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ACM ASIACCS 2025 | 26 August 2025, Hanoi
Objectives
LM-SHIELD2025 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-SHIELD2025 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 AsiaCCS2025 guidelines at https://asiaccs2025.hust.edu.vn/call-for-papers/.
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: 21 February 2025 26 March 2025 25 April 2025
Notification of Acceptance: 8 May 2025 16 May 2025
Camera-Ready Deadline: 25 May 2025
Workshop Date: 26 August 2025
The Speakers
The Speakers
Speaker 1: Professor Siu Ming YIU
Speaker 1: Professor Siu Ming YIU
Professor Yiu is currently a professor and the Master Programme Director at the School of Computing and Data Science of The University of Hong Kong (HKU). He is also the Director of the School’s FinTech and Blockchain Laboratory. He was selected three times by Clarivate Analytics as one of the Highly Cited Researchers in the world in 2016, 2017 and 2019, and one of the top 1% researchers in HKU for 12 consecutive years (2011-2022).
Professor Yiu’s research areas include cryptography, cybersecurity, privacy technology, FinTech, and bioinformatics. In the areas of cryptography, he served as the conference chair in Hong Kong for ASIACRYPT 2017, one of the flag-ship conferences in the field and as the programme chair/committee members for other prestigious cybersecurity conferences. In addition to academic research, Professor Yiu has been a consultant to various companies in the areas of cybersecurity and data privacy.
Speaker 2: Prof Shui Yu
Speaker 2: Prof Shui Yu
Shui Yu is Professor of the School of Computer Science in the Faculty of Engineering and Information Technology at UTS, the Deputy Chair of the UTS Research Committee, and is a researcher of cybersecurity, privacy and the networking, communication aspects of Big Data, and applied mathematics for computer science. In 2013, he initiated a new field, networking for big data, in the networking and communication domain. Shui was the leading editor of Networking for Big Data, published in 2015, which supplied an unprecedented look at cutting-edge research on the networking and communication aspects of Big Data. Many of his research outputs have been adopted by industry, for example, the auto scale strategy of Amazon Cloud against distributed denial-of-service attacks. As the corporate world has increasingly adopted new technologies to analyze and store vast amounts of data in a bid to generate valuable insights and unlock strategic value, Shui has concentrated on the privacy and security concerns associated with big data. Among other issues, he has researched security issues associated with smart grids, which present opportunities to help solve the problems of carbon emissions and the energy crisis. He has also investigated creating anonymous transactions on Blockchain to deal with threats to users’ privacy. His anonymous communication work for web browsing privacy has been cited by more than 200 US patents. He has published two monographs and edited two books, and produced more than 600 technical papers, published in top journals such as IEEE TPDS, TC, TIFS, TMC, TKDE, TETC, ToN, and INFOCOM. His h-index is 80. Shui serves his research communities in various roles, including serving on the editorial boards of IEEE Communications Surveys and Tutorials, IEEE Communications Magazine and the IEEE Internet of Things Journal, among others. He has been a member of organizing committees for many international conferences, such as the publication chair for IEEE Globecom 2015, IEEE INFOCOM 2016 and 2017, TPC chair for IEEE Big Data Service 2015, and general chair for ACSW 2017. He served as a Distinguished Lecturer of IEEE Communications Society (2018-2021). He is a Distinguished Visitor of IEEE Computer Society (2022-2024), a voting member of IEEE ComSoc Educational Services board, and an elected member of Board of Governors of IEEE Communications Society and IEEE Vehicular Technology Society, respectively. He is a Fellow of IEEE.
The AsiaCCS 2025 Workshop on Privacy in Large Language Models (LLM) and Natural Language Processing (NLP) - (LM-SHIELD2025)
The AsiaCCS 2025 Workshop on Privacy in Large Language Models (LLM) and Natural Language Processing (NLP) - (LM-SHIELD2025)
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