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1. Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning

   Lai, Phung; Hu, Han; Phan, Hai; Jin, Ruoming; Thai, My; Chen, An (August 2023, Proceedings of The 1st Conference on Lifelong Learning Agents)
2. LIFELONG DP: CONSISTENTLY BOUNDED DIFFERENTIAL PRIVACY IN LIFELONG MACHINE LEARNING

   Lai, Phung; Hu, Han; Phan, NhatHai; Jin, Ruoming; Thai, My T.; Chen, An M. (July 2022, 1st Conference on Lifelong Learning Agents)

   Precup, Doina; Chandar, Sarath; Pascanu, Razvan (Ed.)

   In this paper, we show that the process of continually learning new tasks and memorizing previous tasks introduces unknown privacy risks and challenges to bound the privacy loss. Based upon this, we introduce a formal definition of Lifelong DP, in which the participation of any data tuples in the training set of any tasks is protected, under a consistently bounded DP protection, given a growing stream of tasks. A consistently bounded DP means having only one fixed value of the DP privacy budget, regardless of the number of tasks. To preserve Lifelong DP, we propose a scalable and heterogeneous algorithm, called L2DP-ML with a streaming batch training, to efficiently train and continue releasing new versions of an L2M model, given the heterogeneity in terms of data sizes and the training order of tasks, without affecting DP protection of the private training set. An end-to-end theoretical analysis and thorough evaluations show that our mechanism is significantly better than baseline approaches in preserving Lifelong DP. The implementation of L2DP-ML is available at: https://github.com/haiphanNJIT/PrivateDeepLearning. 

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3. Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning

   Lai, Phung; Hu, Han; Phan, NhatHai; Jin, Ruoming; Thai, My T.; Chen, An (January 2022, Conference on Lifelong Learning Agents. PMLR.)
4. Differentially Private Lifelong Learning

   NhatHai Phan, My T. (December 2019, Privacy in Machine Learning (PriML), NeurIPS'19 Workshop)

   null (Ed.)
5. Simple Lifelong Learning Machines

   Dey, Jayanta; Vogelstein, Joshua T; Helm, Hayden S; LeVine, Will; Mehta, Ronak D; Tomita, Tyler M; Xu, Haoyin; Geisa, Ali; Wang, Qingyang; van_de_Ven, Gido M; et al (July 2025, IEEE transactions on pattern analysis and machine intelligence)