{"total":30,"rows":[{"id":"2207.00220","title":"Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset","created":"2022-07-01","cited_by_count":113,"area":null},{"id":"2210.14348","title":"Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe","created":"2022-10-25","cited_by_count":108,"area":null},{"id":"2202.00443","title":"The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization","created":"2022-01-25","cited_by_count":95,"area":null},{"id":"2210.13918","title":"Differentially Private Language Models for Secure Data Sharing","created":"2022-10-25","cited_by_count":68,"area":null},{"id":"2311.04044","title":"PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models","created":"2023-11-07","cited_by_count":37,"area":null},{"id":"2208.10400","title":"DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting","created":"2022-08-22","cited_by_count":28,"area":null},{"id":"2302.07636","title":"DP-BART for Privatized Text Rewriting under Local Differential Privacy","created":"2023-02-15","cited_by_count":28,"area":null},{"id":"2109.03175","title":"When differential privacy meets NLP: The devil is in the detail","created":"2021-09-07","cited_by_count":25,"area":null},{"id":"2301.09112","title":"Differentially Private Natural Language Models: Recent Advances and Future Directions","created":"2023-01-22","cited_by_count":20,"area":null},{"id":"2404.03324","title":"A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking The Privacy-Utility Trade-off","created":"2024-04-04","cited_by_count":13,"area":null},{"id":"2406.14318","title":"The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts","created":"2024-06-20","cited_by_count":13,"area":null},{"id":"2208.08140","title":"Differential Privacy in Natural Language Processing: The Story So Far","created":"2022-08-17","cited_by_count":12,"area":null},{"id":"2202.12138","title":"How reparametrization trick broke differentially-private text representation learning","created":"2022-02-24","cited_by_count":10,"area":null},{"id":"2205.06135","title":"Fair NLP Models with Differentially Private Text Encoders","created":"2022-05-12","cited_by_count":9,"area":null},{"id":"2405.19831","title":"Just Rewrite It Again: A Post-Processing Method for Enhanced Semantic Similarity and Privacy Preservation of Differentially Private Rewritten Text","created":"2024-05-30","cited_by_count":9,"area":null},{"id":"2310.14312","title":"Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysis","created":"2023-10-22","cited_by_count":5,"area":null},{"id":"2309.10551","title":"A Neighbourhood-Aware Differential Privacy Mechanism for Static Word Embeddings","created":"2023-09-19","cited_by_count":4,"area":null},{"id":"2410.12443","title":"Reconstruction of Differentially Private Text Sanitization via Large Language Models","created":"2024-10-16","cited_by_count":4,"area":null},{"id":"2503.20846","title":"Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead","created":"2025-03-26","cited_by_count":4,"area":null},{"id":"2303.03053","title":"Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting","created":"2023-03-06","cited_by_count":3,"area":null},{"id":"2410.08027","title":"Private Language Models via Truncated Laplacian Mechanism","created":"2024-10-10","cited_by_count":3,"area":null},{"id":"2211.10708","title":"A Survey on Differential Privacy with Machine Learning and Future Outlook","created":"2022-11-19","cited_by_count":2,"area":null},{"id":"2503.14991","title":"Inspecting the Representation Manifold of Differentially-Private Text","created":"2025-03-19","cited_by_count":2,"area":null},{"id":"2509.25729","title":"Controlled Generation for Private Synthetic Text","created":"2025-09-30","cited_by_count":2,"area":null},{"id":"2503.09338","title":"Investigating User Perspectives on Differentially Private Text Privatization","created":"2025-03-12","cited_by_count":1,"area":null},{"id":"2503.13872","title":"Empirical Calibration and Metric Differential Privacy in Language Models","created":"2025-03-18","cited_by_count":1,"area":null},{"id":"2407.00764","title":"Characterizing Stereotypical Bias from Privacy-preserving Pre-Training","created":"2024-06-30","cited_by_count":0,"area":null},{"id":"2506.11687","title":"Differential Privacy in Machine Learning: A Survey from Symbolic AI to LLMs","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2603.22968","title":"Beyond Theoretical Bounds: Empirical Privacy Loss Calibration for Text Rewriting Under Local Differential Privacy","created":"2026-03-24","cited_by_count":0,"area":null},{"id":"2608.29624","title":"PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization","created":"2026-08-30","cited_by_count":0,"area":null}],"next_cursor":null}