PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies

• URL: http://arxiv.org/abs/2410.08122v1
• Date: Thu, 10 Oct 2024 17:07:57 GMT
• Title: PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies
• Authors: Arjhun Swaminathan, Anika Hannemann, Ali Burak Ünal, Nico Pfeifer, Mete Akgün,
• Abstract summary: We present a novel algorithm PP-GWAS designed to improve upon existing standards in terms of computational efficiency and scalability without sacrificing data privacy. Experimental evaluation with real world and synthetic data indicates that PP-GWAS can achieve computational speeds twice as fast as similar state-of-the-art algorithms. We have assessed its performance using various datasets, emphasizing its potential in facilitating more efficient and private genomic analyses.
• Score: 2.516577526761521
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Genome-wide association studies are pivotal in understanding the genetic underpinnings of complex traits and diseases. Collaborative, multi-site GWAS aim to enhance statistical power but face obstacles due to the sensitive nature of genomic data sharing. Current state-of-the-art methods provide a privacy-focused approach utilizing computationally expensive methods such as Secure Multi-Party Computation and Homomorphic Encryption. In this context, we present a novel algorithm PP-GWAS designed to improve upon existing standards in terms of computational efficiency and scalability without sacrificing data privacy. This algorithm employs randomized encoding within a distributed architecture to perform stacked ridge regression on a Linear Mixed Model to ensure rigorous analysis. Experimental evaluation with real world and synthetic data indicates that PP-GWAS can achieve computational speeds twice as fast as similar state-of-the-art algorithms while using lesser computational resources, all while adhering to a robust security model that caters to an all-but-one semi-honest adversary setting. We have assessed its performance using various datasets, emphasizing its potential in facilitating more efficient and private genomic analyses.

Related papers

• Efficient Privacy-Preserving KAN Inference Using Homomorphic Encryption [9.0993556073886]
  Homomorphic encryption (HE) facilitates privacy-preserving inference for deep learning models. Complex structure of KANs, incorporating nonlinear elements like the SiLU activation function and B-spline functions, renders existing privacy-preserving inference techniques inadequate. We propose an accurate and efficient privacy-preserving inference scheme tailored for KANs.
  arXiv  Detail & Related papers  (2024-09-12T04:51:27Z)
• Quantized Hierarchical Federated Learning: A Robust Approach to Statistical Heterogeneity [3.8798345704175534]
  We present a novel hierarchical federated learning algorithm that incorporates quantization for communication-efficiency. We offer a comprehensive analytical framework to evaluate its optimality gap and convergence rate. Our findings reveal that our algorithm consistently achieves high learning accuracy over a range of parameters.
  arXiv  Detail & Related papers  (2024-03-03T15:40:24Z)
• Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification [51.04894019092156]
  Federated learning (FL) has been recognized as a rapidly growing area, where the model is trained over clients under the FL orchestration (PS) In this paper, we propose a novel primal sparification algorithm for and guarantee non-smooth FL problems. Its unique insightful properties and its analyses are also presented.
  arXiv  Detail & Related papers  (2023-10-30T14:15:47Z)
• Theoretically Principled Federated Learning for Balancing Privacy and Utility [61.03993520243198]
  We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters. It can achieve personalized utility-privacy trade-off for each model parameter, on each client, at each communication round in federated learning.
  arXiv  Detail & Related papers  (2023-05-24T13:44:02Z)
• Validation Diagnostics for SBI algorithms based on Normalizing Flows [55.41644538483948]
  This work proposes easy to interpret validation diagnostics for multi-dimensional conditional (posterior) density estimators based on NF. It also offers theoretical guarantees based on results of local consistency. This work should help the design of better specified models or drive the development of novel SBI-algorithms.
  arXiv  Detail & Related papers  (2022-11-17T15:48:06Z)
• Federated Offline Reinforcement Learning [55.326673977320574]
  We propose a multi-site Markov decision process model that allows for both homogeneous and heterogeneous effects across sites. We design the first federated policy optimization algorithm for offline RL with sample complexity. We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed.
  arXiv  Detail & Related papers  (2022-06-11T18:03:26Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)
• Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis [8.863089484787835]
  We propose a distributed approximate Newton-type Newton-type training scheme, namely FedOVA, to solve the heterogeneous statistical challenge brought by heterogeneous data. FedOVA decomposes a multi-class classification problem into more straightforward binary classification problems and then combines their respective outputs using ensemble learning.
  arXiv  Detail & Related papers  (2021-10-14T17:35:24Z)
• Privacy-preserving Logistic Regression with Secret Sharing [0.0]
  We propose secret sharing-based privacy-preserving logistic regression protocols using the Newton-Raphson method. Our implementation results show that our improved method can handle large datasets used in securely training a logistic regression from multiple sources.
  arXiv  Detail & Related papers  (2021-05-14T14:53:50Z)
• Sample-based and Feature-based Federated Learning via Mini-batch SSCA [18.11773963976481]
  This paper investigates sample-based and feature-based federated optimization. We show that the proposed algorithms can preserve data privacy through the model aggregation mechanism. We also show that the proposed algorithms converge to Karush-Kuhn-Tucker points of the respective federated optimization problems.
  arXiv  Detail & Related papers  (2021-04-13T08:23:46Z)
• Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data [77.88594632644347]
  Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge. We propose a novel momentum-based method to mitigate this decentralized training difficulty.
  arXiv  Detail & Related papers  (2021-02-09T11:27:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.