Li Xiong

Liu, Y. ., Zhao, S. ., Xiong, L. ., Liu, Y. ., & Chen, H. . (2023). Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model. In Proceedings of the AAAI Conference on Artificial Intelligence. Retrieved from https://par.nsf.gov/biblio/10391220

Wang, W. ., Tang, P. ., Lou, J. ., Shao, Y. ., Waller, L. ., Ko, Y.- an ., & Xiong, L. . (2024). IGAMT: Privacy-Preserving Electronic Health Record Synthesization with Heterogeneity and Irregularity. In The 38th Annual AAAI Conference on Artificial Intelligence. AAAI Press. http://doi.org/10.1609/aaai.v38i14.29491

Da, Y. ., Ahuja, R. ., Xiong, L. ., & Shahabi, C. . (2021). REACT:Real-Time Contact Tracing and Risk Monitoring via PrivacyEnhanced Mobile Tracking. In IEEE International Conference on Data Engineering. Retrieved from https://par.nsf.gov/biblio/10225082

Seyedi, S. ., Xiong, L. ., Nemati, S. ., & Clifford, G. D. (2021). An Analysis Of Protected Health Information Leakage In Deep-Learning Based De-Identification Algorithms. In Association for the Advancement of Artificial Intelligence Workshop on Privacy Preserving AI. Retrieved from https://par.nsf.gov/biblio/10392681

Liu, J. ., Lou, J. ., Liu, J. ., Xiong, L. ., Pei, J. ., & Sun, J. . (2021). Dealer: an end-to-end model marketplace with differential privacy. Proceedings of the VLDB Endowment, 14(6), 13. http://doi.org/10.14778/3447689.3447700

Liu, J. ., Lou, J. ., Xiong, L. ., Liu, J. ., & Meng, X. . (2021). Projected federated averaging with heterogeneous differential privacy. Proceedings of the VLDB Endowment, 15(4), 13. http://doi.org/10.14778/3503585.3503592

Xie, H. ., Ma, J. ., Xiong, L. ., & Yang, C. . (2021). Federated graph classification over non-iid graphs. In Advances in neural information processing systems. Retrieved from https://par.nsf.gov/biblio/10332813

Ma, J. ., Zhang, Q. ., Lou, J. ., Xiong, L. ., Bhavani, S. ., & Ho, J. C. (2021). Communication Efficient Tensor Factorization for Decentralized Healthcare Networks. In 2021 IEEE International Conference on Data Mining (ICDM) (p. 6). http://doi.org/10.1109/ICDM51629.2021.00147

Wang, H. ., Hong, H. ., Xiong, L. ., Qin, Z. ., & Hong, Y. . (2022). PrivLBS: Local Differential Privacy for Location-Based Services with Staircase Randomized Response. In Proceedings of the ACM Conference on Computer and Communications Security. Retrieved from https://par.nsf.gov/biblio/10353743