Jordanian Journal of Informatics and Computing

ISSN: 3080-6828 (Online)

Secure Multi-Party Computation for Cross-Institutional Query Federation without Raw Data Exposure in Healthcare Databases

by 

Haider H. Razzaq

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Published: 2026

Abstract

Cross-institutional healthcare research requires secure analysis of patient data distributed across hospitals while complying with the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR). Existing federated query platforms restrict analyses to predefined aggregates and may expose institution-level statistics, whereas federated learning remains vulnerable to information leakage. This paper proposes a Secure Multi-Party Computation (SMPC) framework for privacy-preserving federated Structured Query Language (SQL) queries across multiple healthcare institutions without disclosing individual records or intermediate results. The framework introduces a federated query language and compiler that transforms queries into depth-bounded arithmetic circuits. It integrates verifiable secret sharing, Beaver-triple generation, and distributed key generation to support secure equality testing, comparison, record linkage, and aggregation. Protocol complexity and security guarantees are analyzed under semi-honest and malicious adversarial models, with malicious security approximately doubling communication requirements. An analytical cost model, calibrated using the SPDZ family of Multi-Party Computation (MPC) frameworks, evaluates scalability across 3–30 institutions and 1,000–500,000 patient records per institution. Results project that a representative cross-institutional linkage query requires less than one second and approximately 77 megabytes of communication for 15 institutions containing 10,000 records each. Four healthcare case studies demonstrate the framework’s potential applicability to rare-disease cohort discovery, drug-safety surveillance, and comparative-effectiveness research. The proposed framework provides a scalable approach to privacy-preserving healthcare analytics while supporting regulatory compliance and enabling secure collaborative research without centralizing sensitive patient information.

Keywords

Secure multi-party computationfederated query processinghealthcare data privacysecret sharingprivacy-preserving record linkageverifiable secret sharingmalicious securityHIPAAGDPR.

Secure Multi-Party Computation for Cross-Institutional Query Federation without Raw Data Exposure in Healthcare Databases is licensed under CC BY 4.0CCBY

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