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.

翻译：高合规性领域中的实体解析是一个长期存在的复杂问题，其核心挑战在于：身份的安全匹配常因显著的数据异质性（包括个人标识符的句法变异）而受阻。为此，我们提出一种新颖的多模态框架，该框架可处理政府及金融机构典型的海量数据集。具体而言，我们的方法旨在应对数据体量、匹配保真度与隐私保护这三重挑战。因此，在整个匹配生命周期中，个人可识别信息的底层明文在计算上始终不可访问，从而使机构能够在严格满足严苛监管要求的同时，通过密码学手段确保客户机密性，并实现显著的低等错误率，同时保持大规模计算的可处理性。