用全同态加密实现跨模态隐私保护的实体消歧，兼顾安全与效率。
Multimodal Privacy-Preserving Entity Resolution with Fully Homomorphic Encryption
- 基于全同态加密，在不解密前提下处理多源异构身份数据
- 在政府金融场景中实现低误判率，且全程保护敏感信息
- 适合高合规要求领域，如政务、金融中的隐私敏感匹配
在高合规性领域，实体消歧面临严峻挑战：身份信息存在显著的数据异构性，如标识符的语法差异，且需保障数据安全。为此，我们提出一种新型多模态框架，适用于政府和金融机构常见的大规模数据集。该方法同时应对数据量大、匹配精度高和隐私保护三大难题。在整个匹配生命周期中，个人身份信息的明文始终无法被计算访问，使机构能以密码学保证客户隐私，满足严格的监管要求，同时实现可验证的低等错误率，并保持大规模计算的可行性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。