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Efficient Federated Kinship Relationship Identification.

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This study introduces Incremental Update Kinship Identification (INK), a novel framework for privacy-preserving kinship estimation. INK enhances accuracy in identifying genetic relationships across federated data while minimizing the exposure of sensitive genetic markers.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Privacy-Preserving Technologies

Background:

  • Kinship relationship estimation is crucial in genomic studies.
  • Genetic data silos present challenges for cross-warehouse relationship retrieval.
  • Accurate identification of related individuals is vital for research and clinical use.

Purpose of the Study:

  • To propose a novel privacy-preserving framework for kinship relationship estimation.
  • To address the challenge of identifying kinship across federated genetic data warehouses.
  • To balance privacy with the accuracy of kinship estimation.

Main Methods:

  • Developed the Incremental Update Kinship Identification (INK) framework.
  • Incorporated an incremental process for dynamic updates.
  • Utilized auxiliary information and informative scores to manage privacy-accuracy trade-offs.

Main Results:

  • INK achieves higher correctness in kinship identification.
  • The framework successfully minimizes the exposure of genetic markers.
  • Empirical evaluations demonstrate the effectiveness of the proposed approach.

Conclusions:

  • INK offers an effective solution for privacy-preserving kinship estimation in federated environments.
  • The framework provides a controllable balance between data privacy and estimation accuracy.
  • This approach facilitates more secure and accurate genomic data analysis for research and clinical applications.