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Privacy-preserving record linkage in large databases using secure multiparty computation
Peeter Laud1, Alisa Pankova2,3
1Cybernetica AS, Ülikooli 2, Tartu, 51003, Estonia. peeter@cyber.ee.
BMC Medical Genomics
|October 13, 2018
Summary
This study introduces a privacy-preserving data deduplication method for multiple databases, ensuring patient confidentiality. The secure multiparty computation approach is practical for large datasets, enabling accurate record linkage without data compromise.
Area of Science:
- Computer Science
- Data Security
- Cryptography
Background:
- Combining data from multiple owners, like health centers, requires privacy protection.
- Deduplication is crucial to avoid biased analysis and ensure each patient's data is counted once.
- Privacy-preserving record linkage is essential for secure data integration.
Purpose of the Study:
- To develop a privacy-preserving method for deduplicating records across multiple databases.
- To enable secure data analysis without compromising sensitive information from data owners or individuals.
Main Methods:
- Utilized secure multiparty computation (SMPC) for privacy-preserving deduplication.
- Leveraged the Sharemind SMPC platform, known for its practical speed.
- Implemented exact matching for record identification.
Main Results:
- Demonstrated feasibility with approximately 10 million records across 1000 simulated health centers.
- Achieved a computation time of about 30 minutes over a 100 Mbit/s WAN.
- Attained a high accuracy with an expected error rate of 2-40.
Conclusions:
- The developed solution is practical and ready for real-world application.
- The method offers well-defined security properties inherited from the Sharemind platform.
- Future work may extend the solution to support approximate record matching.
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