, C., Lyu, L., Yu, H., & Chen, G. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3159236 · Cited by 30
Metadata
- Authors
- Song, C., & Raghunathan, A.
- Affiliations
- Cornell University & Google Brain, New York, NY, USA
- Facebook & Google Brain, Melon Park, CA, USA
- Venue
- Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security
- Publisher
- ACM
- Published
- 2020-10-30
- Pages
- 377-390
- Cited by
- 175
- References
- 47
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
