Privacy in the Time of Language Models
Open accessAbstract
Pretrained large language models (LLMs) have consistently shown state-of-the-art performance across multiple natural language processing (NLP) tasks. These models are of much interest for a variety of industrial applications that use NLP as a core component. However, LLMs have also been shown to memorize portions of their training data, which can contain private information. Therefore, when building and deploying LLMs, it is of value to apply privacy-preserving techniques that protect sensitive data.
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Advanced generative AI models trained on massive datasets possess sophisticated memory mechanisms that may result in privacy leaks.
“Unlike previous concerns that primarily focused on sensitive data protection, advanced generative AI models trained on massive datasets posses sophisticated memory mechanisms that may result in privacy leaks (Carlini etal. 2021, 2022; Peris etal. 2023).”
From When LLMs meet cybersecurity: a systematic literature review (2025) · cites this paper as Peris et al. 2023
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- Authors
- Peris, C., Dupuy, C., Majmudar, J., Parikh, R., Smaili, S., Zemel, R., & Gupta, R.
- Affiliations
- Amazon (United States) (US)
- Columbia University (US)
- ORCID
- Venue
- Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2023-02-27
- Pages
- 1291-1292
- OpenAlex
- W4321521234
- Cited by
- 41
- References
- 5
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 20 · 2023: 2
- FWCI
- 7.28
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM International Conference on Web Search and Data Mining (WSDM) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- large language models, privacy preservation, sensitive data protection, data privacy
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