Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis

TL;DR

This paper introduces Missing-by-Design (MBD), a framework enabling privacy-compliant, certifiable removal of data modalities in multimodal sentiment analysis without retraining.

Contribution

MBD combines structured representation learning with a certifiable deletion pipeline, allowing selective modality removal while maintaining task performance.

Findings

MBD achieves strong predictive accuracy with incomplete inputs.

The framework provides a machine-verifiable Modality Deletion Certificate.

MBD offers an efficient alternative to full retraining for privacy compliance.

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.