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ACM ASIACCS 2026 | 1-5 June 2026, Bangalore, India
Objectives
LM-SHIELD2026 aims to bring together leading researchers, engineers, and professionals from academia, government, and industry to discuss and explore the complex challenges and solutions related to privacy issues in large language models (LLMs) and natural language processing (NLP). As LLMs and NLP technologies continue to advance and integrate into various sectors, concerns about privacy breaches, data protection, and ethical use have become paramount. Despite their transformative capabilities, many current systems remain vulnerable to privacy breaches that can expose sensitive information and facilitate malicious activities. To establish trust in these systems, it is essential to develop robust privacy-preserving techniques and ensure compliance with privacy regulations. This workshop will serve as a premier platform for sharing cutting-edge research, innovative approaches, and practical solutions that address privacy issues in LLM and NLP systems.
LM-SHIELD2026 aims to bring together leading researchers, engineers, and professionals from academia, government, and industry to discuss and explore the complex challenges and solutions related to privacy issues in large language models (LLMs) and natural language processing (NLP). As LLMs and NLP technologies continue to advance and integrate into various sectors, concerns about privacy breaches, data protection, and ethical use have become paramount. Despite their transformative capabilities, many current systems remain vulnerable to privacy breaches that can expose sensitive information and facilitate malicious activities. To establish trust in these systems, it is essential to develop robust privacy-preserving techniques and ensure compliance with privacy regulations. This workshop will serve as a premier platform for sharing cutting-edge research, innovative approaches, and practical solutions that address privacy issues in LLM and NLP systems.
Scope and Topics
Scope and Topics
We invite submissions of research papers and works-in-progress that address various aspects of privacy issues in LLM and NLP systems. Topics of interest (relevant to NLP and LLM) include, but are not limited to,
Privacy-Preserving Techniques
Data Management, Anonymization, and Sanitization
Machine Unlearning
Adversarial attacks and Defences
Ethics, Regulatory Aspects, and Responsible AI
Fairness and Accountability
Interpretability and Transparency
AI Agents and Collaborations
Secure and/or Privacy-Preserving Distributed Machine Learning
Decentralized LLMs.
Evaluation and Metrics
Case studies (related to areas such as consistency check, code generation, bug finding, and prompt privacy) or implementations within specific domains and applications: manufacturing and IoT, power grid and energy, medical and healthcare
Emerging challenges in LLM deployment, e.g., knowledge distillation, RAG
Submission Guidelines
Submission Guidelines
We welcome original contributions that have not been published and are not currently under consideration by any other conference or journal. Submissions should be formatted according to the ACM SIGS format and should not exceed 12 pages, including references and appendices. All other formatting must follow the AsiaCCS2026 guidelines at https://asiaccs2026.cse.iitkgp.ac.in/call-for-papers/.
Important: The review process is double-blind, so papers should not include any identifying information, such as author names, affiliations, or acknowledgments.
Ethical Declaration and Consideration
All submitted papers must include a mandatory section addressing Ethical Declaration and Consideration. This section should outline how ethical guidelines have been followed, particularly in relation to the use of LLMs and NLP. Authors must explicitly discuss any ethical concerns, including data privacy, bias mitigation, and the involvement of human subjects or domain experts in their research. Papers without this section will not be considered for review.
Submission Link
Submission Link
Important Dates
Submission Deadline: 20 January 2026
Notification of Acceptance: 16 March 2026
Camera-Ready Deadline: 1 April 2026
Workshop Date: 1 June 2026
The Speakers
The Speakers
Speaker 1: TBN
Speaker 1: TBN
Speaker 2: TBN
Speaker 2: TBN
The AsiaCCS 2026 Workshop on Privacy in Large Language Models (LLM) and Natural Language Processing (NLP) - (LM-SHIELD2026)
The AsiaCCS 2026 Workshop on Privacy in Large Language Models (LLM) and Natural Language Processing (NLP) - (LM-SHIELD2026)
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