Recent building emergency management research has highlighted the need for the effective utilization of dynamically changing building information. BIM (building information modelling) can play a significant role in this process due to its comprehensive and standardized data format and integrated process. This paper introduces a BIM based virtual environment supported by virtual reality (VR) and a serious game engine to address several key issues for building emergency management, for example, timely two-way information updating and better emergency awareness training. The focus of this paper lies on how to utilize BIM as a comprehensive building information provider to work with virtual reality technologies to build an adaptable immersive serious game environment to provide real-time fire evacuation guidance. The innovation lies on the seamless integration between BIM and a serious game based virtual reality (VR) environment aiming at practical problem solving by leveraging state-of-the-art computing technologies. The system has been tested for its robustness and functionality against the development requirements, and the results showed promising potential to support more effective emergency management.

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Transactions are records that contain a set of items about individuals. For example, items browsed by a customer when shopping online form a transaction. Today, many activities are carried out on the Internet, resulting in a large amount of transaction data being collected. Such data are often shared and analyzed to improve business and services, but they also contain private information about individuals that must be protected. Techniques have been proposed to sanitize transaction data before their release, and set-based generalization is one such method. In this article, we study how well set-based generalization can protect transactions. We propose methods to attack set-generalized transactions by exploiting contextual information that is available within the released data. Our results show that set-based generalization may not provide adequate protection for transactions, and up to 70% of the items added into the transactions during generalization to obfuscate original data can be detected by our methods with a precision over 80%.

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