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[2602.16144] Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis
Summary
The paper presents Missing-by-Design (MBD), a framework for revocable multimodal sentiment analysis that enhances privacy compliance by allowing selective deletion of data modalities.
Why It Matters
As multimodal systems handle sensitive personal data, ensuring user privacy through the ability to revoke specific data modalities is crucial. MBD addresses this need by providing a structured approach to sentiment analysis that balances privacy and utility, making it relevant for developers and researchers in AI and data privacy.
Key Takeaways
- MBD enables selective deletion of data modalities for privacy compliance.
- The framework combines structured representation learning with a certifiable deletion process.
- Experiments demonstrate strong predictive performance even with incomplete inputs.
- MBD offers a practical trade-off between privacy and utility in sentiment analysis.
- Surgical unlearning is presented as an efficient alternative to full retraining.
Computer Science > Computation and Language arXiv:2602.16144 (cs) [Submitted on 18 Feb 2026] Title:Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis Authors:Rong Fu, Wenxin Zhang, Ziming Wang, Chunlei Meng, Jiaxuan Lu, Jiekai Wu, Kangan Qian, Hao Zhang, Simon Fong View a PDF of the paper titled Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis, by Rong Fu and 8 other authors View PDF HTML (experimental) Abstract:As multimodal systems increasingly process sensitive personal data, the ability to selectively revoke specific data modalities has become a critical requirement for privacy compliance and user autonomy. We present Missing-by-Design (MBD), a unified framework for revocable multimodal sentiment analysis that combines structured representation learning with a certifiable parameter-modification pipeline. Revocability is critical in privacy-sensitive applications where users or regulators may request removal of modality-specific information. MBD learns property-aware embeddings and employs generator-based reconstruction to recover missing channels while preserving task-relevant signals. For deletion requests, the framework applies saliency-driven candidate selection and a calibrated Gaussian update to produce a machine-verifiable Modality Deletion Certificate. Experiments on benchmark datasets show that MBD achieves strong predictive performance under incomplete inputs and delivers a p...
