Cryptography and Data Security

Homomorphic Encryption: An Application to Polygenic Risk Scores

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

Privacy Preserving Epigenetic PaceMaker Stronger Privacy and Improved Efficiency

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

Secure federated Boolean count queries using fully-homomorphic cryptography

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.

Secure Deep Learning on Genomics Data via a Homomorphic Encrypted Residue Activation Network

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

Privacy-preserving genotype imputation with fully homomorphic encryption

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

SVAT: Secure Outsourcing of Variant Annotation and Genotype Aggregation

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

Ultra-Fast Homomorphic Encryption Models enable Secure Outsourcing of Genotype Imputation

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

Innocent until proven guilty: Privacy-preserving search over a central CODIS criminal database from the field

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

Private Genomes and Public SNPs: Homomorphic encryption of genotypes and phenotypes for shared quantitative genetics

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

Sketching Algorithms for Genomic Data Analysis and Querying in a Secure Enclave

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.

Secure Wavelet Matrix: Alphabet-Friendly Privacy-Preserving String Search

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.