AMPPERE：一个用于隐私保护实体解析计算的通用抽象机
AMPPERE: A Universal Abstract Machine for Privacy-Preserving Entity Resolution Evaluation
摘要：
实体解析是识别不同数据集中引用现实世界中相同实体的记录的任务。在敏感域（例如金融账户、医院健康记录）中，实体解析必须满足隐私要求，以避免向不受信任方泄露个人身份信息等敏感信息。现有的解决方案要么过于算法特定，要么在计算的准确性、隐私和运行时效率之间存在隐含的权衡。我们提出了AMMPERE，一个抽象的计算模型进行普遍的隐私保护实体决议。AMPPERE提供了抽象，封装了多种算法和平台无关的方法，使用Jaccard相似性的变体来执行私有数据匹配和实体解析。具体来说，我们表明，双方可以执行实体解析他们的数据，而不会泄露敏感信息。我们严格比较和分析的可行性，性能开销和隐私保护性能的这些方法的Sharemind多方计算（MPC）平台上，以及PALISADE，基于格的同态加密库。AMPPERE系统证明了隐私保护实体解决方案对真实世界数据的有效性，同时提供了防止信息泄露的诱导成本的精确表征。
英文摘要：
Entity resolution is the task of identifying records in different datasets that refer to the same entity in the real world. In sensitive domains (e.g. financial accounts, hospital health records), entity resolution must meet privacy requirements to avoid revealing sensitive information such as personal identifiable information to untrusted parties. Existing solutions are either too algorithmically-specific or come with an implicit trade-off between accuracy of the computation, privacy, and run-time efficiency. We propose AMMPERE, an abstract computation model for performing universal privacy-preserving entity resolution. AMPPERE offers abstractions that encapsulate multiple algorithmic and platform-agnostic approaches using variants of Jaccard similarity to perform private data matching and entity resolution. Specifically, we show that two parties can perform entity resolution over their data, without leaking sensitive information. We rigorously compare and analyze the feasibility, performance overhead and privacy-preserving properties of these approaches on the Sharemind multi-party computation (MPC) platform as well as on PALISADE, a lattice-based homomorphic encryption library. The AMPPERE system demonstrates the efficacy of privacy-preserving entity resolution for real-world data while providing a precise characterization of the induced cost of preventing information leakage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
