Miran Kim

The paper introduces a privacy-preserving genotype imputation method using haplotype classifiers, improving accuracy and throughput compared to linear models.

This paper evaluates local hidden Markov models for genotype imputation, showing they can accurately impute common, uncommon, and rare variants.

This paper develops a privacy-aware kinship inference method for admixed populations using reference panels and homomorphic encryption to protect genetic data.

The paper introduces a secure imputation server that provides encrypted genotype imputation with provable privacy, demonstrating its utility through benchmarking and simulated GWAS.

SVAT securely outsources variant annotation and genotype aggregation using homomorphic encryption to protect genetic privacy during analysis and data sharing.

This paper develops secure genotype imputation methods using homomorphic encryption, showing comparable accuracy and efficiency to non-secure methods while protecting genetic data privacy.