Networking, Data, and Security systems (NDSs) Group



The Networking, Data, and Security systems (NDSs) research group at McMaster University focuses on developing theories, technologies, tools and systems to address the challenges of emerging big data applications, cloud and/or mobile systems, and the involved security and privacy concerns, therefore push the next society-transforming change.

Research Areas

  • Information and Data Systems
  • Mobile and Pervasive Computing
  • Security and Privacy

  • Descriptions

    Information and Data Systems

    In recent years, we have witnessed gigantic volume of data, generated in very fast speed. On the other hand, with the recent advances of computing devices, the gigantic volume of data have helped researchers to make a breakthrough in machine learning techniques. However, a set of challenges and concerns for online big data processing havenít been successfully addressed. My research objective is to enable dramatic advances in the research of big data systems, by devising innovative methods and algorithms to address big data challenges, building practical and useful big data systems, providing framework support for a wide range of big data applications, and testing them through real-life deployments.

    Among the big data applications, video data is considered as biggest big data. In this domain, we conduct research on online content-based search, content-based similarity comparison, inforamtoin fusion between video contents and textal descriptions, as well as the general system support for stream processing of unstructured data.

    Mobile and Pervasive Computing

    Computing devices (e.g., smart phones, wireless sensors) turn progressively smaller and more powerful, these devices have made significant contribution to data and information collection. We build mobile and pervasive computing systems which make their computing and communication capabilities fully play, and elegantly integrated with users. As an example, we have built a SafeCam system, which is a smartphone-based system that jointly leverages vehicle dynamics and the real-time traffic control information (e.g., traffic signals) to detect and study driver dangerous behaviors at intersections. In this domain, we are interested in various aspects of mobile and pervasive computing, such as security and privacy, novel applications, performance analysis and modeling, protocol design, and prototype system implementation.

    Security and Privacy

    (1) Privacy-preserving Sensing and Computing

    Participatory sensing applications rely on individuals to share local and personal data with others to produce aggregated models and knowledge. In this setting, privacy is an important consideration, and lack of privacy could discourage widespread adoption of many exciting applications. Our team develop privacy-preserving schemes which protect the privacy of individuals while allowing useful information and knowledge to be collected.

    (2) Protecting Against Side-Channel Information Leak for WLAN Users

    Side-channel information leaks have been reported in various online applications, especially, in wireless local area networks (WLANs) due to the shared-medium nature of wireless links and the ease of eavesdropping. Even when Wi-Fi traffic is encrypted, traffic characteristics are identifiable, which can be used to infer sensitive user activities and data. Our team investigates the such vulnerability and develops technologies against the side channel information leak in WLAN environments.

    (3) A Location Verification System in Location-based Social Network Services

    As location-based services (LBS) grow in popularity, there are many incentives for users to claim a forged location. The location cheating can be launched automatically and in a large scale at ease. In our research, we take Foursquare as an example LBS to investigate the vulnerability of location cheating. We design and implement a location verification system, and conduct experiments to evaluate the performance of our location verification system.

    (4) Detection of Information Leak Across Platforms

    Nowadays, Internet is full of information. Many people and organizations have websites, various social network accounts, tweets, etc. The across-platform information leak may threat users privacy severely, even though individual pages or posts do not disclose any private information. Detection of the across-platform information leak requires to search over a super large data space, which posts a great challenge on semantic analysis, and data indexing & storage systems. We will take the advantage of the development in big data systems, and explore the issue and develop theories, technologies, tools and systems to address the challenges in across-platform information leak detection.