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Example Applications

Located in applications/

Sequre includes several end-to-end privacy-preserving applications that demonstrate the framework's capabilities across genomics, healthcare, and machine learning domains.

Overview

Application File ML Model Data Type Description
Credit Score credit_score.codon Neural network MPU (partition) Privacy-preserving credit scoring classifier
DTI dti.codon Neural network MPU (partition) Drug-target interaction prediction
GANON ganon.codon Classification MPU Secure metagenomic classification
Genotype Imputation genotype_imputation.codon Linear regression MPP / MPU Impute missing genotypes across distributed cohorts
GWAS gwas.codon PCA + linear regression MPU (partition) Genome-wide association study with secure PCA
KING king.codon Kinship coefficients Sharetensor Secure kinship estimation (KING-robust method)
MI mi.codon Multiple imputation MPU Secure multiple imputation with Rubin's rules
MNIST mnist.codon Multinomial logistic regression MPU Handwritten digit classification
OPAL opal.codon Linear SVM MPU Metagenomic profiling (secure OPAL pipeline)

Patterns demonstrated

@sequre entry points

All applications use @sequre-annotated functions as their secure computation entry points:

@sequre
def gwas_protocol(mpc):
    X_mpu = MPU(mpc, local_genotypes, "partition")
    ...

MPU with horizontal partitioning

Most applications distribute data across parties using MPU(mpc, data, "partition"), where each party holds its own rows:

  • GWAS: Each hospital/biobank holds patient genotypes
  • DTI: Each institution holds drug-target pairs
  • Credit Score: Each party holds customer records

Protocol switching

Applications like GWAS and Genotype Imputation use via_mpc to switch between MHE and MPC for operations like eigenvalue decomposition and matrix inverse.

Secure ML pipeline

Applications compose Sequre's ML modules:

  • LinReg → genotype imputation, GWAS
  • LogReg (multinomial) → MNIST
  • lsvm_train → OPAL
  • PCA (random_pca_*) → GWAS
  • MI (Imputer, MI, MICE) → MI application

Configuration

Applications use TOML configuration files in applications/config/:

Config Application
credit_score.toml Credit score neural network parameters
gwas.toml GWAS dataset paths and PCA settings
king.toml KING kinship parameters
mi.toml MI imputation settings
pca.toml PCA standalone configuration

See Configuration for the full configuration reference.