The proliferation of mobile phones with integrated sensors makes large scale sensing possible at low cost. During mobile sensing, data mostly contain sensitive information of users such as their real-time location. When such information are not effectively secured, users’ privacy can be violated due to eavesdropping and information disclosure. In this paper, we demonstrated the possibility of unauthorized access to location information of a user during sensing due to the ineffective security mechanisms in most sensing applications. We analyzed 40 apps downloaded from Google Play Store and results showed a 100% success rate in traffic interception and disclosure of sensitive information of users. As a countermeasure, a security scheme which ensures encryption and authentication of sensed data using Advanced Encryption Standard 256-Galois Counter Mode was proposed. End-to-end security of location and motion data from smartphone sensors are ensured using the proposed security scheme. Security analysis of the proposed scheme showed it to be effective in protecting Android based sensor data against eavesdropping, information disclosure and data modification.

Mobile blockchain has achieved huge success with the integration of edge computing services. This concept, when applied in mobile crowd sensing, enables transfer of sensor data from blockchain clients to edge nodes. Edge nodes perform proof-of-work on sensor data from blockchain clients and append validated data to the chain. With this approach, blockchain can be performed pervasively. However, securing sensitive sensor data in a mobile blockchain (client/edge node architecture) becomes imperative. To this end, this paper proposes an integrated framework for mobile blockchain which ensures key agreement between clients and edge nodes using Elliptic Curve Diffie-Hellman algorithm. Also, the framework provides efficient encryption of sensor data using the Advanced Encryption Standard algorithm. Finally, key agreement processes in the framework were analyzed and results show that key pairing between the blockchain client and the edge node is a non-trivial process.

Automatic data annotation eliminates most of the challenges we faced due to the manual methods of annotating sensor data. It significantly improves users’ experience during sensing activities since their active involvement in the labeling process is reduced. An unsupervised learning technique such as clustering can be used to automatically annotate sensor data. However, the lingering issue with clustering is the validation of generated clusters. In this paper, we adopted the k-means clustering algorithm for annotating unlabeled sensor data for the purpose of detecting sensitive location information of mobile crowd sensing users. Furthermore, we proposed a cluster validation index for the k-means algorithm, which is based on Multiple Pair-Frequency. Thereafter, we trained three classifiers (Support Vector Machine, K-Nearest Neighbor, and Naïve Bayes) using cluster labels generated from the k-means clustering algorithm. The accuracy, precision, and recall of these classifiers were evaluated during the classification of “non-sensitive” and “sensitive” data from motion and location sensors. Very high accuracy scores were recorded from Support Vector Machine and K-Nearest Neighbor classifiers while a fairly high accuracy score was recorded from the Naïve Bayes classifier. With the hybridized machine learning (unsupervised and supervised) technique presented in this paper, unlabeled sensor data was automatically annotated and then classified.

The proliferation of mobile devices such as smartphones and tablets with embedded sensors and communication features has led to the introduction of a novel sensing paradigm called mobile crowd sensing. Despite its opportunities and advantages over traditional wireless sensor networks, mobile crowd sensing still faces security and privacy issues, among other challenges. Specifically, the security and privacy of sensitive location information of users remain lingering issues, considering the “on” and “off” state of global positioning system sensor in smartphones. To address this problem, this paper proposes “SenseCrypt”, a framework that automatically annotates and signcrypts sensitive location information of mobile crowd sensing users. The framework relies on K-means algorithm and a certificateless aggregate signcryption scheme (CLASC). It incorporates spatial coding as the data compression technique and message query telemetry transport as the messaging protocol. Results presented in this paper show that the proposed framework incurs low computational cost and communication overhead. Also, the framework is robust against privileged insider attack, replay and forgery attacks. Confidentiality, integrity and non-repudiation are security services offered by the proposed framework.

Internet of Things (IoT) has transcended from its application in traditional sensing networks such as wireless sensing and radio frequency identification to life-changing and critical applications. However, IoT networks are still vulnerable to threats, attacks, intrusions, and other malicious activities. Intrusion Detection Systems (IDS) that employ unsupervised learning techniques are used to secure sensitive data transmitted on IoT networks and preserve privacy. This paper proposes a hybrid model for intrusion detection that relies on a dimension reduction algorithm, an unsupervised learning algorithm, and a classifier. The proposed model employs Principal Component Analysis (PCA) to reduce the number of features in a dataset. The K-means algorithm generates clusters that serve as class labels for the Support Vector Machine (SVM) classifier. Experimental results using the NSL-KDD and the UNSW-NB15 datasets justify the effectiveness of our proposed model in detecting malicious activities in IoT networks. The proposed model, when trained, identifies benign and malicious behaviours using an unlabelled dataset.