[1]吴振豪, 高健博, 李青山, 等. 数据安全治理中的安全技术研究[J]. 信息安全研究, 2021, 7(10): 907914[2]Alvim M, Chatzikokolakis K, Palamidessi C, et al. Local differential privacy on metric spaces: Optimizing the tradeoff with utility[C] Proc of IEEE Computer Security Foundations Symposium. Piscataway, NJ: IEEE, 2018: 262267[3]Zhao Y, Chen J. A survey on differential privacy for unstructured data content[J]. ACM Computing Surveys, 2022, 54(10s): 128[4]朱天清, 何木青, 邹德清. 基于差分隐私的大数据隐私保护[J]. 信息安全研究, 2015, 1(3): 224229[5]Dernoncourt F, Lee J Y, Uzuner O, et al. Deidentification of patient notes with recurrent neural networks[J]. Journal of the American Medical Informatics Association, 2017, 24(3): 596606[6]Li D, RastegarMojarad M, Elayavilli R K, et al. A frequencyfiltering strategy of obtaining PHIfree sentences from clinical data repository[C] Proc of the 6th ACM Conf on Bioinformatics, Computational Biology and Health Informatics. New York: ACM, 2015: 315324[7]Feyisetan O, Balle B, Drake T, et al. Privacyand utilitypreserving textual analysis via calibrated multivariate perturbations[C] Proc of the 13th Int Conf on Web Search and Data Mining. New York: ACM, 2020: 178186[8]Xu N, Feyisetan O, Aggarwal A, et al. Densityaware differentially private textual perturbations using truncated Gumbel noise[C] Proc of Int FLAIRS Conference. New York: ACM, 2021: 3434[9]Yue X, Du M, Wang T, et al. Differential privacy for text analytics via natural text sanitization[C] Proc of the Association for Computational Linguistics: ACLIJCNLP 2021. Stroudsburg, PA: ACL, 2021: 38533866[10]Chen S, Mo F, Wang Y, et al. A customized text sanitization mechanism with differential privacy[C] Proc of the Association for Computational Linguistics(ACL 2023). Stroudsburg, PA: ACL, 2023: 57475758[11]Quteineh H, Samothrakis S, Sutcliffe R. Textual data augmentation for efficient active learning on tiny datasets[C] Proc of the 2020 Conf on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2020: 74007410[12]Pennington J, Socher R, Manning C D. Glove: Global vectors for word representation[C] Proc of the 2014 Conf on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2014: 15321543[13]Devlin J, Chang M W, Lee K, et al. Bert: Pretraining of deep bidirectional transformers for language understanding[C] Proc of the 2019 Conf on the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA: ACL, 2019: 41714186[14]Wang A, Singh A, Michael J, et al. GLUE: A multitask benchmark and analysis platform for natural language understanding[C] Proc of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. New York: ACM, 2018: 353355[15]谭作文, 张连福. 机器学习隐私保护研究综述[J]. 软件学报, 2020, 31(7): 21272156[16]张梅舒, 徐雅斌. 多维数值型敏感属性数据的个性化隐私保护方法[J]. 计算机应用, 2020, 40(2): 491496
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