Achieving communication-efficient privacy-preserving range query in fog-based IoT

Abstract

Fog-based IoT (Internet of Things) is a fast-growing technology in which many firms and industries are currently investing to develop their own real-time and low latency decentralized data processing and analysis applications. It narrows down the gap between cloud and IoT end-devices as cloud computing is not a consistently perfect solution for many IoT applications. Compared with the traditional IoT solutions, fog-enabled IoT can offer a high level of compliance, better efficiency, and stronger security by providing local data pre-processing, filtering, and forwarding mechanisms. These benefits make the fog-enhanced IoT an appropriate paradigm for many IoT services in different applications varying from health monitoring systems to smart grids and even food manufacturing. However, fog-enhanced IoT arises many security and privacy concerns since fog nodes are deployed at the network edge and may not be fully trustable. Furthermore, fog is considered as a non-trivial extension of the cloud, and thus some security and privacy challenges will continue to persist. These challenges might affect the adaptation of fog computing into the IoT. At the same time, fog improves the IoT end-devices' security and privacy by offering an ideal platform to employ homomorphic encryption schemes. Homomorphic encryption schemes allow performing mathematical operations on ciphertexts without violating the IoT devices' privacy. This means that instead of separately delivering each IoT device's data to the control center, the fog nodes can forward the encrypted aggregated results. This alternative approach significantly reduces the communication overhead and greatly strengthens the security robustness. Thus, system developers can design data aggregation algorithms that yield more bandwidth-efficient, secure, and private schemes than traditional cloud deployment. In this thesis, we emphasize on range aggregate queries in fog-enhanced IoT. In particular, we carry on research on communication and computational efficient privacy-preserving range query processing schemes in which the querying user can efficiently execute range queries on IoT end-devices in the fog computing environment. The main contributions of this thesis can be summarized as 1) Taking the computational burden into consideration, we devise an efficient Symmetric Homomorphic Encryption (SHE) scheme. The proposed scheme maintains data privacy and security as well as supports homomorphic calculation in arithmetic circuits including both multiplication and addition operations. 2) To achieve higher communication performance, we develop some range decomposition/composition techniques to transfomr the range queries. These techniques transform a given range query [L; U] into corresponding data structures that realize privacy-preserving communication-efficient range aggregate query protocols. We develop three different decomposition/composition schemes and investigate their computational and communication performance. 3) Analysing the security of these developed schemes to ensure that proposed schemes are privacy-preserving, i.e. querying user's query and IoT end-devices' data can not be identified or profiled by not only fraudulent/dishonest but also honest-but-curious entities. 4) Conducting extensive performance evaluations to demonstrate the effectiveness of the proposed schemes in terms of communication outcomes and computational effort reduction.