# Multimodal Privacy-Preserving Entity Resolution with Fully Homomorphic Encryption

arXiv: https://arxiv.org/abs/2601.18612 · HTML: https://mlsanity.com/paper/2601.18612/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 4 of 4)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Does CLIP Know My Face?](https://mlsanity.com/paper/2209.07341/) (2022)
- [HEFT: Homomorphically Encrypted Fusion of Biometric Templates](https://mlsanity.com/paper/2208.07241/) (2022)

## Abstract (arXiv)

The canonical challenge of entity resolution within high-compliance sectors, where secure identity reconciliation is frequently confounded by significant data heterogeneity, including syntactic variations in personal identifiers, is a longstanding and complex problem. To this end, we introduce a novel multimodal framework operating with the voluminous data sets typical of government and financial institutions. Specifically, our methodology is designed to address the tripartite challenge of data volume, matching fidelity, and privacy. Consequently, the underlying plaintext of personally identifiable information remains computationally inaccessible throughout the matching lifecycle, empowering institutions to rigorously satisfy stringent regulatory mandates with cryptographic assurances of client confidentiality while achieving a demonstrably low equal error rate and maintaining computational tractability at scale.