Computer Science > Computation and Language
arXiv:2602.16144 (cs)
[Submitted on 18 Feb 2026 (v1), last revised 10 Mar 2026 (this version, v2)]
Title:Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis
Authors:Rong Fu, Ziming Wang, Chunlei Meng, Jiaxuan Lu, Jiekai Wu, Kangan Qian, Hao Zhang, Simon Fong
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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 practical privacy-utility trade-off, positioning surgical unlearning as an efficient alternative to full retraining.
| Comments: | |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.16144 [cs.CL] |
| (or arXiv:2602.16144v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2602.16144
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Submission history
From: Rong Fu [view email][v1] Wed, 18 Feb 2026 02:29:33 UTC (1,892 KB)
[v2] Tue, 10 Mar 2026 03:41:20 UTC (1,892 KB)
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View a PDF of the paper titled Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis, by Rong Fu and 7 other authors
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