Enhancing user privacy by data driven selection mechanisms for finding transmission-relevant data samples in energy recommender systems

In order to find energy saving potentials, future home energy recommender systems needs a large database of historic energy consumption information from various appliances. Having reference data, those systems could decide whether an appliance is wasting energy or not. However, the collection of this reference data degrades the user privacy as energy traces contain sensitive information which allows the exhibition of user behavior. In order to mitigate those privacy implications, we propose a method of sparse data collection. Our proposed solution minimizes the amount of collected reference data by removing energy traces which do not provide new information for the recommender system. Our proposed solution is capable of reducing the collected amount of data by a factor of 2 without lowering the accuracy of the future home energy recommender system.