Advanced Security and Privacy Technologies for Data Protection: Analysis, Design and Application in Big Data Era

大数据被广泛认为是一种新的“自然资源”，已成为加拿大经济增长和竞争力的关键驱动力。为了使加拿大适应大数据时代，我们不仅需要准备新一代善于管理大数据的数据工程师，还需要准备可靠的大数据处理平台，以实现大数据时代安全和保护隐私的协作计算。因此，拟议研究的广泛目标是关注高效、安全和保护隐私的大数据处理和协作计算。 由于大数据具有海量、高速、多样性（“3V”）的特点，传统的安全和隐私解决方案无法直接迁移以充分应对大数据背景下出现的新挑战。因此，我们需要高度重视大数据安全和隐私研究。然而，目前致力于大数据安全和隐私挑战的著作很少。为了填补这一空白，该提案中的努力将为大数据安全和隐私研究提供新的思路。具体来说，该研究将采用跨学科的方法研究一套先进的安全和隐私技术，即通过结合密码学、分布式计算和数据挖掘技术，为大数据提供安全可靠的数据收集、存储、传输和处理。具体而言，该提案将解决大数据“3V”特征带来的重大技术挑战，主要包括以下四个方面： i) 在大数据获取、组织和分析阶段，开发轻量级、安全的大数据密码协议； ii) 开发用于大数据计算的安全分布式编程框架； iii) 开发保护隐私的大数据挖掘，以防止大数据中无意的隐私泄露；和 iv) 为大数据开发可靠、来源集成且访问可控的数据来源。 拟议的研究将集中广泛地利用首席研究员的研究专业知识，以及隶属于新不伦瑞克大学计算机科学学院的 CIC（加拿大网络安全研究所）的大力支持。这项前沿研究将为安全大数据的发展产生新的想法和知识，支持总部培训并为加拿大提供新的安全大数据解决方案。通过该项目培训的总部人员将配备大数据和安全技术，以及无线通信和移动计算；这些对于物联网和大数据计算时代的成功至关重要，并将有助于确保加拿大IT行业的繁荣未来。

Big data has been widely regarded as a new “natural resource” and has become a key driver of economic growth and competitiveness in Canada. In order to adapt Canada to the big data era, we need to prepare not only a new generation of data engineers skilled at managing big data, but also reliable big data process platforms to enable secure and privacy-preserving collaborative computing in big data era. The broad objective of the proposed research is therefore to focus on efficient, secure, and privacy-preserving big data processing and collaborative computing. As big data is characterized by high volume, velocity, and variety (“3V”), traditional security and privacy solutions cannot be directly migrated to fully address newly-emerging challenges in the context of big data. Therefore, we need to pay significant attention to big data security and privacy research. However, there are currently few works dedicated to security and privacy challenges in big data. To fill this gap, the efforts in this proposal shall shed new light on big data security and privacy research. Specifically, the proposed research will investigate a set of advanced security and privacy technologies using an interdisciplinary approach, i.e., by combining cryptography, distributed computing, and data mining techniques, to provide secure and reliable data collection, storage, transmission, and processing for big data. In particular, this proposal will address significant technical challenges arising from “3V” characteristics of big data, in the following four thrusts: i) developing lightweight and secure cryptographic protocols for big data in the phase of big data acquisition, organization, and analysis; ii) developing secure distributed programming frameworks for computing in big data; iii) developing privacy-preserving big data mining to prevent inadvertent privacy disclosures in big data; and iv) developing reliable, provenance-integrated, and access-controllable data provenance for big data. The proposed research will draw intensively and extensively on the Principal Investigator's research expertise, as well as strong supports from the CIC (Canadian Institute for Cybersecurity) affiliated with the University of New Brunswick Faculty of Computer Science. This cutting-edge research will generate new ideas and knowledge for the evolution of secure big data, enable HQP training and provide new secure big data solutions for Canada. The HQP trained through the program will be equipped with big data and security technologies, as well as wireless communications and mobile computing; these are crucial for success in the era of the Internet of Things and big data computing, and will help to ensure a prosperous future for the Canadian IT industry.