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.

One of the serious security breaches that threatens today's smart technologies is the broadcasting of false alarms. These alarms may severely affect road management systems. Vehicular networks suffer from this type of threat because of the opacity and the on-purpose broadcasting of false contents that occur frequently. Modern cloud-fog communication architectures force the organization of hierarchical communications, resulting in more robust governance of disseminated contents. This paper proposes a prediction technique that is based on a binary classification using hidden Markov model (HMM). Our contributions in this research area are as follows: 1) modeling fog data features to enforce organization of communication, 2) proposing a technique for the automatic modeling of HMM parameters, which facilitate the precise prediction of false contents, and 3) proposing a dissemination algorithm for fast communication. We conducted several experiments to measure and compare the performance of the proposed model with that of state-of-the-art classification algorithms. We also performed a misuse analysis to measure the ability of our proposed model to detect false alarms in synthesis and real-world datasets. In addition, we conducted a further analysis to demonstrate the sensitivity of our methodology to cutoff points. The results showed that our HMM-based technique provides promising prediction accuracy and significantly outperforms existing classification techniques in detecting false-alarm contents