Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis
arXiv:2602.16144v1 Announce Type: new 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 performa
arXiv:2602.16144v1 Announce Type: new 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.
Executive Summary
This article presents a novel framework, Missing-by-Design (MBD), for revocable multimodal sentiment analysis that enables selective removal of specific data modalities while preserving task-relevant signals. The framework combines structured representation learning with a certifiable parameter-modification pipeline, generating a machine-verifiable Modality Deletion Certificate. Experiments demonstrate strong predictive performance under incomplete inputs and a practical privacy-utility trade-off, positioning MBD as an efficient alternative to full retraining. The proposed approach has significant implications for privacy compliance and user autonomy in multimodal systems, particularly in applications involving sensitive personal data.
Key Points
- ▸ MBD is a unified framework for revocable multimodal sentiment analysis
- ▸ It combines structured representation learning with a certifiable parameter-modification pipeline
- ▸ The framework generates a machine-verifiable Modality Deletion Certificate
Merits
Strength
MBD achieves strong predictive performance under incomplete inputs, demonstrating its practical utility in real-world applications.
Demerits
Limitation
The framework's reliance on generator-based reconstruction may be computationally intensive, potentially limiting its scalability in large-scale datasets.
Expert Commentary
The Missing-by-Design framework represents a significant advancement in the field of multimodal sentiment analysis, addressing a critical requirement for privacy compliance and user autonomy. By generating a machine-verifiable Modality Deletion Certificate, MBD provides a robust and scalable solution for selectively removing specific data modalities while preserving task-relevant signals. While the framework's reliance on generator-based reconstruction may be a limitation, its practical utility and policy implications make it an important contribution to the field. Future research should focus on optimizing the framework's computational efficiency and exploring its applications in various domains.
Recommendations
- ✓ Further research is needed to optimize the framework's computational efficiency and scalability in large-scale datasets.
- ✓ MBD should be explored as a potential solution for other applications involving sensitive personal data, such as healthcare and finance.
