The growth in biomedical data resources has raised potential privacy concerns and risks of genetic information leakage. For instance, exome sequencing aids clinical decisions by comparing data through web services, bu...The growth in biomedical data resources has raised potential privacy concerns and risks of genetic information leakage. For instance, exome sequencing aids clinical decisions by comparing data through web services, but it requires significant trust between users and providers. To alleviate privacy concerns, the most commonly used strategy is to anonymize sensitive data. Unfortunately, studies have shown that anonymization is insufficient to protect against reidentification attacks. Recently, privacy-preserving technologies have been applied to preserve application utility while protecting the privacy of biomedical data. We present the PICOTEES framework, a privacy-preserving online service of phenotype exploration for genetic-diagnostic variants (https://birthdefectlab.cn:3000/). PICOTEES enables privacy-preserving queries of the phenotype spectrum for a single variant by utilizing trusted execution environment technology, which can protect the privacy of the user's query information, backend models, and data, as well as the final results. We demonstrate the utility and performance of PICOTEES by exploring a bioinformatics dataset. The dataset is from a cohort containing 20,909 genetic testing patients with 3,152,508 variants from the Children's Hospital of Fudan University in China, dominated by the Chinese Han population (>99.9%). Our query results yield a large number of unreported diagnostic variants and previously reported pathogenicity.展开更多
基金funded by the Shanghai Hospital Development Center(SHDC2020CR6028-002 to W.Zhou)National Key R&D Program of China(2020YFC2006402 to Y.Lu)+7 种基金National Key R&D Program of China(2022ZD0116003 to X.Dong)the Science and Technology Commission of Shanghai(22002400700 to S.Wu)Shanghai Municipal Science and Technology Major Project(20Z11900600 to W.Zhou)National Key Research and Development Program(2018YFC0116903 to W.Zhou)Major Research Projects for Young and Middle-aged People of Fujian Province(2021ZQNZD017 to Y.Lu)supported by Key Lab Information Network Security,Ministry of Public Security(to H.Zheng and S.Wang)“Pioneer”and”Leading Goose”R&D Program of Zhejiang(No.2022C01126 to Q.Sun and S.Wang)National Key R&D Program of China(2021YFC2500802 and 2021YFC2500806 to H.Zheng and S.Wang).
文摘The growth in biomedical data resources has raised potential privacy concerns and risks of genetic information leakage. For instance, exome sequencing aids clinical decisions by comparing data through web services, but it requires significant trust between users and providers. To alleviate privacy concerns, the most commonly used strategy is to anonymize sensitive data. Unfortunately, studies have shown that anonymization is insufficient to protect against reidentification attacks. Recently, privacy-preserving technologies have been applied to preserve application utility while protecting the privacy of biomedical data. We present the PICOTEES framework, a privacy-preserving online service of phenotype exploration for genetic-diagnostic variants (https://birthdefectlab.cn:3000/). PICOTEES enables privacy-preserving queries of the phenotype spectrum for a single variant by utilizing trusted execution environment technology, which can protect the privacy of the user's query information, backend models, and data, as well as the final results. We demonstrate the utility and performance of PICOTEES by exploring a bioinformatics dataset. The dataset is from a cohort containing 20,909 genetic testing patients with 3,152,508 variants from the Children's Hospital of Fudan University in China, dominated by the Chinese Han population (>99.9%). Our query results yield a large number of unreported diagnostic variants and previously reported pathogenicity.
