Cryptography and Data Security

This paper applies fully homomorphic encryption to compute polygenic risk scores securely, showing high accuracy and feasibility with synthetic and real data.

This paper introduces a privacy-preserving protocol for the Epigenetic Pacemaker model, improving efficiency and privacy compared to prior methods.

This paper introduces a secure federated Boolean count query method using homomorphic encryption to address data deduplication and record unification challenges in distributed biomedical data.

This paper introduces ResActNet, a homomorphic encryption-friendly deep learning network for secure genomics analysis, achieving low approximation errors in classification and regression tasks.

This paper develops a privacy-preserving genotype imputation algorithm using homomorphic encryption, achieving performance comparable to plaintext methods.

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.

The paper introduces a cryptographic algorithm that allows private DNA profile matching against a CODIS database without revealing the profile to the database provider.

This paper introduces a homomorphic encryption method for genotypes and phenotypes to enable privacy-preserving quantitative genetic analysis across studies.

SkSES enables secure and privacy-preserving SNP analysis across encrypted genomic data using sketching algorithms in Intel SGX enclaves, demonstrating efficient SNP querying in a large dataset.

The paper introduces a privacy-preserving string search algorithm that operates in logarithmic time relative to alphabet size, demonstrating efficiency on protein sequences and clinical texts.