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CAREER: Privacy-Guaranteed Distributed Interactions in Critical Infrastructure Networks

CAREER: Privacy-Guaranteed Distributed Interactions in Critical Infrastructure Networks
职业:关键基础设施网络中保证隐私的分布式交互
批准号:
1350914
负责人:
Lalitha Sankar
金额:
$45.5万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-01-15 至 2019-12-31

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中文摘要
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英文摘要
Information sharing between operators (agents) in critical infrastructure systems such as the Smart Grid is fundamental to reliable and sustained operation. The contention, however, between sharing data for system stability and reliability (utility) and withholding data for competitive advantage (privacy) has stymied data sharing in such systems, sometimes with catastrophic consequences. This motivates a data sharing framework that addresses the competitive interests and information leakage concerns of agents and enables timely and controlled information exchange.This research develops a foundational approach to privacy-guaranteed information sharing among distributed self-interested agents in complex systems using information theory and game theory. This multidisciplinary project focuses on four mutually related challenges in multi-agent network abstractions of the Smart Grid: (1) characterization of the fundamental limits of distributed interaction with privacy constraints; (2) operational and practical significance of information-theoretic privacy measures; (3) formalizing the cost of privacy and the role of trust and repeated interactions for cooperation; and a direct application of these results via (4) distributed algorithms and protocols for privacy-guaranteed data sharing in the Smart Grid. The research has the broader implication of enabling information sharing in a variety of complex networks with strict privacy requirements including electronic healthcare and water distribution systems, and also engenders academic and industry collaborations in power systems. This research project incorporates carefully tailored outreach efforts including privacy awareness for middle- and high-school students, and active engagement of undergraduate and graduate students, especially females, in research.
期刊论文(2)
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会议论文
Robustness of Maximal α-Leakage to Side Information
最大 α 泄漏到辅助信息的鲁棒性
DOI: 10.1109/isit.2019.8849769
发表时间: 2019
期刊: International Symposium on Information Theory
影响因子: --
作者: [Liao, Jiachun, Sankar, Lalitha, Kosut, Oliver, Calmon, Flavio P.]
通讯作者: Calmon, Flavio P.
DOI: 10.1109/isit.2019.8849796
发表时间: 2019-02
期刊: 2019 IEEE International Symposium on Information Theory (ISIT)
影响因子: --
作者: [Tyler Sypherd;Mario Díaz;L. Sankar;P. Kairouz]
通讯作者: Tyler Sypherd;Mario Díaz;L. Sankar;P. Kairouz
Exploiting Physical and Dynamical Structures for Real-time Inference in Electric Power Systems
  • 批准号:
    2246658
  • 项目类别:
    Standard Grant
  • 资助金额:
    $36.0万
  • 财政年份:
    2023
  • 负责人:
    Lalitha Sankar
  • 依托单位:
Collaborative Research: SCH: Fair Federated Representation Learning for Breast Cancer Risk Scoring
  • 批准号:
    2205080
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2022
  • 负责人:
    Lalitha Sankar
  • 依托单位:
Unifying Information- and Optimization-Theoretic Approaches for Modeling and Training Generative Adversarial Networks
  • 批准号:
    2134256
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $110.0万
  • 财政年份:
    2021
  • 负责人:
    Lalitha Sankar
  • 依托单位:
RAPID: SaTC: FACT: Federated Analytics based Contact Tracing for COVID-19
  • 批准号:
    2031799
  • 项目类别:
    Standard Grant
  • 资助金额:
    $20.0万
  • 财政年份:
    2020
  • 负责人:
    Lalitha Sankar
  • 依托单位:
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