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
基本信息
- 批准号:1350914
- 负责人:
- 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
- 起止时间:2014-01-15 至 2019-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
关键基础设施系统(如智能电网)中的运营商(代理商)之间的信息共享是可靠和持续运营的基础。然而,为了系统稳定性和可靠性(效用)而共享数据与为了竞争优势(隐私)而保留数据之间的争论阻碍了此类系统中的数据共享,有时会带来灾难性的后果。这激发了一个数据共享框架,解决了竞争利益和代理的信息泄漏的关注,并使及时和受控的信息exchanges.This研究开发了一个基本的方法,在复杂的系统中,使用信息论和博弈论的分布式自利代理之间的隐私保证的信息共享。这个多学科的项目集中在智能电网的多代理网络抽象的四个相互关联的挑战:(1)与隐私约束的分布式交互的基本限制的表征;(2)信息理论的隐私措施的操作和实际意义;(3)形式化的隐私成本和信任的作用和重复的相互作用的合作;以及通过(4)用于智能电网中的隐私保证数据共享的分布式算法和协议来直接应用这些结果。该研究具有更广泛的意义,可以在具有严格隐私要求的各种复杂网络中实现信息共享,包括电子医疗保健和配水系统,并在电力系统中产生学术和行业合作。该研究项目结合了精心定制的外展工作,包括初中和高中学生的隐私意识,以及本科生和研究生,特别是女性,在研究中的积极参与。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 影响因子:0
- 作者:Liao, Jiachun;Sankar, Lalitha;Kosut, Oliver;Calmon, Flavio P.
- 通讯作者:Calmon, Flavio P.
A Tunable Loss Function for Binary Classification
- DOI:10.1109/isit.2019.8849796
- 发表时间:2019-02
- 期刊:
- 影响因子:0
- 作者:Tyler Sypherd;Mario Díaz;L. Sankar;P. Kairouz
- 通讯作者:Tyler Sypherd;Mario Díaz;L. Sankar;P. Kairouz
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Lalitha Sankar其他文献
Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest Neighbors Label Spreading
通过最近邻标签传播实现与域无关的公平校正的标签噪声鲁棒性
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Nathan Stromberg;Rohan Ayyagari;Sanmi Koyejo;Richard Nock;Lalitha Sankar - 通讯作者:
Lalitha Sankar
Last Iterate Convergence of Popov Method for Non-monotone Stochastic Variational Inequalities
非单调随机变分不等式波波夫方法的最后迭代收敛
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Daniil Vankov;A. Nedich;Lalitha Sankar - 通讯作者:
Lalitha Sankar
Lalitha Sankar的其他文献
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{{ truncateString('Lalitha Sankar', 18)}}的其他基金
Exploiting Physical and Dynamical Structures for Real-time Inference in Electric Power Systems
利用物理和动态结构进行电力系统实时推理
- 批准号:
2246658 - 财政年份:2023
- 资助金额:
$ 45.5万 - 项目类别:
Standard Grant
Collaborative Research: SCH: Fair Federated Representation Learning for Breast Cancer Risk Scoring
合作研究:SCH:乳腺癌风险评分的公平联合表示学习
- 批准号:
2205080 - 财政年份:2022
- 资助金额:
$ 45.5万 - 项目类别:
Standard Grant
Unifying Information- and Optimization-Theoretic Approaches for Modeling and Training Generative Adversarial Networks
统一信息理论和优化理论方法来建模和训练生成对抗网络
- 批准号:
2134256 - 财政年份:2021
- 资助金额:
$ 45.5万 - 项目类别:
Continuing Grant
RAPID: SaTC: FACT: Federated Analytics based Contact Tracing for COVID-19
RAPID:SaTC:事实:基于联合分析的 COVID-19 接触者追踪
- 批准号:
2031799 - 财政年份:2020
- 资助金额:
$ 45.5万 - 项目类别:
Standard Grant
CIF: Small: Alpha Loss: A New Framework for Understanding and Trading Off Computation, Accuracy, and Robustness in Machine Learning
CIF:小:Alpha 损失:理解和权衡机器学习中的计算、准确性和鲁棒性的新框架
- 批准号:
2007688 - 财政年份:2020
- 资助金额:
$ 45.5万 - 项目类别:
Standard Grant
Student Travel Support for the 2020 IEEE SGComm Conference. To be Held November, 11-13, 2020 at Arizona State University.
2020 年 IEEE SGComm 会议的学生旅行支持。
- 批准号:
2024805 - 财政年份:2020
- 资助金额:
$ 45.5万 - 项目类别:
Standard Grant
CIF: Medium: Collaborative Research: Information-theoretic Guarantees on Privacy in the Age of Learning
CIF:媒介:协作研究:学习时代隐私的信息理论保证
- 批准号:
1901243 - 财政年份:2019
- 资助金额:
$ 45.5万 - 项目类别:
Continuing Grant
Collaborative Research: High-Dimensional Spatio-Temporal Data Science for a Resilient Power Grid: Towards Real-Time Integration of Synchrophasor Data
合作研究:弹性电网的高维时空数据科学:同步相量数据的实时集成
- 批准号:
1934766 - 财政年份:2019
- 资助金额:
$ 45.5万 - 项目类别:
Continuing Grant
CIF: Small: Collaborative Research: Generative Adversarial Privacy: A Data-driven Approach to Guaranteeing Privacy and Utility
CIF:小型:协作研究:生成对抗性隐私:保证隐私和实用性的数据驱动方法
- 批准号:
1815361 - 财政年份:2018
- 资助金额:
$ 45.5万 - 项目类别:
Standard Grant
CPS: TTP Option: Synergy: A Verifiable Framework for Cyber- Physical Attacks and Countermeasures in a Resilient Electric Power Grid
CPS:TTP 选项:协同:弹性电网中网络物理攻击和对策的可验证框架
- 批准号:
1449080 - 财政年份:2015
- 资助金额:
$ 45.5万 - 项目类别:
Cooperative Agreement
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