Information Accountability for Advanced Research Data Management
高级研究数据管理的信息责任
基本信息
- 批准号:RGPIN-2016-06062
- 负责人:
- 金额:$ 1.6万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2019
- 资助国家:加拿大
- 起止时间:2019-01-01 至 2020-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
To realize the full potential of data-intensive research in a collaborative research environment, there are several challenges that need to be addressed by the development of sufficient technological solutions. At the core of the challenges are information privacy in data collection, storage, transformation, and sharing, particularly when research data are originated from human activities and health records. In response to these challenges, the long term goal of this research program is to significantly enhance knowledge on information privacy and develop methods to address two interrelated privacy problems in the context of data-intensive research: (1) privacy self-management and (2) privacy auditing (monitoring). Our first research goal is to develop and evaluate methods for semantically codifying privacy expert's knowledge in form of privacy profiles such that it can be substituted for complex consent forms. The methods will improve users ability to express their privacy preferences and make informed decisions about the consequences of sharing their data. Our second research goal is enforcing the conditions and constraints defined through associated privacy profiles on data lifecycle (collect, use, transformation, and disclosure). We understand a preventive approach (e.g., access control, encryption or anonymization) to enforce data privacy particularly in the research context is inadequate since privacy obligations need to be fulfilled after the access is granted. Therefore, the privacy is enforced if one could hold the data custodian accountable a posteriori for the treatment of the data. We intend to complement the preventive approach with enhancing knowledge on information accountability. We expect the outcome of our research will directly impact people's ability to make informed decisions on their privacy without excessive burden. Enabling individuals to foresee the consequences of sharing their data will also foster research for public good since individuals concerns on data misuse will be addressed. The research will benefit the data custodians too as an integrated log mechanism lowers the barriers to make organizations accountable for their treatment of sensitive personal information. The long-term research in this field will engender research collaboration both within Canada and internationally. A transparent process of data privacy maximizes the value of collected data, facilitates compliance with ethics and privacy policies, maintains the provenance of data for long-term exploration, and fosters reproducible science. Canada has been recognized as a leader in protecting individuals privacy by developing advanced privacy policies and regulations. The long-term research in this field will support the Canadian lead by providing technological supports to enforce the privacy policies and regulations in the international collaborative research context.
为了在协作研究环境中实现数据密集型研究的全部潜力,有几个挑战需要通过开发足够的技术解决方案来解决。这些挑战的核心是数据收集、存储、转换和共享中的信息隐私,特别是当研究数据来自人类活动和健康记录时。为了应对这些挑战,本研究计划的长期目标是显著提高对信息隐私的认识,并开发方法来解决数据密集型研究背景下的两个相互关联的隐私问题:(1)隐私自我管理和(2)隐私审计(监控)。我们的第一个研究目标是开发和评估以隐私配置文件的形式对隐私专家的知识进行语义编码的方法,以便它可以取代复杂的同意书。这些方法将提高用户表达隐私偏好的能力,并就共享数据的后果做出明智的决定。我们的第二个研究目标是强制实施通过相关隐私配置文件在数据生命周期(收集、使用、转换和披露)上定义的条件和约束。我们理解,特别是在研究背景下,执行数据隐私的预防性方法(例如,访问控制、加密或匿名化)是不够的,因为在授予访问权限后,需要履行隐私义务。因此,如果可以在事后追究数据保管人对数据处理的责任,那么隐私就是强制的。我们打算通过加强对信息问责的了解来补充预防办法。我们预计我们的研究结果将直接影响人们在没有过重负担的情况下对自己的隐私做出明智决定的能力。使个人能够预见共享数据的后果也将促进公益研究,因为个人对数据滥用的担忧将得到解决。这项研究也将使数据托管人员受益,因为集成的日志机制降低了让组织对其处理敏感个人信息负责的门槛。这一领域的长期研究将在加拿大国内和国际上产生研究合作。透明的数据隐私保护过程最大限度地提高了收集数据的价值,促进了对道德和隐私政策的遵守,维护了用于长期探索的数据的出处,并促进了可重复的科学。加拿大已被公认为是通过制定先进的隐私政策和法规来保护个人隐私的领先者。该领域的长期研究将通过提供技术支持,在国际合作研究背景下执行隐私政策和法规,从而支持加拿大的领先地位。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Samavi, Reza其他文献
Machine Learning and Mobile Health Monitoring Platforms: A Case Study on Research and Implementation Challenges
- DOI:
10.1007/s41666-018-0021-1 - 发表时间:
2018-06-01 - 期刊:
- 影响因子:5.9
- 作者:
Boursalie, Omar;Samavi, Reza;Doyle, Thomas E. - 通讯作者:
Doyle, Thomas E.
MLCM: Multi-Label Confusion Matrix
- DOI:
10.1109/access.2022.3151048 - 发表时间:
2022-01-01 - 期刊:
- 影响因子:3.9
- 作者:
Heydarian, Mohammadreza;Doyle, Thomas E.;Samavi, Reza - 通讯作者:
Samavi, Reza
Using Medical Imaging Effective Dose in Deep Learning Models: Estimation and Evaluation
- DOI:
10.1109/trpms.2020.3029038 - 发表时间:
2021-03-01 - 期刊:
- 影响因子:4.4
- 作者:
Boursalie, Omar;Samavi, Reza;Koff, David A. - 通讯作者:
Koff, David A.
Samavi, Reza的其他文献
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{{ truncateString('Samavi, Reza', 18)}}的其他基金
Information Accountability for Advanced Research Data Management
高级研究数据管理的信息责任
- 批准号:
RGPIN-2016-06062 - 财政年份:2022
- 资助金额:
$ 1.6万 - 项目类别:
Discovery Grants Program - Individual
Information Accountability for Advanced Research Data Management
高级研究数据管理的信息责任
- 批准号:
RGPIN-2016-06062 - 财政年份:2021
- 资助金额:
$ 1.6万 - 项目类别:
Discovery Grants Program - Individual
Information Accountability for Advanced Research Data Management
高级研究数据管理的信息责任
- 批准号:
RGPIN-2016-06062 - 财政年份:2020
- 资助金额:
$ 1.6万 - 项目类别:
Discovery Grants Program - Individual
Information Accountability for Advanced Research Data Management
高级研究数据管理的信息责任
- 批准号:
RGPIN-2016-06062 - 财政年份:2018
- 资助金额:
$ 1.6万 - 项目类别:
Discovery Grants Program - Individual
Information Accountability for Advanced Research Data Management
高级研究数据管理的信息责任
- 批准号:
RGPIN-2016-06062 - 财政年份:2017
- 资助金额:
$ 1.6万 - 项目类别:
Discovery Grants Program - Individual
Information Accountability for Advanced Research Data Management
高级研究数据管理的信息责任
- 批准号:
RGPIN-2016-06062 - 财政年份:2016
- 资助金额:
$ 1.6万 - 项目类别:
Discovery Grants Program - Individual
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