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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689368
• 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.**

这项建议关系到我们在网络信息理论和通信方面的研究计划的加强和继续。具体地说，我们将为通信和信息网络开发先进的信源信道编码、数据隐私和传染病缓解解决方案。这一三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，在这些网络中，信息需要在多个设备之间可靠和迅速地交换，在不损害用户隐私的情况下得到有效利用，并安全地存储在不受恶意攻击污染的数据库中。在已有成果的基础上，我们将专注于以下主要主题。*(I)双向通信：为每个终端同时接收和发送信号的双向通信网络建立核心香农理论极限。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信源-信道编码。*(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。主要目标是使数据收集代理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)传染病缓解：为网络流行病开发随机传染模型，特别强调准确表示恶意软件在通信网络中的传播和在社交网络中的舆论动态。我们还将设计并彻底验证基于最大熵和博弈论原理的稳健治愈技术，用于大规模网络中的感染控制和缓解。*拟议的研究将以每年严格培训7名高素质人员为特色(3名博士，2名硕士)。和2名本科生)，让他们沉浸在信息理论、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。这项研究的结果有望加深对双向网络信源-信道编码理论和实践的理解，并为无线通信和传感器网络技术的可持续发展做出贡献。预计它还将有助于部署弹性用户隐私技术，并明智地控制和遏制大数据网络中的传染性现象。**