Efficient Secure Distributive Machine Learning over Mobile Network Systems

近年来，智能手机、可穿戴设备和平板电脑应用程序的使用正在增长，而且还在广泛增加。移动应用程序使用大量敏感数据，如用户信息和位置、医疗保健数据、银行信息和生物信息学。物联网(IoT)是另一个海量大数据资源，未来的5G蜂窝系统旨在支持其应用。机器学习(ML)在许多实践中已成为决策过程的力量，例如，医疗保健中的趋势预测控制疾病，金融中的金融市场预测，经济增长的智能消费者目标，基础设施中的流量模式和能源使用，以及网络防御系统中的垃圾邮件检测、流量分析和入侵检测，仅举几例。然而，处理这些请求的服务器需要访问来自设备和用户的大量敏感数据，这使得用户数据的安全和隐私面临巨大风险。Google、Amazon、Microsoft和IBM等云服务提供商提供机器学习即服务(MLaaS)工具，如数据可视化、API和其他工具作为其服务的一部分。 建议的研究考虑一种机器学习模型，其中敏感数据保留在移动设备(智能手机、传感器、车载设备、物联网设备等)上。在本地处理一些ML算法。在这种情况下，训练数据被分发到移动设备，并且服务器/云使用本地计算的更新来学习共享模型。我们称之为基于移动网络的分布式机器学习(DMLMN)模型。我们面临的问题是针对服务器/云提供数据的安全性和保密性，同时保持服务器/云执行ML算法中必要计算的能力。因此，这项研究的目的是探索一种新的范例，用于设计具有高效计算、通信和网络攻击弹性的安全DMLMN算法和协议。 我们将采用掩码-然后-噪声消除加密的新范例，并评估算法和协议在主动恶意攻击(例如，移动设备或服务器可能是恶意的，攻击者可能伪造训练数据导致偏差模型，如何检测它们等)下的性能和权衡。我们将尝试通过结合密码学、编码理论和物理层安全来实现这一目标。 研究结果将为分布式安全移动学习提供新的高效实用的算法和协议。这项研究将使金融、生物信息学、卫生和医疗、互联网和云数据产业以及数据市场等行业受益。在这种独特的环境中接受培训的HQP将丰富加拿大的工程师和研究人员人才库，使他们拥有创造创新解决方案的专业知识，以应对不断变化的挑战，开发安全的DMLMN。

In recent years, the use of smartphones, wearables, and tablet applications is growing, and extensively increasing. Mobile applications use a large amount of sensitive data such as users' information and location, health care data, banking information, and bioinformatics. The Internet--of--Things (IoT) is another resource of massive big data, and future 5G cellular systems aim to support its applications. Machine learning (ML) has risen as the power of decision-making processes in many practices, e.g., disease control by trend prediction in healthcare, predictions for financial markets in finance, intelligent consumer targeting for economic growth, traffic patterns and energy usage in infrastructure, and spam detection, traffic analysis and intrusion detection in network defence systems, to name a few. However, a server handling these requests needs to access a large amount of sensitive data from devices and users, which puts the security and privacy of users' data at immense risk. Cloud service providers such as Google, Amazon, Microsoft, and IBM provide machine--learning--as--a--service (MLaaS) tools, such as data visualization, APIs, and others as part of their service. The proposed research considers a machine learning model where sensitive data remains on the mobile devices (smart phones, sensors, automotive on-board units, IoT devices, etc.) that process some ML algorithms locally. In this scenario, training data is distributed to mobile devices, and the server/cloud learns a shared model using locally computed updates. We term this as distributive machine learning over mobile networks (DMLMN) model. The problem that we are facing is providing the security and privacy of data against the server/cloud, while maintaining the server/cloud's ability to perform the necessary computations in ML algorithms. The goal of the proposed research is therefore to investigate a new paradigm for designing algorithms and protocols with efficient computation, communication, and cyber-attack resiliency for secure DMLMN. We will adopt a new paradigm of mask--then--encrypt with noise cancellation and evaluate the performance and tradeoffs of algorithms and protocols under active malicious attacks (e.g., mobile device or server may be malicious, attacker may craft the training data which results in a bias model, how to detect them, etc.). We will attempt to achieve this goal through combinations of cryptography, coding theory, and physical layer security. The outcome will provide new efficient and practical algorithms and protocols for distributive secure mobile learning. The research will benefit sectors such as financial, bioinformatics, health and medical, Internet and cloud data industrials, and data market. HQP trained in this unique environment will enrich the talent pool of engineers and researchers in Canada with the expertise to create innovative solutions to the evolving challenges of developing secure DMLMN.