PrivateNLP 2026
Seventh Workshop on Privacy in Natural Language Processing
Colocated with ACL 2026, San Diego (CA), USA (and on Zoom)
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Overview
Privacy-preserving data analysis has become essential in the age of Large Language Models (LLMs) where access to vast amounts of data can provide gains over tuned algorithms. A large proportion of user-contributed data comes from natural language e.g., text transcriptions from voice assistants.
It is therefore important to curate NLP datasets while preserving the privacy of the users whose data is collected, and train ML models that only retain non-identifying user data.
The workshop aims to bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to designing, building, verifying, and testing privacy preserving systems in the context of Natural Language Processing.
Information about the workshop's topics of interest can be found in the Call for Papers below.
Call for Papers
PrivateNLP invites quality research contributions in different formats:
Original research papers (long and short)
Position and opinion papers
All submissions will undergo a double-blind review process, and accepted submissions will be presented at the workshop.
Topics of interest include but are not limited to:
Privacy preserving machine learning for language models
Generating privacy preserving test sets
Data extraction attacks on NLP systems (e.g. membership inference attacks)
Differential privacy for NLP models and data
Generating Differentially private derived data
NLP, privacy and regulatory compliance
Private Generative Adversarial Networks
Privacy in Active Learning and Crowdsourcing
Privacy and Federated Learning in NLP
User perceptions on privatized personal data
Auditing provenance in language models
Continual learning under privacy constraints
NLP for studying privacy policies and other texts about privacy
Ethical ramifications of AI/NLP in support of usable privacy
Homomorphic encryption for language models
Machine unlearning methods for language models
Auditing privacy-preserving methods applied to NLP models and data
Memorization of private information by language models
Important Dates
Submission deadline: March 5, 2026 Extended to March 19, 2026
Fast-track submission deadline: March 24, 2026
Acceptance notification: April 28, 2026
Camera-ready versions: May 12, 2026
Submission deadline for presenting findings papers: May 28, 2026
Workshop: July 2 or 3, 2026
All deadlines 23:59 Anywhere on Earth
Submission Instructions
Two types of submissions are invited: full papers and short papers. Please follow the ACL submission policies.
Full papers should not exceed eight (8) pages of text, plus unlimited references. Final versions of full papers will be given one additional page of content (up to 9 pages) so that reviewers' comments can be taken into account.
Short papers may consist of up to four (4) pages of content, plus unlimited references. Upon acceptance, short papers will still be given up to five (5) content pages in the proceedings.
We also ask authors to include a limitations section (required) and a section on ethical considerations (optional), following guidelines from the main conference. These sections do not count towards the page limit. Please note that these sections should not introduce new methods, analysis, or results.
We will be using OpenReview for submissions: https://openreview.net/group?id=aclweb.org/ACL/2026/Workshop/PrivateNLP
Please note OpenReview's moderation policy for newly created profiles:
New profiles created without an institutional email will go through a moderation process that can take up to two weeks.
New profiles created with an institutional email will be activated automatically.
No anonymity period will be required for papers submitted to the workshop, per the latest updates to the ACL anonymity policy. However, submissions must still remain fully anonymized.
Fast-Track Submission
If your paper has been reviewed by ACL, EMNLP, EACL, or ARR and the average rating is higher than 2.5 (either average soundness or excitement score), the paper is qualified to be submitted to the fast-track. In the appendix, please include the reviews and a short statement discussing what parts of the paper have been revised.
Link to fast-track submissions: https://ucloud.univie.ac.at/index.php/s/FPrAMn7FwgHbPnE
Please upload the following 3 documents in a single ZIP file:
ARR reviews (including discussions and the meta-review) as a single PDF (e.g. printing the review webpage to PDF)
The submitted anonymous paper as PDF
A plain text file with the corresponding author's name and contact email
Dual Submission Policy
In addition to previously unpublished work, we invite papers on relevant topics which have been submitted to alternative venues (such as other NLP or ML conferences). Please follow double-submission policy from ACL. Accepted cross-submissions will be presented as posters, with an indication of the original venue. Selection of cross-submissions will be determined solely by the organizing committee.
Non-Archival Option
There are no formatting or page restrictions for non-archival submissions. The accepted papers to the non-archival track will be displayed on the workshop website, but will NOT be included in the workshop proceedings or otherwise archived.
Agenda
Venue: San Diego (CA), USA
Zoom link: Available on the Underline page for the workshop
Date: July 3, 2026 (Friday)
Timezone: GMT-8
Keynote Speaker
Franziska Boenisch, CISPA Helmholtz Center for Information Security
Bio: Franziska is a tenure-track faculty at the CISPA Helmholtz Center for Information Security, where she co-leads the SprintML lab. Her research focuses on private and trustworthy machine learning; during her Ph.D. at Freie Universität Berlin and Fraunhofer AISEC she pioneered the notion of individualized privacy in ML. Before joining CISPA, she was a Postdoctoral Fellow at the University of Toronto and the Vector Institute. She received an ERC Starting Grant in 2025 for research on privacy in foundation models and has been recognized with the Fraunhofer ICT Dissertation Award (2023), a GI Junior Fellowship (2024), and a Werner‑von‑Siemens Fellowship (2025).
Title: From Risks to Resilience: Protecting Privacy in Adapted Language Models
Abstract: Large language models are omnipresent and for real-world use, they are adapted to downstream tasks via prompting, fine-tuning, adapters, and related techniques. These adaptations frequently involve sensitive data, yet the privacy risks they introduce remain poorly understood. In this talk, I will present a systematic study of the fundamental privacy risks arising from LLM adaptation, covering both attack methods that expose these vulnerabilities and defenses based on differentially private variants of common adaptation techniques. I will further examine how much information empirically leaks under differential privacy, highlighting that residual risks vary widely across adaptation methods, data regimes, and the distributional gap between pretraining and adaptation data. These findings underscore the importance of rigorous privacy auditing for privately adapted LLMs.
Program
8:30 - 8:40: Welcome and opening remarks
8:40 - 9:30: Keynote (Franziska Boenisch) - From Risks to Resilience: Protecting Privacy in Adapted Language Models
9:30 - 10:30: Oral session 1
9:30 - 9:40: STAMP: Stylometric Text Anonymization with Memory-guided Policy Optimization (Zhan Shi, Yefeng Yuan, Liang Cheng, Yuhong Liu)
9:40 - 9:50: The Challenge of Identifying the Origin of Black-Box Large Language Models (Ziqing Yang, Yixin Wu, Yun Shen, Wei Dai, Michael Backes, Yang Zhang)
9:50 - 10:00: Loss Masking Under the Hood: Backdoor Concealment and Private Data Memorization in LLMs (Tagore Rao Kosireddy, Evan Lucas)
10:00 - 10:10: A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation (Stephen Meisenbacher, Angelo Kleinert, Florian Matthes)
10:10 - 10:20: Mechanistic Access Control in Large Language Models via Latent Refusal Probing (Veronica Rammouz, Đorđe Klisura, Anthony Rios)
10:20 - 10:30: Prompt Stylometry for On-Device Affect-Adaptive AI: A Feasibility Study in Linguistic Signal Detection and Response Steering (Debmalya Pal)
10:30 - 11:00: Coffee break
11:00 - 12:20: Oral session 2
11:00 - 11:10: Controlling What You Share: Assessing Language Model Adherence to Privacy Preferences (Guillem Ramirez, Alexandra Birch, Ivan Titov)
11:10 - 11:20: From Conventional Web Privacy to Agentic Disclosure: How Tool Schemas May Invite LLM Oversharing (Shahriar Shayesteh, Shomir Wilson)
11:20 - 11:30: SecureLLM: Using Inference-time Compositionality to Build Secure Language Models (Abdulrahman Alabdulkareem, Christian Michael Arnold, Yerim Lee, Pieter M Feenstra, Conner Arnold, Boris Katz, Andrei Barbu, Brian Cheung)
11:30 - 11:40: Safer Reasoning Traces: Measuring and Mitigating Chain-of-Thought Leakage in LLMs (Patrick Ahrend, Tobias Eder, Xiyang Yang, Zhiyi Pan, Georg Groh)
11:40 - 11:50: Practical Memorization Tests for Detecting Copyrighted Data in Large Language Models (Michael-Andrei Panaitescu-Liess, Aadi Palnitkar, Archit Kambhamettu, Yigitcan Kaya, Daniel Brown, Sungbin Oh, Sean Michael McLeish, Marco Bornstein, Furong Huang, Tom Goldstein)
11:50 - 12:00: Differentially-Private Text Rewriting reshapes Linguistic Style (Stefan Arnold)
12:00 - 12:10: Linguistic Identity Leakage: When Language Reveals Identity in Anonymized Text (Wajdi Zaghouani)
12:10 - 12:20: Soft Membership Is a Hypergraph: Tight Differential Privacy for Language-Model Support (Manoj Saravanan)
12:20 - 12:30: Closing remarks
Committee
Organizers
Ivan Habernal - Ruhr-University Bochum (Germany)
Sepideh Ghanavati - University of Maine (USA)
Sara Haghighi - University of Maine (USA)
Krithika Ramesh - Johns Hopkins University (USA)
Timour Igamberdiev - University of Vienna (Austria)
Shomir Wilson - Pennsylvania State University (USA)
Program Committee
Andrea Atzeni
Christina Lohr
Eugenio Martínez Camara
Isar Nejadgholi
Lizhen Qu
Peter Story
Pierre Lison
Ruyu Zhou
Sebastian Ochs
Travis Breaux
Christos Dimitrakakis
Stephen Meisenbacher
Stefan Arnold
Ildikó Pilán
Juraj Vladika
James Flemings
Mark Dras
Debabrota Basu
Contact
For questions/queries regarding the workshop or submission, please contact: privatenlp26-orga@lists.ruhr-uni-bochum.de
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