Abstract: Medical datasets frequently contain missing values, which can negatively impact machine learning models used in healthcare. However, imputing these values while ensuring patient privacy presents a significant challenge. This survey explores various privacy-preserving data imputation techniques, with a focus on Secure Multi-Party Computation (MPC). We review four imputation methods—mean, median, regression, and k-nearest neighbors (KNN)—and how each can be implemented securely in distributed medical environments. The paper also discusses hybrid approaches, integration with differential privacy, and federated settings. Our analysis concludes that MPC-based imputation provides strong privacy guarantees with high accuracy, paving the way for privacy-conscious medical data analysis.
Keywords: Data Imputation, Medical Data Privacy, Multi-Party Computation (MPC), Secure Computation, Privacy-Preserving Machine Learning
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DOI:
10.17148/IJARCCE.2025.14643
