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ITR - (ASE+NHS) - (dmc+int): Privacy-Preserving Data Integration and Sharing

ITR - (ASE+NHS) - (dmc+int): Privacy-Preserving Data Integration and Sharing
ITR - (ASE NHS) - (dmc int):隐私保护数据集成和共享
批准号:
0428168
负责人:
Christopher Clifton
金额:
$100.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2004
资助国家:
美国
项目状态:
已结题
起止时间:
2004-09-15 至 2008-08-31

项目摘要

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中文摘要
翻译
集成和共享来自多个数据源的数据一直是数据库社区面临的一个长期挑战。这个问题在许多情况下都是至关重要的,包括企业和组织的数据集成、互联网上的数据共享、政府机构之间的协作以及科学数据的交换。对国家具有重要意义的许多应用,如应急准备和反应;以及许多科学领域的研究,需要在参与者之间整合和共享数据。由于无法确保隐私,数据集成受到严重阻碍。如果没有隐私框架,消息来源就不愿分享他们的数据。问题包括害怕泄露机密信息以及保护个人隐私的规定。虽然在计算分布式数据聚合而不披露该数据方面取得了进展;例如,在保护隐私的分布式数据挖掘中,它假设数据集成问题(模式匹配、记录链接)已经解决。因此,缺乏保护隐私的数据集成框架已成为部署数据集成的关键瓶颈。该项目将开发创建和管理联邦数据库所需的技术,同时控制私有数据的披露。虽然重点将放在保护隐私的一般数据集成技术上,但该项目将在多种但特别相关的问题领域(包括科学研究和应急准备)的背景下开展工作。这些领域的专家参与开发和测试这些技术将确保对国家重要领域产生影响。
英文摘要
Integrating and sharing data from multiple sources has been a long-standing challenge in the database community. This problem is crucial in numerous contexts, including data integration for enterprises and organizations, data sharing on the Internet, collaboration among government agencies, and the exchange of scientific data. Many applications of national importance, such as emergency preparedness and response; as well as research in many scientific domains, require integrating and sharing data among participants.Data integration is seriously hampered by an inability to ensure privacy. Without a privacy framework, sources are reluctant to share their data. Problems include fear of disclosing confidential information as well as regulations protecting individual privacy. While there has been progress in computing aggregations of distributed data without disclosing that data; e.g., privacy-preserving distributed data mining, it assumes data integration problems (schema matching, record linkage) are solved. As a consequence, the lack of a privacy-preserving data integration framework has become a key bottleneck to deploying data integration.This project will develop the technology needed to create and manage federated databases while controlling the disclosure of private data. While the emphasis will be on general techniques for data integration that preserve privacy, the project will work in the context of diverse but particularly relevant problem domains, including scientific research and emergency preparedness. Involvement of domain experts from these fields in developing and testing the techniques will ensure impact on areas of national importance.
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会议论文
Collaborative Research: SaTC: CORE: Medium: Broad-Spectrum Facial Image Protection with Provable Privacy Guarantees
  • 批准号:
    2114123
  • 项目类别:
    Standard Grant
  • 资助金额:
    $53.12万
  • 财政年份:
    2021
  • 负责人:
    Christopher Clifton
  • 依托单位:
Collaborative Research: Workshop to Develop a Roadmap for Greater Public Use of Privacy-Sensitive Government Data
  • 批准号:
    2129895
  • 项目类别:
    Standard Grant
  • 资助金额:
    $3.17万
  • 财政年份:
    2021
  • 负责人:
    Christopher Clifton
  • 依托单位:
FAI: Identifying, Measuring, and Mitigating Fairness Issues in AI
  • 批准号:
    1939728
  • 项目类别:
    Standard Grant
  • 资助金额:
    $21.69万
  • 财政年份:
    2020
  • 负责人:
    Christopher Clifton
  • 依托单位:
Collaborative Research: ITR: Distributed Data Mining to Protect Information Privacy
  • 批准号:
    0312357
  • 项目类别:
    Standard Grant
  • 资助金额:
    $27.63万
  • 财政年份:
    2003
  • 负责人:
    Christopher Clifton
  • 依托单位:
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