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.

翻译：随着多模态系统日益处理敏感个人数据，选择性撤销特定数据模态的能力已成为隐私合规与用户自主权的关键需求。本文提出"基于设计的缺失"（Missing-by-Design, MBD）框架，这是一个将结构化表征学习与可验证参数修改流程相结合的可撤销多模态情感分析统一框架。在用户或监管机构可能要求删除特定模态信息的隐私敏感应用中，可撤销性至关重要。MBD通过学习属性感知嵌入并采用基于生成器的重构方法，在恢复缺失通道的同时保留任务相关信号。针对删除请求，该框架应用显著性驱动的候选参数选择与校准高斯更新，生成可机器验证的模态删除证书。在基准数据集上的实验表明，MBD在不完整输入下实现了强大的预测性能，并提供了实用的隐私-效用权衡，将精准遗忘定位为完全重新训练的高效替代方案。