Anonymisation Models for Text Data: State of the art, Challenges and Future Directions

Calibrating Noise to Sensitivity in Private Data Analysis

Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272 · see citing works

Neural Architectures for Named Entity Recognition

Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1030 · Cited by 4,401 · see citing works

Protecting respondents identities in microdata release

Samarati, P. (2001) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.971193 · Cited by 2,339 · see citing works

General Data Protection Regulation

van Alsenoy, B. (2018) · Data Protection Law in the EU: Roles, Responsibilities and Liability · doi:10.1002/9781119436775.ch18 · Cited by 2,161 · see citing works

Named Entity Recognition with Bidirectional LSTM-CNNs

Chiu, J., & Nichols, E. (2016) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00104 · Cited by 1,925 · see citing works

Privacy-preserving data publishing: A survey of recent developments

Fung, B. C. M., Wang, K., Chen, R., & Yu, P. S. (2010) · ACM Computing Surveys · doi:10.1145/1749603.1749605 · Cited by 1,704 · see citing works

Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression

Samarati, P., & Sweeney, L. (2018) · doi:10.1184/r1/6625469 · Cited by 1,341 · see citing works

Towards Sharp Inapproximability For Any 2-CSP

Austrin, P. (2007) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2007.41 · Cited by 1,339 · see citing works

Privacy and Freedom

Westin, A. F. (1970) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,184 · see citing works

Learning Differentially Private Recurrent Language Models

McMahan, H. B., Ramage, D., Talwar, K., & Zhang, L. (2017) · arXiv · doi:10.48550/arxiv.1710.06963 · Cited by 849 · see citing works

De-identification of patient notes with recurrent neural networks

Dernoncourt, F., Lee, J. Y., Uzuner, Ö., & Peter Szolovits, B. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw156 · Cited by 389 · see citing works

Automatic de-identification of textual documents in the electronic health record: a review of recent research

Meystre, S. M., Friedlin, J., South, B. R., Shen, S., & Samore, M. H. (2010) · BMC Medical Research Methodology · doi:10.1186/1471-2288-10-70 · Cited by 329 · see citing works

Demographic dialectal variation in social media: a case study of African-American English

Blodgett, S. L., Green, L., & O'Connor, B. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1120 · Cited by 291 · cited 1× · see citing works

General Data Protection Regulation

Alsenoy, B. V. (2019) · Intersentia eBooks · doi:10.1017/9781780688459.021 · Cited by 283 · see citing works

Adversarial Removal of Demographic Attributes from Text Data

Elazar, Y., & Goldberg, Y. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1002 · Cited by 261 · see citing works

Revisiting the uniqueness of simple demographics in the US population

Golle, P. (2006) · doi:10.1145/1179601.1179615 · Cited by 258 · see citing works

Is Deidentification Sufficient to Protect Health Privacy in Research?

Rothstein, M. A. (2010) · The American Journal of Bioethics · doi:10.1080/15265161.2010.494215 · Cited by 255 · see citing works

Replacing Personally-Identifying Information in Medical Records, the Scrub System

Sweeney, L. (2018) · PubMed · doi:10.1184/r1/6625565 · Cited by 251 · see citing works

Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus

Stubbs, A., & Uzuner, Ö. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.07.020 · Cited by 216 · see citing works

Mechanism Design via Differential Privacy

McSherry, F., & Talwar, K. (2007) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2007.4389483 · Cited by 211 · see citing works

Recognizing textual entailment models and applications

Dagan, I. (2013) · Cited by 200 · see citing works

De-identification of clinical notes via recurrent neural network and conditional random field

Liu, Z., Tang, B., Wang, X., & Chen, Q. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.05.023 · Cited by 153 · see citing works

Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review

Chevrier, R., Foufi, V., Gaudet-Blavignac, C., Robert, A., & Lovis, C. (2019) · Journal of Medical Internet Research · doi:10.2196/13484 · Cited by 150 · see citing works

The MITRE Identification Scrubber Toolkit: Design, training, and assessment

Aberdeen, J. S., Bayer, S., Yeniterzi, R., Wellner, B., Clark, C., Hanauer, D. A., Malin, B., & Hirschman, L. (2010) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2010.09.007 · Cited by 146 · see citing works

The limits of differential privacy (and its misuse in data release and machine learning)

Domingo‐Ferrer, J., Sánchez, D., & Blanco-Justicia, A. (2021) · Communications of the ACM · doi:10.1145/3433638 · Cited by 109 · see citing works

De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1

Stubbs, A., Filannino, M., & Uzuner, Ö. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.06.011 · Cited by 105 · see citing works

Generalised Differential Privacy for Text Document Processing

Fernandes, N., Dras, M., & McIver, A. K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-17138-4_6 · Cited by 99 · see citing works

The Anonymisation Decision-Making Framework

Elliot, M. J., Mackey, E., O’Hara, K., & Tudor, C. (2016) · ePrints Soton (University of Southampton) · Cited by 95 · see citing works

Efficient techniques for document sanitization

Chakaravarthy, V. T., Gupta, H., Roy, P., & Mohania, M. (2008) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1458082.1458194 · Cited by 91 · see citing works

Obfuscating Gender in Social Media Writing

Reddy, S., & Knight, K. K. (2016) · Workshop on Natural Language Processing and Computational Social Science (NLP+CSS) · doi:10.18653/v1/w16-5603 · Cited by 86 · see citing works

C‐sanitized: A privacy model for document redaction and sanitization

Sánchez, D., & Batet, M. (2016) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23363 · Cited by 80 · see citing works

THE HEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACT

Becker, S. (2004) · Journal of Christian Nursing · doi:10.1097/01.cnj.0000262449.14139.a9 · Cited by 80 · see citing works

A study of deep learning methods for de-identification of clinical notes in cross-institute settings

Yang, X., Lyu, T., Li, Q., Lee, C.-Y., Bian, J., Hogan, W. R., & Wu, Y. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0935-4 · Cited by 73 · see citing works

Developing a standard for de-identifying electronic patient records written in Swedish: Precision, recall and F-measure in a manual and computerized annotation trial

Velupillai, S. U., Dalianis, H., Hassel, M., & Nilsson, G. H. (2009) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2009.04.005 · Cited by 70 · see citing works

Hiding in plain sight: use of realistic surrogates to reduce exposure of protected health information in clinical text

Carrell, D., Malin, B., Aberdeen, J. S., Bayer, S., Clark, C., Wellner, B., & Hirschman, L. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001034 · Cited by 70 · see citing works

Database Anonymization: Privacy Models, Data Utility, and Microaggregation-based Inter-model Connections

Domingo‐Ferrer, J., Sánchez, D., & Soria-Comas, J. (2016) · Synthesis lectures on information security, privacy, and trust · doi:10.2200/s00690ed1v01y201512spt015 · Cited by 69 · see citing works

Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text

Feyisetan, O., Diethe, T., & Drake, T. (2019) · Proceedings · doi:10.1109/icdm.2019.00031 · Cited by 68 · see citing works

FELIX: Flexible Text Editing Through Tagging and Insertion

Mallinson, J., Severyn, A., Malmi, E., & Garrido, G. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.111 · Cited by 66 · see citing works

Text de-identification for privacy protection: A study of its impact on clinical text information content

Meystre, S. M., Ferrández, Ó., Friedlin, J., South, B. R., Shen, S., & Samore, M. H. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.01.011 · Cited by 65 · see citing works

Detecting privacy leaks using corpus-based association rules

Chow, R., Golle, P., & Staddon, J. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1401997 · Cited by 61 · see citing works

The Health Insurance Portability & Accountability Act and the Practice of Dentistry in the United States: Privacy and Confidentiality

Chasteen, J. E., Murphy, G., Forrey, A. W., & Heid, D. W. (2003) · The Journal of Contemporary Dental Practice · doi:10.5005/jcdp-4-1-59 · Cited by 57 · see citing works

Viewing the GDPR through a de-identification lens: a tool for compliance, clarification, and consistency

Hintze, M. (2017) · International Data Privacy Law · doi:10.1093/idpl/ipx020 · Cited by 56 · see citing works

A Machine Learning Based System for Semi-Automatically Redacting Documents

Cumby, C., & Ghani, R. (2011) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v25i2.18851 · Cited by 55 · see citing works

Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results.

Marimon, M., González-Agirre, A., Intxaurrondo, A., Rodriguez, H., Martin, J. L., Villegas, M., & Krallinger, M. (2019) · IberLEF@SEPLN · Cited by 54 · see citing works

TextHide: Tackling Data Privacy in Language Understanding Tasks

Huang, Y., Song, Z., Chen, D., Li, K., & Arora, S. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.123 · Cited by 53 · see citing works

Privacy-Aware Text Rewriting

Xu, Q., Qu, L., Xu, C., & Cui, R. (2019) · International Conference on Natural Language Generation · doi:10.18653/v1/w19-8633 · Cited by 48 · see citing works

t-Plausibility: Generalizing Words to Desensitize Text

Anandan, B., Clifton, C., Jiang, W., Murugesan, M., Pastrana-Camacho, P. J., & Si, L. (2012) · Transactions on data privacy · Cited by 47 · see citing works

Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference

Mosallanezhad, A., Beigi, G., & Liu, H. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1240 · Cited by 46 · see citing works

Customization scenarios for de-identification of clinical notes

Hartman, T., Howell, M. D., Dean, J., Hoory, S., Slyper, R., Laish, I., Gilon, O., Vainstein, D., Corrado, G. S., Chou, K., et al. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1026-2 · Cited by 43 · see citing works

The Rules of Redaction: Identify, Protect, Review (and Repeat)

Bier, E. A., Chow, R., Golle, P., King, T. H., & Staddon, J. (2009) · IEEE Security & Privacy · doi:10.1109/msp.2009.183 · Cited by 42 · see citing works

Toward sensitive document release with privacy guarantees

Sánchez, D., & Batet, M. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.12.013 · Cited by 33 · see citing works

Utility-preserving privacy protection of nominal data sets via semantic rank swapping

Rodríguez-García, M., Batet, M., & Sánchez, D. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.02.008 · Cited by 31 · see citing works

Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records

Friedrich, M., Köhn, A., Wiedemann, G., & Biemann, C. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1584 · Cited by 30 · see citing works

An Introduction to NLP-based Textual Anonymisation

Medlock, B. (2006) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2t7a4mqb9ktq · Cited by 30 · see citing works

Significance of Term Relationships on Anonymization

Anandan, B., & Clifton, C. (2011) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2011.240 · Cited by 25 · see citing works

Can Physicians Recognize Their Own Patients in De-identified Notes?

Stéphane, M., Shuying, S., Hofmann, D., & Adi, G. (2014) · Studies in health technology and informatics · doi:10.3233/978-1-61499-432-9-778 · Cited by 24 · see citing works

A semantic framework for noise addition with nominal data

Rodríguez-García, M., Batet, M., & Sánchez, D. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.01.032 · Cited by 23 · see citing works

A survey of automatic de-identification of longitudinal clinical narrativesPreprint

Yogarajan, V., Mayo, M., & Pfahringer, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.06765 · Cited by 13 · see citing works

Semantic Disclosure Control: semantics meets data privacy

Batet, M., & Sánchez, D. (2018) · Online Information Review · doi:10.1108/oir-03-2017-0090 · Cited by 12 · see citing works

Generation of Surrogates for De-Identification of Electronic Health Records

Chen, A., Jonnagaddala, J., Nekkantti, C., & Liaw, S. (2019) · Studies in health technology and informatics · doi:10.3233/shti190185 · Cited by 12 · see citing works

Pseudonymisation of Personal Names and other PHIs in an Annotated Clinical Swedish Corpus

Alfalahi, A., Brissman, S., & Dalianis, H. (2012) · Cited by 12 · see citing works

Learner Corpus Anonymization in the Age of GDPR: Insights from the Creation of a Learner Corpus of Swedish

Megyesi, B. B., Granstedt, L., Johansson, S., Prentice, J., Rosén, D., Schenström, C.-J., Sundberg, G., Wirén, M., & Volodina, E. (2018) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 12 · see citing works

De-Identification of Emails: Pseudonymizing Privacy-Sensitive Data in a German Email Corpus

Institut für Germanistik, A.-A.-U. K., Eder, E., Krieg-Holz, U., Institut für Germanistik, A.-A.-U. K., & Hahn, U. (2019) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-056-4_030 · Cited by 9 · see citing works

CodE Alltag 2.0 — A Pseudonymized German-Language Email Corpus

Eder, E., Krieg-Holz, U., & Hahn, U. (2020) · Language Resources and Evaluation · Cited by 6 · see citing works

Towards Privacy by Design in Learner Corpora Research: A Case of On-the-fly Pseudonymization of Swedish Learner Essays

Volodina, E., Mohammed, Y. A., Derbring, S., Matsson, A., & Megyesi, B. B. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.32 · Cited by 6 · see citing works

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 · see citing works

“It's a Fair Game”, or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents

Zhang, Z., Jia, M., Lee, H.-P. (., Yao, B., Das, S., Lerner, A., Wang, D., & Li, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642385 · Cited by 95 · see citing works

Knowledge Unlearning for Mitigating Privacy Risks in Language Models

Jang, J., Yoon, D., Yang, S., Cha, S., Lee, M., Logeswaran, L., & Seo, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.805 · Cited by 135 · see citing works

The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization

Pilán, I., Lison, P., Øvrelid, L., Papadopoulou, A. I., Sánchez, D., & Batet, M. (2022) · Computational Linguistics · doi:10.1162/coli_a_00458 · Cited by 85 · see citing works

Beyond rating scales: With targeted evaluation, large language models are poised for psychological assessment

Kjell, O., Kjell, K., & Schwartz, H. A. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115667 · Cited by 96 · see citing works

Learning to Unlearn: Instance-Wise Unlearning for Pre-trained Classifiers

Cha, S., Cho, S., Hwang, D., Lee, H., Moon, T., & Lee, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.28996 · Cited by 47 · see citing works

Man vs the machine in the struggle for effective text anonymisation in the age of large language models

Patsakis, C., & Lykousas, N. (2023) · Scientific Reports · doi:10.1038/s41598-023-42977-3 · Cited by 50 · see citing works

Privacy- and Utility-Preserving NLP with Anonymized data: A case study of Pseudonymization

Yermilov, O., Raheja, V., & Chernodub, A. (2023) · Workshop on Trustworthy Natural Language Processing (TrustNLP) · doi:10.18653/v1/2023.trustnlp-1.20 · Cited by 34 · see citing works

Digital forgetting in large language models: a survey of unlearning methods

Blanco-Justicia, A., Jebreel, N., Manzanares-Salor, B., Sánchez, D., Domingo-Ferrer, J., Collell, G., & Eeik Tan, K. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11078-6 · Cited by 31 · see citing works

An efficient approach for textual data classification using deep learning

Alqahtani, A., Khan, H. U., Alsubai, S., Sha, M., Almadhor, A. S., Iqbal, T., & Abbas, S. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.992296 · Cited by 23 · see citing works

Rescriber: Smaller-LLM-Powered User-Led Data Minimization for LLM-Based Chatbots

Zhou, J., Xu, E., Wu, Y., & Li, T. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713701 · Cited by 17 · see citing works

CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applications

Chen, C., Zhou, D., Ye, Y., Li, T. J.-J., & Yao, Y. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712156 · Cited by 18 · see citing works

A comprehensive review of current trends, challenges, and opportunities in text data privacy

Shahriar, S., Dara, R. A., & Akalu, R. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104358 · Cited by 17 · see citing works

Recovering from Privacy-Preserving Masking with Large Language Models

Vats, A., Liu, Z., Peng, S., Paul, D., Ma, Y., Pang, Y., Ahmed, Z., & Kalinli, O. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448234 · Cited by 16 · see citing works

Beyond Rating Scales: With Targeted Evaluation, Language Models are Poised for Psychological AssessmentPreprint

Kjell, O., Kjell, K., & Schwartz, H. A. (2023) · doi:10.31234/osf.io/yfd8g · Cited by 15 · see citing works

Understanding Users' Security and Privacy Concerns and Attitudes Towards Conversational AI Platforms

Ali, M. T., Arunasalam, A., & Farrukh, H. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00241 · Cited by 22 · see citing works

Evaluation of an automated Presidio anonymisation model for unstructured radiation oncology electronic medical records in an Australian setting

Kotevski, D. P., Smee, R. I., Field, M., Nemes, Y. N., Broadley, K., & Vajdic, C. M. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104880 · Cited by 14 · see citing works

On Text-based Personality Computing: Challenges and Future Directions

Fang, Q., Giachanou, A., Bagheri, A., Boeschoten, L., van Kesteren, E., Kamalabad, M. S., & Oberski, D. L. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.691 · Cited by 14 · see citing works

Evaluating the disclosure risk of anonymized documents via a machine learning-based re-identification attack

Manzanares-Salor, B., Sánchez, D., & Lison, P. (2024) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-024-01066-3 · Cited by 15 · see citing works

Preserving Privacy Through Dememorization: An Unlearning Technique For Mitigating Memorization Risks In Language Models

Kassem, A. M., Mahmoud, O., & Saad, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.265 · Cited by 12 · see citing works

How reparametrization trick broke differentially-private text representation learning

Habernal, I. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-short.87 · Cited by 12 · see citing works

Mirroring Privacy Risks with Digital Twins: When Pieces of Personal Data Suddenly Fit Together

Bäumer, F. S., Schultenkämper, S., Geierhos, M., & Lee, Y. S. (2024) · SN Computer Science · doi:10.1007/s42979-024-03413-z · Cited by 13 · see citing works

Enhancing text anonymization via re-identification risk-based explainability

Manzanares-Salor, B., & Sánchez, D. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112945 · Cited by 11 · see citing works

Evaluating Synthetic Data Generation from User Generated Text

Chim, J., Ive, J., & Liakata, M. (2024) · Computational Linguistics · doi:10.1162/coli_a_00540 · Cited by 10 · see citing works

Privacy-Preserving Models for Legal Natural Language Processing

Yin, Y., & Habernal, I. (2022) · Natural Legal Language Processing Workshop (NLLP) · doi:10.18653/v1/2022.nllp-1.14 · Cited by 11 · see citing works

Anonymization, Hashing and Data Encryption Techniques: A Comparative Case Study

Vardalachakis, M., Kondylakis, H., Tampouratzis, M. G., Papadakis, N. K., & Mastorakis, N. E. (2023) · International Conference on Applied Mathematics & Computer Science (ICAMCS) · doi:10.1109/icamcs59110.2023.00028 · Cited by 9 · see citing works

A privacy-preserving dialogue system based on argumentation

Fazzinga, B., Galassi, A., & Torroni, P. (2022) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2022.200113 · Cited by 8 · see citing works

Automated Redaction of Personally Identifiable Information on Drug Labels Using Optical Character Recognition and Large Language Models for Compliance with Thailand’s Personal Data Protection Act

Thetbanthad, P., Sathanarugsawait, B., & Praneetpolgrang, P. (2025) · Applied Sciences · doi:10.3390/app15094923 · Cited by 7 · see citing works

A Survey on Current Trends and Recent Advances in Text Anonymization

Deußer, T., Sparrenberg, L., Berger, A., Hahnbück, M., Bauckhage, C., & Sifa, R. (2025) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa65442.2025.11247969 · Cited by 7 · see citing works

Beyond Individual Concerns: Multi-user Privacy in Large Language Models

Zhan, X., Seymour, W., & Such, J. M. (2024) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3640794.3665883 · Cited by 7 · see citing works

PIILO: an open-source system for personally identifiable information labeling and obfuscation

Holmes, L., Crossley, S. A., Sikka, H., & Morris, W. (2023) · Information and Learning Sciences · doi:10.1108/ils-04-2023-0032 · Cited by 7 · see citing works

Navigating the Risks: A Survey of Security and Privacy Threats in LLM-Based Agents

Gan, Y., Yang, Y., W, Z., He, P., Zeng, R., Wang, Y., Li, Q., Zhou, C., Li, S., Wang, T., et al. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3807666 · Cited by 39 · see citing works

Automatic Evaluation of Disclosure Risks of Text Anonymization Methods

Manzanares-Salor, B., Sánchez, D., & Lison, P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-13945-1_12 · Cited by 6 · see citing works

Unsupervised Text Deidentification

Morris, J. X., Chiu, J., Zabih, R., & Rush, A. M. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.352 · Cited by 7 · see citing works

Exploring How UK Public Authorities Use Redaction to Protect Personal Information

Chen, Y., & Kirkham, R. (2024) · ACM Transactions on Management Information Systems · doi:10.1145/3651989 · Cited by 6 · see citing works

DaRAV: A Tool for Visualizing De-Anonymization Risks

Adamakis, E., Boch, M., Bampoulidis, A., Margetis, G., Gindl, S., & Stephanidis, C. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00291 · Cited by 6 · see citing works

A comparative analysis, enhancement and evaluation of text anonymization with pre-trained Large Language Models

Manzanares-Salor, B., & Sánchez, D. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129474 · Cited by 7 · see citing works

ClustEm4Ano: Clustering Text Embeddings of Nominal Textual Attributes for Microdata Anonymization

Aufschläger, R., Wilhelm, S., Heigl, M., & Schramm, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-83472-1_9 · Cited by 5 · see citing works

Differentially private de-identifying textual medical document is compliant with challenging NLP analyses: Example of privacy-preserving ICD-10 code association

Tchouka, Y., Couchot, J.-F., Laiymani, D., Selles, P., & Rahmani, A. (2024) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200416 · Cited by 5 · see citing works

Anonymisation Methods for Complex Data Based on Privacy Models

Boch, M., Adamakis, E., Gindl, S., Margetis, G., & Stephanidis, C. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-45645-9_22 · Cited by 4 · see citing works

A comparative Study on AI-Driven Anonymization Techniques for Protecting Personal Data

-, M. P., -, A. K. U., -, M. R., & -, J. D. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i04.24770 · Cited by 5 · see citing works

Grandma Karl is 27 years old – research agenda for pseudonymization of research data

Volodina, E., Dobnik, S., Tiedemann, T. L., & Vu, X.-S. (2023) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice58306.2023.00047 · Cited by 4 · see citing works

Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts

Amin, S., Goldstein, N. P., Wixted, M., García‐Rudolph, A., Martínez-Costa, C., & Neumann, G. (2022) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2022.bionlp-1.20 · Cited by 5 · see citing works

EntUn: Mitigating the forget-retain dilemma in unlearning via entropy

Jung, D. (2025) · ICT Express · doi:10.1016/j.icte.2025.06.007 · Cited by 3 · see citing works

LLM-as-a-Judge for Privacy Evaluation? Exploring the Alignment of Human and LLM Perceptions of Privacy in Textual Data

Meisenbacher, S., Klymenko, A., & Matthes, F. (2025) · Proceedings of the 2025 Workshop on Human-Centered AI Privacy and Security · doi:10.1145/3733816.3760760 · Cited by 4 · see citing works

Automatic Named Entity Obfuscation in Speech

Preiss, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.39 · Cited by 3 · see citing works

Evaluating the Efficacy of AI Techniques in Textual Anonymization: A Comparative Study

Asimopoulos, D. C., Siniosoglou, I., Argyriou, V., Goudos, S. K., Psannis, K. E., Karditsioti, N., Saoulidis, T., & Sarigiannidis, P. G. (2024) · International Balkan Conference on Communications and Networking (BalkanCom) · doi:10.1109/balkancom61808.2024.10557182 · Cited by 3 · see citing works

Privacy-Preserving Domain Adaptation of Semantic Parsers

Mireshghallah, F., Su, Y., Hashimoto, T., Eisner, J. M., & Shin, R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.271 · Cited by 3 · see citing works

MuCAAT: Multilingual Contextualized Authorship Anonymization of Texts from social networks

Panov, V., Kovalchuk, M., Filatova, A., & Teryoshkin, S. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.11.016 · Cited by 2 · see citing works

Enhancing Text Anonymisation: A Study on CRF, LSTM, and ELMo for Advanced Entity Recognition

Siniosoglou, I., Asimopoulos, D. C., Argyriou, V., Λάγκας, Θ., Lytos, A., Moscholios, I. D., Goudos, S. K., & Sarigiannidis, P. G. (2024) · Panhellenic Conference on Electronics & Telecommunications (PACET) · doi:10.1109/pacet60398.2024.10497084 · Cited by 2 · see citing works

Trustworthiness in Vision-Language Models

Vu, K., & Lai, P. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6389-7_7 · Cited by 1 · see citing works

Neural text sanitization with privacy risk indicators: an empirical analysis

Papadopoulou, A. I., Lison, P., Anderson, M., Øvrelid, L., & Pilán, I. (2026) · Language Resources and Evaluation · doi:10.1007/s10579-026-09911-1 · Cited by 4 · see citing works

Custom Dataset Text Classification: An Ensemble Approach with Machine Learning and Deep Learning Models

Valluri, D., Manne, S., & Tripuraneni, N. (2023) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia60377.2023.10426114 · Cited by 1 · see citing works

RE-DACT: Adaptive Redaction and Anonymization Tool Using Machine-Learning

Kumar, K., & S, H. A. A. (2024) · International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) · doi:10.1109/icuis64676.2024.10866838 · Cited by 1 · see citing works

Spend Your Budget Wisely: Towards an Intelligent Distribution of the Privacy Budget in Differentially Private Text Rewriting

Meisenbacher, S., Lee, C. J., & Matthes, F. (2024) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/3714393.3726504 · Cited by 1 · see citing works

A Preliminary Evaluation of a Privacy-Preserving Dialogue System.

Fazzinga, B., Galassi, A., & Torroni, P. (2021) · Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) · Cited by 1 · see citing works

Procedure informatiche di tutela della trasparenza e riservatezza dei dati

Marinai, S. (2024) · Studi e saggi · doi:10.36253/979-12-215-0316-6.14 · Cited by 1 · see citing works

Multilingual Transformers for Named Entity Recognition

Vīksna, R., & Skadiņa, I. (2022) · Baltic Journal of Modern Computing · doi:10.22364/bjmc.2022.10.3.18

reteLLMe: Design Rules for Using Large Language Models to Protect the Privacy of Individuals in Their Textual Contributions

Brahem, M., Watissee, J., Eichler, C., Boiret, A., Anciaux, N., & Fuentes, J. M. D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82349-7_3

Beyond the Hype: Assessing Limitations of Large Language Models in Support Ticket Anonymization

Rafetseder, D., Weilguny, C., Haidinger, P., Pichler, H.-P., & Narzt, W. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93418-6_21

Evaluating the accuracy of automated and semi-automated anonymization tools for unstructured health records

Alrazihi, L. A., Biswas, S., & George, K. J. (2025) · Surgical Neurology International · doi:10.25259/sni_459_2025

BALANCE: A Fairness-Aware Framework for Privacy-Preserving Synthetic Clinical Text Generation

Tang, Y., & Ye, D. (2025) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3743093.3771004

LGPD Benchmark: A Legal Text Corpus for Evaluating Personal Data Pseudonymization in Brazilian PortuguesePreprint

Filho, M. A. D. S., & Ribas, B. C. (2025) · Research Square · doi:10.21203/rs.3.rs-8309951/v1

Ethical Considerations in GenAI Integration in TESOL

Savvidou, C. (2026) · Springer eBooks · doi:10.1007/978-3-032-14502-4_5

Designing Privacy Choice in Generative AI Chatbot Ecosystems

Liu, L., Li, X. A., Lin, A. Y., & Yao, Y. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790809

Ethical Challenges in Primary vs. Secondary Datasets: A Systematic Review of Manipulation and Transparency

Riska, S. Y., Widiyaningtyas, T., Elmunsyah, H., & Sendari, S. (2026) · Jurnal Ilmiah Teknologi Informasi Asia · doi:10.32815/jitika.1227

(d, c, l)-Privacy: Privacy Preservation Models for Content-Based Datasets Using Information Retrieval Techniques

Riyana, S., & Harnsamut, N. (2026) · Mathematics · doi:10.3390/math14111896

Privacy Preservation in Textual Data: A Systematic Mapping Study on Differential Privacy and Semantic Similarity

Lim-Apo, D. L., & Canedo, E. D. (2026) · Congresso Ibero-Americano em Engenharia de Software (CIbSE) · doi:10.5753/cibse.2026.42448

Enhancing Data Accessibility through Automated Personally Identifiable Information De-Identification in Crash Narratives

Devadiga, M., Vajari, M. A., Qin, X., & McRoy, S. (2026) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981261447268

A Modular Multi-Agent Multimodal AI Framework Integrating Vision–Language Models and IoT Data for Explainable Clinical Decision Support

Et-Tousy, J., Malek, C., & Zyane, A. (2026) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset68627.2026.11538732

Natural language processing for data analysis: an application on the well-being

Boi, S., Tedesco, N., & Salaris, L. (2026) · Quality & Quantity · doi:10.1007/s11135-026-02894-9

A Case Study on the Impact of Anonymization Along the RAG Pipeline

Bodea, A.-E., Meisenbacher, S., & Matthes, F. (2026) · ACM International Workshop on Security and Privacy Analytics (IWSPA) · doi:10.1145/3806007.3810957

Adaptive PII Detection via Evolutionary Orchestration and Multi-agent Debate

Bang, G., Kim, D., Kim, D., & Min, M. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-1947-6_42

Anonymous-by-Construction: An LLM-Driven Framework for Privacy-Preserving Text

Albanese, F., Ronco, P., & D’Ippolito, N. (2026) · RAIE · doi:10.1145/3786156.3788578

Privacy-Preserving Entity Transformation: A Semantic Compatibility Framework for Language Models

Desai, A., & Prakash, N. (2026) · International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) · doi:10.1109/mlise70044.2026.11607655

Automated Annotation of Privacy Information in User Interactions with Large Language Models

Zeng, H., Liu, X., Hu, Y., Niu, C., Wu, F., Tang, S., & Chen, G. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770854.3785680 · Cited by 1 · see citing works

Recent Advances in Text Anonymization: A Systematic Review

Litvak, M., & Jorge, A. (2026) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.70127

Text Re-identification Evaluation with Retrieval-Augmented Generation

Manzanares-Salor, B., & Sánchez, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37976-4_24

Automated Anonymization via NER and Template-Based Redaction: a Case Study of Greek Structured PDF Documents

Καρούσος, Ν., Vorvilas, G., & Verykios, V. S. (2026) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa71656.2026.11705448

AnonAgentBench: Measuring Coding-Agent Utility Under Reversible Repository Anonymization

Brito, L. (2026) · doi:10.1145/3847320.3847396

Privacy and Security Issues in Generative AI: GenAI Shield, a Prompt-Level Privacy and Security Analyzer

Sakharam, A. T., Sudhakarrao, B. V., Ravindra, A. S., & Shamkuwar, M. (2026) · International Journal of Science and Engineering Science Research · doi:10.63671/ijsesr.v2i4.182

Multi-Token Completion for Text Anonymization

Madaan, P., Ramesh, K., Bauer, L., Peris, C., & Field, A. (2026) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2026.eacl-long.276

Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference

Mosallanezhad, A., Beigi, G., & Liu, H. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1240 · Cited by 46 · see citing works

Privacy- and Utility-Preserving NLP with Anonymized data: A case study of Pseudonymization

Yermilov, O., Raheja, V., & Chernodub, A. (2023) · Workshop on Trustworthy Natural Language Processing (TrustNLP) · doi:10.18653/v1/2023.trustnlp-1.20 · Cited by 34 · see citing works

Automating the anonymisation of textual corpora

Sardiña, L. G. (2018) · Cited by 2 · see citing works

Unlocking the Potential of Large Language Models for Clinical Text Anonymization: A Comparative Study

Pissarra, D., Curioso, I., Alveira, J., Pereira, D., Ribeiro, B., Souper, T., Gomes, V., Carreiro, A. V., & Rolla, V. G. (2024) · Workshop on Privacy in Natural Language Processing (PrivateNLP) · doi:10.18653/v1/2024.privatenlp-1.8 · Cited by 7 · see citing works