SCC-PG Improving Community and Neighborhood Safety through Open Data Collection

SCC-PG 通过开放数据收集改善社区和邻里安全

基本信息

  • 批准号:
    1952029
  • 负责人:
  • 金额:
    $ 15万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-08-15 至 2022-07-31
  • 项目状态:
    已结题

项目摘要

The goal of this planning grant proposal is to study how data obtained by members of the public through camera pictures, video feeds and similar technology can be used to improve public safety without unduly infringing on personal rights such as privacy. To date, this effort has been driven by governments, through CCTV and similar technology, or private companies, through devices such as doorbell cameras. In contrast, this proposal will focus on how public safety can be enhanced through the use of data that are obtained and contributed voluntarily by individuals and are publicly maintained. For example, individuals might choose to capture the license plate identifiers of the automobiles that drive through their streets. The community might then agree to share this information with policing agencies seeking to track down stolen vehicles, reckless driving, or other criminal activities. As these examples illustrate, this arrangement raises significant questions about surveillance, trust, and privacy. This project aims to study these issues from multiple perspectives—technological, historical, and legal—with the aim of developing a workable and publicly vetted approach to concerns around privacy and public safety raised by recent commercial and technological developments.This proposal addresses a new and burgeoning phenomenon. Closed circuit TV and public webcams have been available for decades, and people have used cell phone cameras on a massive scale for at least a decade. Much newer, however, is the increasing integration of such sensor capabilities in a wider set of environments, such as doorbell cameras. Newer still is the idea that this integration could be crowdsourced to the public rather than controlled by private companies. This project will study these societal issues from the following perspectives: (1) technology, including a survey of low-cost embedded devices that are suitable for collecting, encrypting, transmitting, and storing data from personally accessible devices; (2) data privacy, in which this project will appraise the protocols that might manage how these data are secured, stored, and kept private, including a characterization of anonymization methodologies; (3) historical, in which this project will review and analyze past episodes of public data collection and public safety, with the aim of determining how this history can provide guides for our current work; (4) legal, including a review of various state and federal statutes, as well as Fourth Amendment jurisprudence, with the goal of analyzing how the law might govern this type of information collection and storage; (5) safety, where this project will address the effect public data collection and dissemination of the type proposed can have on public safety.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
本规划拨款建议的目的,是研究如何在不过度侵犯个人权利(例如私隐)的情况下,利用市民透过摄影机拍摄的照片、视像及类似科技所取得的资料,改善公共安全。迄今为止,这一努力一直是由政府通过闭路电视和类似技术,或私营公司通过门铃摄像头等设备推动的。相比之下,这项提案将侧重于如何通过使用个人自愿获得和提供并公开保存的数据来加强公共安全。例如,个人可能选择捕捉在他们街道上行驶的汽车的车牌标识符。然后,社区可能会同意与寻求追踪被盗车辆,鲁莽驾驶或其他犯罪活动的警察机构分享这些信息。正如这些例子所表明的那样,这种安排引发了有关监视、信任和隐私的重大问题。该项目旨在从技术、历史和法律等多个角度研究这些问题,旨在开发一种可行的、公开审查的方法,以解决最近商业和技术发展引发的隐私和公共安全问题。这个建议针对的是一个新兴的现象。闭路电视和公共网络摄像头已经出现了几十年,人们大规模使用手机摄像头也至少有10年了。然而,较新的是在更广泛的环境中集成这种传感器功能,例如门铃摄像头。更新的想法是,这种整合可以众包给公众,而不是由私人公司控制。本项目将从以下角度研究这些社会问题:(1)技术,包括对适合收集、加密、传输和存储个人可访问设备数据的低成本嵌入式设备的调查;(2)数据隐私,该项目将评估可能管理如何保护、存储和保持这些数据隐私的协议,包括匿名化方法的特征;(3)历史,该项目将回顾和分析过去的公共数据收集和公共安全事件,目的是确定这段历史如何为我们当前的工作提供指导;(4)法律,包括对各种州和联邦法规的审查,以及第四修正案的判例,目的是分析法律如何管理这种类型的信息收集和存储;(5)安全,本项目将解决拟议的公共数据收集和传播可能对公共安全产生的影响。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

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Daniel Balasubramanian其他文献

Fine-grain analysis of common coupling and its application to a Linux case study
  • DOI:
    10.1016/j.jss.2006.12.550
  • 发表时间:
    2007-08-01
  • 期刊:
  • 影响因子:
  • 作者:
    Dror G. Feitelson;Tokunbo O.S. Adeshiyan;Daniel Balasubramanian;Yoav Etsion;Gabor Madl;Esteban P. Osses;Sameer Singh;Karlkim Suwanmongkol;Minhui Xie;Stephen R. Schach
  • 通讯作者:
    Stephen R. Schach
Common coupling and pointer variables, with application to a Linux case study
  • DOI:
    10.1007/s11219-006-9005-0
  • 发表时间:
    2007-01-17
  • 期刊:
  • 影响因子:
    2.300
  • 作者:
    Stephen R. Schach;Tokunbo O. S. Adeshiyan;Daniel Balasubramanian;Gabor Madl;Esteban P. Osses;Sameer Singh;Karlkim Suwanmongkol;Minhui Xie;Dror G. Feitelson
  • 通讯作者:
    Dror G. Feitelson
Automatically reasoning about metamodeling
  • DOI:
    10.1007/s10270-013-0315-y
  • 发表时间:
    2013-02-27
  • 期刊:
  • 影响因子:
    3.200
  • 作者:
    Ethan K. Jackson;Tihamer Levendovszky;Daniel Balasubramanian
  • 通讯作者:
    Daniel Balasubramanian
Deliberative, search-based mitigation strategies for model-based software health management
  • DOI:
    10.1007/s11334-013-0215-x
  • 发表时间:
    2013-07-28
  • 期刊:
  • 影响因子:
    1.100
  • 作者:
    Nagabhushan Mahadevan;Abhishek Dubey;Daniel Balasubramanian;Gabor Karsai
  • 通讯作者:
    Gabor Karsai
RAMPART: Reinforcement Against Malicious Penetration by Adversaries in Realistic Topologies
RAMPART:在现实拓扑中加强对抗对手的恶意渗透

Daniel Balasubramanian的其他文献

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