The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition

Kuo, Tsung-Ting; Jiang, Xiaoqian; Tang, Haixu; Wang, XiaoFeng; Harmanci, Arif; Kim, Miran; Post, Kai W.; Bu, Diyue; Bath, Tyler; Kim, Jihoon; Liu, Weijie; Chen, Hongbo; Ohno-Machado, Lucila

doi:10.1093/jamia/ocac165

Cited by 15 publications

(11 citation statements)

References 70 publications

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“…Also, STI can be extended by integrating secure and privacy-preserving mechanisms through homo-morphic encryption and Tensorflow-compatible libraries such as tf-encrypted [35]) to provide secure and privacy-preserving genotype imputation [36, 37], which is imperative yet under-explored in current tools.…”

Section: Discussionmentioning

confidence: 99%

Split-Transformer Impute (STI): A Transformer Framework for Genotype Imputation

Mowlaei

Chen

et al. 2023

Preprint

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With recent advances in DNA sequencing technologies, researchers are able to acquire increasingly larger volumes of genomic datasets, enabling the training of powerful models for downstream genomic tasks. However, genome scale dataset often contain many missing values, decreasing the accuracy and power in drawing robust conclusions drawn in genomic analysis. Consequently, imputation of missing information by statistical and machine learning methods has become important. We show that the current state-of-the-art can be advanced significantly by applying a novel variation of the Transformer architecture, called Split-Transformer Impute (STI), coupled with improved preprocessing of data input into deep learning models. We performed extensive experiments to benchmark STI against existing methods using resequencing datasets from human 1000 Genomes Project and yeast genomes. Results establish superior performance of our new methods compared to competing genotype imputation methods in terms of accuracy and imputation quality score in the benchmark datasets.

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Section: Discussionmentioning

confidence: 99%

Split-Transformer Impute (STI): A Transformer Framework for Genotype Imputation

Mowlaei

Chen

et al. 2023

Preprint

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“…It can be flexibly expanded into more focused development efforts or it can be used for simply encrypt-aggregate-decrypt operations for securing intermediate data [ 109 ]. Its tool base can be expanded with community-driven efforts such as iDASH genomic privacy challenges [ 110 ].…”

Section: Discussionmentioning

confidence: 99%

COLLAGENE enables privacy-aware federated and collaborative genomic data analysis

Li,

Kim,

Zhang

et al. 2023

Genome Biol

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Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935.

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“…The current legal frameworks aim to protect genome data from malicious attacks, but the data are still the target of existing threats [39]. Encrypting genome data while sharing and accessing it provides some protection against attacks, but encryption algorithms are prone to being attacked themselves by hackers to gain access to data [40,41]. Genome data, if hacked can be manipulated and used for harmful activities.…”

Section: The Role Of Mlsecops In the Biotechnology Industry 50mentioning

confidence: 99%

Integrating MLSecOps in the Biotechnology Industry 5.0

Pervez,

J. Titus

2024

The Role of Cybersecurity in the Industry 5.0 Era [Working Title]

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Biotechnology Industry 5.0 is advancing with the integration of cutting-edge technologies such as Machine Learning (ML), the Internet of Things (IoT), and cloud computing. It is no surprise that an industry that utilizes data from customers and can alter their lives is a target of a variety of attacks. This chapter provides a perspective on how Machine Learning Security Operations (MLSecOps) can help secure the biotechnology Industry 5.0. The chapter provides an analysis of the threats in the biotechnology Industry 5.0 and how ML algorithms can help secure with industry best practices. This chapter explores the scope of MLSecOps in the biotechnology Industry 5.0, highlighting how crucial it is to comply with current regulatory frameworks. With biotechnology Industry 5.0 developing innovative solutions in healthcare, supply chain management, biomanufacturing, pharmaceutical sectors, and more, the chapter also discusses the MLSecOps best practices that industry and enterprises should follow while also considering ethical responsibilities. Overall, the chapter provides a discussion of how to integrate MLSecOps into the design, deployment, and regulation of the processes in the biotechnology Industry 5.0.

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The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition

Cited by 15 publications

References 70 publications

Split-Transformer Impute (STI): A Transformer Framework for Genotype Imputation

Split-Transformer Impute (STI): A Transformer Framework for Genotype Imputation

COLLAGENE enables privacy-aware federated and collaborative genomic data analysis

Integrating MLSecOps in the Biotechnology Industry 5.0

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