Security and Privacy in Mobile Crowdsourcing for Internet of Things

在物联网（IoT）时代，移动众包已经成为一种流行而有效的数据收集和处理方法。数据所有者可以贡献物联网设备收集的原始数据，以完成数据密集型任务或提供数据驱动的服务，例如地点（例如餐厅或酒店）推荐或健康状况监测。一些设备包括传感器、车辆、智能家电、移动设备和其他传感器丰富的设备。近年来，移动众包已经成为一种新兴的商业模式，提供高效的数据收集，以及高效的通信平台和计算系统，为各种数据驱动的应用（如实时交通地图）提供数据服务。这种模式特别有效，因为它集成了人群的智能（例如，一个公共群体，每个成员都有能够感知和计算的物联网设备）和物联网设备的无处不在的感知。精确地选择来自数据所有者的贡献数据，目的是高效地解决特定任务或提供特定服务。与传统的传感器网络相比，移动众包不仅增强了对采集数据的本地处理能力，而且实现了语义复杂数据与人类智能的融合。不幸的是，尽管移动众包在物联网中带来了巨大的好处，但它仍然面临着许多安全、隐私和公平方面的挑战。拟议的研究旨在解决这些挑战，特别是考虑从物联网设备中收集安全和隐私保护的分散数据，这在以前的研究报告中没有考虑到。在本研究中，我们的目标是为数据驱动的物联网应用设计一个安全、隐私保护和公平的移动众包框架。具体而言，我们将提出高效和安全的来源方案，以令人满意的方式保证数据源的可靠性、问责性和可信度，并设计保护隐私的数据收集和分析方案，以防止数据所有者的隐私泄露。此外，我们将开发基于区块链的公平数据交换解决方案，鼓励数据所有者共享数据。拟议的研究将有助于加拿大建立和加强其在信息和通信技术方面的领导地位。因此，还可以培育和设计新的商业模式和收入来源，以确保加拿大信息和通信技术部门的持续稳定增长。拟开展的研究重点是在快速发展的物联网领域开发和应用新技术和解决方案。通过该计划培训的高素质人才（HQP）将配备安全技术，以及无线通信和移动计算；这些对于当今物联网时代的成功至关重要，并将有助于确保加拿大ICT行业的繁荣未来。

Mobile crowdsourcing has emerged as a popular and effective method for data collection and processing in the Internet of Things (IoT) era. It is open to data owners to contribute their raw data collected by IoT devices to fulfill data intensive tasks or provide data-driven services, such as place (e.g., restaurant or hotel) recommendation or health condition monitoring. Some devices include sensors, vehicles, smart appliances, mobile devices and other sensor-rich devices. Mobile crowdsourcing, in recent years, has become an emerging business model to provide efficient data collection, as well as an efficient communication platform and computing system to deliver data services to a variety of data-driven applications, such as real-time traffic maps. This model is especially effective as it integrates the intelligence of a crowd (e.g., a public group and each member has IoT devices capable of sensing and computing) and the ubiquitous sensing of IoT devices. The contributed data from data owners are precisely selected with the purposes to solve the specific tasks or provide particular services efficiently and effectively. Compared with the traditional sensor networks, mobile crowdsourcing not only enhances local processing capability on collected data, but also enables the integration of semantically complex data with human intelligence. Unfortunately, despite the tremendous benefits of mobile crowdsourcing in IoT, it still faces many security, privacy and fairness challenges. The proposed research is envisioned to address these challenges, particularly by considering secure and privacy preserving decentralized data collection from IoT devices, which has not been considered in previously reported studies. In this research, our goal is to design a secure, privacy preserving and fair mobile crowdsourcing framework for data-driven IoT applications. Specifically, we will propose efficient and secure provenance schemes to guarantee reliability, accountability and trustworthiness of data sources in a satisfactory way, and design privacy preserving data collection and analysis schemes to prevent privacy leakage of data owners. In addition, we will develop blockchain based fair data exchange solutions to encourage data sharing for data owners. The proposed research will help Canada establish and reinforce its leadership in ICT (Information and Communication Technologies). As such, new business models and revenue streams can also be fostered and devised to ensure continued steady growth of the ICT sector in Canada. The proposed research focuses on the development and application of new technologies and solutions in the rapidly evolving field of IoT. The Highly Qualified Personnel (HQP) trained through the program will be equipped with security technologies, as well as wireless communications and mobile computing; these are crucial for success in today's era of IoT, and will help to ensure a prosperous future for the Canadian ICT industry.