@inproceedings{zhan-etal-2026-forget,
title = "Forget What{'}s Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning",
author = "Zhan, Bihao and
Zhou, Jie and
Li, Junsong and
Yang, Yutao and
Chen, Shilian and
Pan, Qianjun and
Li, Xin and
Wu, Wen and
Wu, Xingjiao and
Chen, Qin and
Yan, Hang and
Dou, Liang and
He, Liang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.39/",
pages = "847--861",
ISBN = "979-8-89176-459-0",
abstract = "Continual Learning (CL) models, while adept at sequential knowledge acquisition, face significant and often overlooked privacy challenges due to accumulating diverse information. Traditional privacy methods, like a uniform Differential Privacy (DP) budget, indiscriminately protect all data, leading to substantial model utility degradation and hindering CL deployment in privacy-sensitive areas. To overcome this, we propose a privacy-enhanced continual learning ($\texttt{PeCL}$) framework that forgets what{'}s sensitive and remembers what matters. Our approach first introduces a token-level dynamic Differential Privacy strategy that adaptively allocates privacy budgets based on the semantic sensitivity of individual tokens. This ensures robust protection for private entities while minimizing noise injection for non-sensitive, general knowledge. Second, we integrate a privacy-guided memory sculpting module. This module leverages the sensitivity analysis from our dynamic DP mechanism to intelligently forget sensitive information from the model{'}s memory and parameters, while explicitly preserving the task-invariant historical knowledge crucial for mitigating catastrophic forgetting. Extensive experiments show that $\texttt{PeCL}$ achieves a superior balance between privacy preserving and model utility, outperforming baseline models by maintaining high accuracy on previous tasks while ensuring robust privacy."
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<abstract>Continual Learning (CL) models, while adept at sequential knowledge acquisition, face significant and often overlooked privacy challenges due to accumulating diverse information. Traditional privacy methods, like a uniform Differential Privacy (DP) budget, indiscriminately protect all data, leading to substantial model utility degradation and hindering CL deployment in privacy-sensitive areas. To overcome this, we propose a privacy-enhanced continual learning (PeCL) framework that forgets what’s sensitive and remembers what matters. Our approach first introduces a token-level dynamic Differential Privacy strategy that adaptively allocates privacy budgets based on the semantic sensitivity of individual tokens. This ensures robust protection for private entities while minimizing noise injection for non-sensitive, general knowledge. Second, we integrate a privacy-guided memory sculpting module. This module leverages the sensitivity analysis from our dynamic DP mechanism to intelligently forget sensitive information from the model’s memory and parameters, while explicitly preserving the task-invariant historical knowledge crucial for mitigating catastrophic forgetting. Extensive experiments show that PeCL achieves a superior balance between privacy preserving and model utility, outperforming baseline models by maintaining high accuracy on previous tasks while ensuring robust privacy.</abstract>
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%0 Conference Proceedings
%T Forget What’s Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning
%A Zhan, Bihao
%A Zhou, Jie
%A Li, Junsong
%A Yang, Yutao
%A Chen, Shilian
%A Pan, Qianjun
%A Li, Xin
%A Wu, Wen
%A Wu, Xingjiao
%A Chen, Qin
%A Yan, Hang
%A Dou, Liang
%A He, Liang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhan-etal-2026-forget
%X Continual Learning (CL) models, while adept at sequential knowledge acquisition, face significant and often overlooked privacy challenges due to accumulating diverse information. Traditional privacy methods, like a uniform Differential Privacy (DP) budget, indiscriminately protect all data, leading to substantial model utility degradation and hindering CL deployment in privacy-sensitive areas. To overcome this, we propose a privacy-enhanced continual learning (PeCL) framework that forgets what’s sensitive and remembers what matters. Our approach first introduces a token-level dynamic Differential Privacy strategy that adaptively allocates privacy budgets based on the semantic sensitivity of individual tokens. This ensures robust protection for private entities while minimizing noise injection for non-sensitive, general knowledge. Second, we integrate a privacy-guided memory sculpting module. This module leverages the sensitivity analysis from our dynamic DP mechanism to intelligently forget sensitive information from the model’s memory and parameters, while explicitly preserving the task-invariant historical knowledge crucial for mitigating catastrophic forgetting. Extensive experiments show that PeCL achieves a superior balance between privacy preserving and model utility, outperforming baseline models by maintaining high accuracy on previous tasks while ensuring robust privacy.
%U https://aclanthology.org/2026.emnlp-main.39/
%P 847-861
Markdown (Informal)
[Forget What’s Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning](https://aclanthology.org/2026.emnlp-main.39/) (Zhan et al., EMNLP 2026)
ACL
- Bihao Zhan, Jie Zhou, Junsong Li, Yutao Yang, Shilian Chen, Qianjun Pan, Xin Li, Wen Wu, Xingjiao Wu, Qin Chen, Hang Yan, Liang Dou, and Liang He. 2026. Forget What’s Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 847–861, Budapest, Hungary. Association for Computational Linguistics.