Privacy-Preserving Technologies in Data

gLinDA analyzes microbiome data using swarm learning to detect differential abundance without sharing raw patient data.

FedPyDESeq2 enables privacy-preserving differential expression analysis on siloed bulk RNA-seq data, achieving results comparable to pooled data analysis and outperforming meta-analysis approaches.

This paper introduces a framework to evaluate privacy risks in genomic data sharing by assessing compliance with NIH policies and identifying vulnerabilities through self-reported practices.

The paper proposes a decentralized sparse federated learning approach for neuroimaging data to reduce communication overhead while maintaining data privacy.

SF-Relate identifies genetic relatives across distributed genomic datasets using a privacy-preserving federated approach with high accuracy and efficiency.

FedGMMAT enables privacy-preserving genetic association testing across institutions using federated learning and homomorphic encryption.

This study benchmarks the accuracy and cost-effectiveness of federated learning and consensus-based learning in medical data analysis, finding that consensus-based learning is more cost-effective with comparable accuracy.

This work uses federated learning to predict compound mechanism of action from cell painting image data, showing its effectiveness in handling data heterogeneity across institutions.

This study evaluates federated learning models for multi-omics Parkinson's disease prediction, finding they perform comparably to centrally trained models with minor performance differences.

This study evaluates federated learning's effectiveness on genomic data using UK Biobank and 1000 Genomes Project datasets, showing performance close to centralized models despite data heterogeneity.

PPML-Omics is a privacy-preserving federated learning method that balances privacy protection and model utility across three omic data tasks.

The paper develops a federated Difference-in-Differences method for privacy-preserving causal impact evaluation using the DataSHIELD platform, demonstrating its effectiveness on simulated and real-world data.

The paper introduces AFS, a tool to revoke patients' private data from pre-trained models by auditing usage and purifying knowledge, tested across multiple tasks and datasets.

ProxyTyper generates proxy panels to protect genetic data privacy during genotype imputation with minimal accuracy loss.

This study introduces an attack that reconstructs privacy-protected data in FL frameworks by exploiting covariance information and proposes defense strategies using differential privacy.

This paper introduces a discriminative sequence model to assess transcriptomic re-identification risks by improving linking attack accuracy across datasets.

The paper introduces an enhanced dsSurvival package for generating privacy-preserving survival curves within the DataSHIELD federated analysis system.

Fed-ComBat harmonizes batch effects in decentralized data, showing comparable results to centralized methods for linear and nonlinear cases.

FedDP improves disease prediction from imbalanced genetic data by combining federated learning with differential privacy and a weighted minmax loss.

This paper introduces two privacy-preserving methods for genomic data analysis using compressive mechanisms and Haar wavelet transforms, achieving differential privacy with high utility.

The paper introduces dsMTL, a framework for privacy-preserving, distributed multi-task machine learning, and evaluates its performance in identifying biological signatures across distributed datasets.

The paper introduces dsSurvival, a tool for privacy-preserving survival analysis in federated settings, enabling meta-analysis of Cox regression models without sharing individual data.

This paper introduces SafeGENIE, a secure and efficient algorithm for LMM-based GWAS that enables collaborative analysis without data sharing.

The paper introduces FAMHE, a privacy-preserving federated analytics system using multiparty homomorphic encryption, enabling accurate analysis of distributed medical data without sharing patient-level information.

The paper introduces efficient differentially private methods for a transmission disequilibrium test in genome-wide association studies, showing significant speed improvements over existing methods.

This paper introduces Swarm Learning, a decentralized machine learning approach for disease classification using transcriptomic data, demonstrating its effectiveness in developing classifiers for COVID-19, tuberculosis, and leukemias while preserving privacy.

The paper proves theoretical bounds on the 10-anonymity privacy risk of using HyperLogLog sketches for federated clinical data queries and provides code to compute these bounds.

The paper introduces decentralized quality control tools for neuroimaging data in federated environments, showing promising results in visualization and outlier detection while preserving privacy.

CanDIG enables secure, federated genomic and health data analysis across jurisdictions while maintaining data locality and privacy.

This paper shows that genetic risk scores can be used to reconstruct genotypes in private genomic databases when models are trained on overlapping participant sets.