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

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加强和延续。具体来说，我们将为通信和信息网络开发先进的源信道编码、数据隐私和传染缓解解决方案。这个三管齐下的目标解决了下一代大数据网络面临的关键和互补的挑战，其中信息需要在多个设备之间可靠、及时地交换，在不损害用户隐私的情况下有效利用信息，并安全地存储在不受恶意攻击污染的数据库中。在我们取得的成就的基础上，我们将重点关注以下主题。 (i) 双向通信：为双向通信网络建立核心香农理论限制，其中每个终端同时接收和发送信号。在我们的理论发现的指导下，我们将为全双工通信系统设计最先进的源信道编码。 (ii) 数据隐私：使用信息和估计理论方法研究去中心化网络的数据隐私机制。主要目标是使数据收集代理能够在不泄露敏感用户信息的情况下优化系统效用，并为大数据存储库构建有效的隐私保护算法。 (iii) 传染缓解：开发网络流行病的随机传染模型，特别强调准确表示通信网络中恶意软件的传播和社交网络中的舆论动态。我们还将基于最大熵和博弈论原理设计并彻底验证强大的治愈技术，用于大规模网络中的感染控制和缓解。 拟议的研究将每年对七名高素质人员（3名博士生、2名硕士生和2名本科生）进行严格培训，让他们沉浸在信息论、无线通信、数据安全和网络流行病等具有挑战性的主题中并产生创新的高质量出版物。这项研究的结果预计将提供对双向网络源信道编码的理论和实践的更深入的理解，并有助于无线通信和传感器网络技术的可持续发展。它还有望有助于弹性用户隐私技术的部署以及大数据网络中传染现象的明智控制和遏制。