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Cape: Context-aware prompt perturbation mechanism with differential privacy. arXiv: 2505.05922."},{"key":"10.1016\/j.neunet.2026.108668_bib0076","series-title":"Workshop in ICML","article-title":"Differentially private synthetic data via foundation model APIs 2: Text","author":"Xie","year":"2024"},{"key":"10.1016\/j.neunet.2026.108668_bib0077","series-title":"Socially responsible language modelling research","article-title":"Training private and efficient language models with synthetic data from llms","author":"Yu","year":"2023"},{"key":"10.1016\/j.neunet.2026.108668_bib0078","series-title":"International conference on machine learning(ICML)","first-page":"57480","article-title":"Privacy-preserving instructions for aligning large language models","author":"Yu","year":"2024"},{"key":"10.1016\/j.neunet.2026.108668_bib0079","series-title":"International conference on learning representations(ICLR)","article-title":"Differentially private fine-tuning of language models","author":"Yu","year":"2022"},{"key":"10.1016\/j.neunet.2026.108668_bib0080","series-title":"International conference on learning representations(ICLR)","article-title":"Do not let privacy overbill utility: Gradient embedding perturbation for private learning","author":"Yu","year":"2020"},{"key":"10.1016\/j.neunet.2026.108668_bib0081","doi-asserted-by":"crossref","unstructured":"Yue, X., Du, M., Wang, T., Li, Y., Sun, H., & Chow, S. S. M. (2021). Differential privacy for text analytics via natural text sanitization. arXiv: 2106.01221.","DOI":"10.18653\/v1\/2021.findings-acl.337"},{"key":"10.1016\/j.neunet.2026.108668_bib0082","series-title":"Meeting of the association for computational linguistics(ACL)","first-page":"1321","article-title":"Synthetic text generation with differential privacy: A simple and practical recipe","author":"Yue","year":"2023"},{"key":"10.1016\/j.neunet.2026.108668_bib0083","article-title":"Defending against neural fake news","volume":"32","author":"Zellers","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.108668_bib0084","series-title":"Findings of the association for computational linguistics: ACL 2025","first-page":"20243","article-title":"Dyntext: Semantic-aware dynamic text sanitization for privacy-preserving llm inference","author":"Zhang","year":"2025"},{"key":"10.1016\/j.neunet.2026.108668_bib0085","series-title":"IEEE conference on computer vision and pattern recognition(cvpr)","first-page":"11854","article-title":"Private-knn: Practical differential privacy for computer vision","author":"Zhu","year":"2020"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026001309?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026001309?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T20:07:36Z","timestamp":1777579656000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026001309"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":85,"alternative-id":["S0893608026001309"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.108668","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Differentially private data augmentation via LLM generation with discriminative and distribution-aligned filtering","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.108668","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. 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