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Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first present an optimal global-recoding method for the (α, k)-anonymity problem. Next we propose two scalable local-recoding algorithms which are both more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general cases.

Energy-efficient lighting installations, together with dimming controls, can make a good contribution to building energy conservation. Energy-efficient lamp fittings provide high light-output values with less power consumption, and electronic circuitry is more efficient than conventional ballasts. Dimming controls can lower lamp output to save energy when illuminance from daylight and light fittings exceeds the target value. This article studies lighting energy consumption and cost analysis when such lighting schemes are used. The findings would be useful for planning green and sustainable energy schemes and applicable to other spaces with similar architectural layouts and lighting control systems.