  Patient datasets contain confidential information which is protected by laws
and regulations such as HIPAA and GDPR. Ensuring comprehensive patient
information necessitates privacy-preserving entity resolution (PPER), which
identifies identical patient entities across multiple databases from different
healthcare organizations while maintaining data privacy. Existing methods often
lack cryptographic security or are computationally impractical for real-world
datasets. We introduce a PPER pipeline based on AMPPERE, a secure abstract
computation model utilizing cryptographic tools like homomorphic encryption.
Our tailored approach incorporates extensive parallelization techniques and
optimal parameters specifically for patient datasets. Experimental results
demonstrate the proposed method's effectiveness in terms of accuracy and
efficiency compared to various baselines.
