Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning
arXiv:2509.12958v2 Announce Type: replace 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 appro
The proliferation of AI models handling sensitive data in continual learning scenarios necessitates robust and practical privacy solutions that balance utility and protection.
This development addresses a critical barrier to deploying continual learning AI in privacy-sensitive domains, potentially broadening its application significantly.
The ability to selectively protect sensitive data at a granular level within AI models mitigates the trade-off between privacy and model utility, making privacy-enhancing technologies more viable for complex AI systems.
- · AI developers
- · Healthcare sector
- · Financial services
- · Privacy-enhancing technology (PET) providers
- · Traditional uniform differential privacy methods
- · Entities struggling with data privacy compliance
AI models can be deployed more safely and effectively in environments with strict data privacy requirements, such as medical research or confidential business processes.
Increased trust in AI systems due to enhanced privacy guarantees will accelerate adoption and integration into critical infrastructure and personal applications.
The development of fine-grained privacy controls for AI could influence future data governance regulations, emphasizing selective protection over blanket restrictions.
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