Abstract
Recent advances in wireless sensor networks for ubiquitous health and activity monitoring systems have triggered the possibility of addressing human needs in smart environments through recognizing human real-time activities. While the nature of streams in such networks requires efficient recognition techniques, it is also subject to suspicious inference-based privacy attacks. In this paper, we propose a framework that efficiently recognizes human activities in smart homes based on spatiotemporal mining technique. In addition, we propose a technique to enhance the privacy of the collected human sensed activities using a modified version of micro-aggregation approach. An extensive validation of our framework has been performed on benchmark data sets yielding quite promising results in terms of accuracy and privacy-utility tradeoff.