  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.
