Source-Channel Coding, Data Privacy and Contagion Mitigation in Communication and Information Networks

通信和信息网络中的源通道编码、数据隐私和传染缓解

• 批准号: RGPIN-2019-04471
• 负责人: Alajaji, Fady
• 金额: $ 3.35万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714899
• 关键词: Source; Channel; Coding; Data; Privacy; Source; Channel; Coding; Data; Privacy

This proposal concerns the enhancement and continuation of our research program in network information theory and communications. Specifically, we will develop advanced source-channel coding, data privacy and contagion mitigation solutions for communication and information networks. This three-pronged objective addresses critical and complementary challenges facing future generation big data networks in which information needs to be reliably and promptly exchanged between multiple devices, efficiently harnessed without compromising user privacy and securely stored in databases that are immune to contamination from malicious attacks. Building on our achievements, we will focus on the following main themes. (i) Bi-Directional Communication: Establish core Shannon-theoretic limits for two-way communication networks where each terminal receives and transmits signals simultaneously. Guided by our theoretical findings, we will then design state-of-the-art source-channel codes for full duplex communication systems. (ii) Data Privacy: Investigate data privacy mechanisms for decentralized networks using information and estimation theoretic methods. Key aims are to enable data collection agents to optimize system utility without leaking sensitive user information and construct effective privacy-preserving algorithms for big data repositories. (iii) Contagion Mitigation: Develop stochastic contagion models for network epidemics with special emphasis on the accurate representation of both malware spread in communication networks and opinion dynamics in social networks. We will also design and thoroughly validate robust curing techniques based on maximum-entropy and game-theoretic principles for infection control and mitigation in large-scale networks. The proposed research will feature the rigorous training of seven highly qualified personnel per year (3 Ph.D., 2 M.Sc. and 2 undergraduate students), immersing them in challenging topics in information theory, wireless communications, data security and network epidemics and resulting in innovative high-quality publications. The results of this research are anticipated to provide a deeper understanding of the theory and practice of source-channel coding for two-way networks and contribute to the sustainable evolution of wireless communications and sensor networks technologies. It is also expected to contribute to the deployment of resilient user privacy techniques and the judicious control and containment of contagious phenomena in big data networks.

该提案涉及我们在网络信息理论和通信方面的研究计划的增强和延续。具体来说，我们将开发用于通信和信息网络的高级源通道编码，数据隐私和缓解解决方案。这个三管齐下的目标解决了未来一代大数据网络面临的关键和互补挑战，在这些大数据网络中，需要可靠并迅速交换多个设备之间的信息，而在不损害用户隐私的情况下可以有效利用并牢固地存储在对恶意攻击中污染的数据库中。在我们的成就的基础上，我们将专注于以下主要主题。 （i）双向通信：为双向通信网络建立核心香农理论限制，每个终端同时接收和传输信号。在我们的理论发现的指导下，我们将为完整的双工通信系统设计最先进的源通道代码。 （ii）数据隐私：使用信息和估计理论方法研究分散网络的数据隐私机制。关键目的是使数据收集代理在不泄漏敏感用户信息的情况下优化系统实用程序，并为大数据存储库构建有效的隐私保护算法。 （iii）传染性缓解：为网络流行病开发随机传染模型，特别强调了在通信网络中传播的恶意软件和社交网络中的意见动态的准确表示。我们还将根据大型网络中的最大渗透和理论原理设计并彻底验证强大的固化技术，以进行感染控制和缓解。 拟议的研究将以每年七名高素质的人员（3博士学位，2个M.Sc和2名本科生）进行严格的培训，使他们沉浸在信息理论，无线通信，数据安全和网络流行病上的挑战性主题中，并导致创新的高质量出版物。预计这项研究的结果将对双向网络的源渠道编码的理论和实践有更深入的了解，并有助于无线通信和传感器网络技术的可持续发展。预计它将有助于弹性用户隐私技术的部署，以及在大数据网络中明智的控制和控制感染现象的明智控制和遏制。