| 2026 | ACL | Do LLMs Really Memorize Personally Identifiable Information? Revisiting PII Leakage with a Cue-Controlled Memorization Framework. | Xiaoyu Luo, Yiyi Chen, Qiongxiu Li, Johannes Bjerva |
| 2025 | EMNLP | Shared Path: Unraveling Memorization in Multilingual LLMs through Language Similarities. | Xiaoyu Luo, Yiyi Chen, Johannes Bjerva, Qiongxiu Li |
| 2025 | ICASSP | Re-Evaluating Privacy in Centralized and Decentralized Learning: An Information-Theoretical and Empirical Study. | Changlong Ji, Richard Heusdens, Stephane Maag, Qiongxiu Li |
| 2025 | ICASSP | Privacy-Preserving Distributed Maximum Consensus Without Accuracy Loss. | Wenrui Yu, Richard Heusdens, Jun Pang, Qiongxiu Li |
| 2025 | ICLR | ADBM: Adversarial Diffusion Bridge Model for Reliable Adversarial Purification. | Xiao Li, Wenxuan Sun, Huanran Chen, Qiongxiu Li, Yingzhe He, Jie Shi, Xiaolin Hu |
| 2025 | NAACL | Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis. | Yiyi Chen, Qiongxiu Li, Russa Biswas, Johannes Bjerva |
| 2024 | ICASSP | Privacy-Preserving Distributed Optimisation using Stochastic PDMM. | Sebastian O. Jordan, Qiongxiu Li, Richard Heusdens |
| 2024 | ICASSP | On the Privacy of Federated Clustering: a Cryptographic View. | Qiongxiu Li, Lixia Luo |
| 2024 | ICASSP | Topology-Dependent Privacy Bound for Decentralized Federated Learning. | Qiongxiu Li, Wenrui Yu, Changlong Ji, Richard Heusdens |
| 2022 | ICASSP | Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model Using Subspace Perturbation. | Qiongxiu Li, Jaron Skovsted Gundersen, Katrine Tjell, Rafal Wisniewski, Mads Grsbll Christensen |
| 2020 | ICASSP | Convex Optimisation-Based Privacy-Preserving Distributed Average Consensus in Wireless Sensor Networks. | Qiongxiu Li, Richard Heusdens, Mads Grsbll Christensen |