Efficient Secure Distributive Machine Learning over Mobile Network Systems

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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.