Cluster Based Efficient Privacy Preserving Data Aggregation (CEPPDA) In People Centric Sensing Networks

Abstract

In the current era of technological advancements, the operations of sensing, computations, data storage and communications are processed effectively with mobile based sensing patterns according to the human activities. This can be termed as People-centric Sensing Network that makes mobile devices into universal mobile sensing nodes that are facilitating many public and personal data sensing applications. However, the network has some security issues in large volume data sharing and data aggregation. With that note, this paper develops a Cluster based Efficient Privacy Preserving Data Aggregation (CEPPDA) that uses a combined data aggregation pattern to induce secure and privacy preserving data aggregation. Moreover, the clustering is incorporated here with two conceits namely, inter and intra cluster structures. In that, intra-cluster performs data slicing and data confusing operations for securing the shared data over network from various malicious nodes and attacks. Further, Malicious Node Detection (MND) process is also framed for effectively tightening the security process in the data communications of People Centric Sensing Network. The Security Analysis is carried out for evaluating the proposed model and the results are analysed for computation and communication overhead. The results outperform the existing models, observed from comparative evaluations.