Privacy-Preserving Technologies in Data

gLinDA: global differential abundance analysis ofmicrobiomes

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

FedPyDESeq2: a federated framework for bulk RNA-seq differential expression analysis

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.

Safeguarding Privacy in Genome Research: A Comprehensive Framework for Authors

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.

Efficient Federated Learning for distributed NeuroImaging Data

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

Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets

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

FedGMMAT: Federated Generalized Linear Mixed Model Association Tests

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

A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications.

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.

Federated Learning for Predicting Compound Mechanism of Action Based on Image-data from Cell Painting

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.

Federated Learning for multi-omics: a performance evaluation in Parkinson's disease

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.

Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project

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: a Privacy-Preserving federated Machine Learning method protects patients' privacy in omic data

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

Privacy-preserving impact evaluation using Difference-in-Differences

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.

Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare

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: Generation of Proxy Panels for Privacy-aware Outsourcing of Genotype Imputation

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

Accessibility of covariance information creates vulnerability in Federated Learning frameworks

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

Assessing transcriptomic re-identification risks using discriminative sequence models

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

dsSurvival 2.0: Privacy enhancing survival curves for survival models in the federated DataSHIELD analysis system

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

Fed-ComBat: A Generalized Federated Framework for Batch Effect Harmonization in Collaborative Studies

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

FedDP: Secure Federated Learning for Disease Prediction with Imbalanced Genetic Data

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

Privacy-Preserving Statistical Analysis of Genomic Data using Compressive Mechanism with Haar Wavelet Transform

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

dsMTL - a computational framework for privacy-preserving, distributed multi-task machine learning

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.

dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD

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.

Secure and federated linear mixed model association tests

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

Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption

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.

Efficient Differentially Private Methods for a Transmission Disequilibrium Test in Genome Wide Association Studies

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

Swarm Learning as a privacy-preserving machine learning approach for disease classification

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.

Expected 10-anonymity of HyperLogLog sketches for federated queries of clinical data repositories

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.

Privacy-preserving quality control of neuroimaging datasets in federated environments

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: Secure Federated Genomic Queries and Analyses Across Jurisdictions

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

Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores

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.