SGER: Privacy-Preserving Communication in Wireless Networks

SGER:无线网络中的隐私保护通信

基本信息

  • 批准号:
    0848569
  • 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:
    2009
  • 资助国家:
    美国
  • 起止时间:
    2009-01-15 至 2011-12-31
  • 项目状态:
    已结题

项目摘要

Wireless networks play a key role in national critical information infrastructure. Mainly due to lack of a protected physical boundary, wireless communications are inherently less secure than its wired counterpart. Privacy has been ranked as the top security concern of wireless users. Inadequate privacy protection can result in significant network information retrieval that enables the adversaries to perform destructive denial-of-service (DoS) attacks, or even paralyze the entire wireless networks. While confidentiality of the message can be ensured through content encryption, it is much more challenging to provide privacy service for wireless networks. In fact, privacy is one of the most notable challenges looming on the horizon that threatens successful deployment of WSN. In this research, we propose to design efficient cryptographic algorithms and network security protocols that can achieve privacy-preserving communications for both MANET and WSN. More specifically, we plan to: (i) Develop efficient cryptographic algorithms that can achieve unconditional privacy-preserving communications even in the presence of local eavesdropper while reconciling communication accountability; (ii) Develop efficient anonymous authentication schemes and protocols for secure user access control and anonymous routing in MANET and WSN. That is, while each user is being authenticated, the user's real identity and location will remain secret for adversaries. (iii) Establish a scientific foundation for theoretical characterization and quantitative measurement of privacy in secure network communications. Our research is expected to have a significant impact on personal privacy, national critical information security as well as homeland security.
无线网络在国家关键信息基础设施中发挥着关键作用。主要由于缺乏受保护的物理边界,无线通信本质上不如有线通信安全。隐私已被列为无线用户最关心的安全问题。不充分的隐私保护会导致大量的网络信息检索,使对手能够执行破坏性的拒绝服务(DoS)攻击,甚至使整个无线网络瘫痪。虽然通过内容加密可以确保消息的机密性,但为无线网络提供隐私服务更具挑战性。事实上,隐私是威胁无线传感器网络成功部署的最显着挑战之一。在这项研究中,我们建议设计有效的密码算法和网络安全协议,可以实现隐私保护的移动自组网和无线传感器网络的通信。更具体地说,我们计划:(一)开发高效的加密算法,可以实现无条件的隐私保护的通信,即使在存在本地窃听者,同时协调通信问责制;(二)开发高效的匿名认证方案和协议,安全的用户访问控制和匿名路由的MANET和WSN。也就是说,当每个用户都被认证时,用户的真实的身份和位置将对对手保持秘密。(iii)为安全网络通信中隐私的理论表征和定量测量奠定科学基础。我们的研究预计将对个人隐私,国家关键信息安全以及国土安全产生重大影响。

项目成果

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Jian Ren其他文献

末次冰期至全新世白令海东南部古气候变化
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    4
  • 作者:
    Oliver Esper;Jian Ren;Gerhard Kuhn;Hartmut Kuehn
  • 通讯作者:
    Hartmut Kuehn
LinSOS: Secure outsourcing of linear computations based on affine mapping
LinSOS:基于仿射映射的线性计算的安全外包
Intravascular polarization sensitive optical coherence tomography in human patients
人类患者的血管内偏振敏感光学相干断层扫描
  • DOI:
    10.1364/cleo_at.2016.aw1o.2
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    0
  • 作者:
    M. Villiger;A. Karanasos;Jian Ren;N. Lippok;M. Shishkov;G. van Soest;S. Nadkarni;E. Regar;B. Bouma
  • 通讯作者:
    B. Bouma
Dispersion analysis and measurement of potassium tantalate niobate crystals by broadband optical interferometers.
宽带光学干涉仪对钽铌酸钾晶体的色散分析和测量。
  • DOI:
    10.1364/ao.56.000234
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    1.9
  • 作者:
    Jian Ren
  • 通讯作者:
    Jian Ren
Stimulation of anterior pituitary galanin and prolactin gene expression in suckling rats
刺激乳鼠垂体前叶甘丙肽和催乳素基因表达
  • DOI:
    10.1385/endo:11:3:251
  • 发表时间:
    1999
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Jian Ren;J. Koenig;S. Hooi
  • 通讯作者:
    S. Hooi

Jian Ren的其他文献

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{{ truncateString('Jian Ren', 18)}}的其他基金

RUI: SpecEES: Collaborative Research: Enabling Secure, Energy-Efficient, and Smart In-Band Full Duplex Wireless
RUI:SpecEES:协作研究:实现安全、节能和智能的带内全双工无线
  • 批准号:
    1923409
  • 财政年份:
    2019
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
SPX: Toward Network Level Parallel Computing: Security, Efficiency and Scalability
SPX:迈向网络级并行计算:安全性、效率和可扩展性
  • 批准号:
    1919154
  • 财政年份:
    2019
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
STARSS: Small: Collaborative: Zero-power Dynamic Signature for Trust Verification of Passive Sensors and Tags
STARSS:小型:协作:用于无源传感器和标签的信任验证的零功耗动态签名
  • 批准号:
    1524520
  • 财政年份:
    2015
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
NeTS: Small: Adaptive Network Coding for Wireless Relay Networks
NeTS:小型:无线中继网络的自适应网络编码
  • 批准号:
    1117831
  • 财政年份:
    2011
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
RAPID: Collaborative Research: Gulf of Mexico Oil Spill Impact on Beach Soil: Radar and Radar Sensor Network-Based Approaches
RAPID:合作研究:墨西哥湾漏油对海滩土壤的影响:雷达和基于雷达传感器网络的方法
  • 批准号:
    1050326
  • 财政年份:
    2010
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
CAREER: Towards Cognitive Communications in Wireless Networks
职业:无线网络中的认知通信
  • 批准号:
    0845812
  • 财政年份:
    2009
  • 资助金额:
    $ 15万
  • 项目类别:
    Continuing Grant

相似海外基金

CAREER: Architectural Foundations for Practical Privacy-Preserving Computation
职业:实用隐私保护计算的架构基础
  • 批准号:
    2340137
  • 财政年份:
    2024
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Collaborative Research: SHF: Small: Efficient and Scalable Privacy-Preserving Neural Network Inference based on Ciphertext-Ciphertext Fully Homomorphic Encryption
合作研究:SHF:小型:基于密文-密文全同态加密的高效、可扩展的隐私保护神经网络推理
  • 批准号:
    2412357
  • 财政年份:
    2024
  • 资助金额:
    $ 15万
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Collaborative Research: CIF-Medium: Privacy-preserving Machine Learning on Graphs
合作研究:CIF-Medium:图上的隐私保护机器学习
  • 批准号:
    2402815
  • 财政年份:
    2024
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    $ 15万
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HarmonicAI: Human-guided collaborative multi-objective design of explainable, fair and privacy-preserving AI for digital health
HarmonicAI:用于数字健康的可解释、公平和隐私保护人工智能的人工引导协作多目标设计
  • 批准号:
    EP/Z000262/1
  • 财政年份:
    2024
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Collaborative Research: CIF-Medium: Privacy-preserving Machine Learning on Graphs
合作研究:CIF-Medium:图上的隐私保护机器学习
  • 批准号:
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合作研究:CIF-Medium:图上的隐私保护机器学习
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  • 财政年份:
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HarmonicAI: Human-guided collaborative multi-objective design of explainable, fair and privacy-preserving AI for digital health
HarmonicAI:用于数字健康的可解释、公平和隐私保护人工智能的人工引导协作多目标设计
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HarmonicAI: Human-guided collAboRative Multi-Objective design of explaiNable, faIr and privaCy-preserving AI for digital health
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  • 批准号:
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  • 财政年份:
    2024
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    $ 15万
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Privacy-preserving machine learning through secure management of data's lifecycle in distributed systems: REMINDER
通过安全管理分布式系统中的数据生命周期来保护隐私的机器学习:提醒
  • 批准号:
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    $ 15万
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    Research Grant
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HarmonicAI:用于数字健康的可解释、公平和隐私保护人工智能的人工引导协作多目标设计
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