课题基金 / 基金详情

项目摘要

项目成果

Xiaoqian Jiang的其他基金

相似基金

相关文献

中文摘要
翻译
项目摘要 基因组学数据共享对于加速生物医学研究和促进更高水平的力量至关重要 在分析中。然而,隐私和安全方面的担忧可能会阻碍跨机构和 国家。人类基因组数据包含可识别的独特信息,不适当的共享将 将个人隐私置于危险之中,并有可能导致敏感个人信息的泄露。此外,患者的 同意对于基因组研究至关重要,因此新的社会技术方法也是必不可少的 在进行人类基因组研究时需要考虑的研究人员。我们打算缩小技术差距,并 带来先进的使能技术来支持人类基因组研究。我们组织了一系列 评估最先进的隐私和安全模型的竞赛,具有真实世界动机的分析任务 有配套的工作间。作为一个新兴的跨学科社区,我们正在推动基因组学的前沿 隐私和安全研究。在我们以往成功的激励下,我们提议继续努力。 我们计划吸引和支持来自全国代表性不足群体的研究人员参与我们的 参加比赛和参加研讨会。
英文摘要
Project Summary Genomics data sharing is of paramount importance to accelerate biomedical research and facilitate higher power in analysis. However, privacy and security concerns can hinder large scale data sharing across institutions and nations. Human genomics data contain unique information that is identifiable, and inappropriate sharing would put individual’s privacy at risk and potentially lead to leakage of sensitive personal information. Also, patients’ consents are crucial for genomic studies, and therefore novel sociotechnical methods are also essential for researchers to consider while conducting human genomic research. We intend to close the technology gap and bring advanced enabling technology to support human genomic research. We have organized a series of competitions to evaluate state-of-the-art privacy and security models with real-world motivated analysis tasks with companion workshops. As an emerging interdisciplinary community, we are pushing the frontiers of genomic privacy and security research. Motivated by our previous successes, we are proposing to continue the effort. We plan to engage and support researchers from nationally underrepresented groups to participate in our competitions and attend the workshops.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Robust privacy preserving distributed analysis platform for cancer research: addressing data bias and disparities
  • 批准号:
    10642562
  • 项目类别:
  • 资助金额:
    $41.19万
  • 财政年份:
    2023
  • 负责人:
    Xiaoqian Jiang
  • 依托单位:
Harmonizing multiple clinical trials for Alzheimer's disease to investigate differential responses to treatment via federated counterfactual learning
Decentralized differentially-private methods for dynamic data release and analysis
  • 批准号:
    10740597
  • 项目类别:
  • 资助金额:
    $61.37万
  • 财政年份:
    2023
  • 负责人:
    Xiaoqian Jiang
  • 依托单位:
Decentralized differentially-private methods for dynamic data release and analysis
海外基金