Communication-efficient privacy-preserving query for fog-enhanced Internet of Things

Abstract

Internet of Things (IoT) has attracted significant attention in recent years and various IoT devices including industrial and utility components and other items embedded with electronics, sensors, and network connectivity already provide rich services to the end users. IoT devices report their data directly to the cloud computing constantly, which causes big data challenges in both storage and transmission. The classic centralized cloud computing paradigm is an ideal solution to deal with the storage issues. However, the cloud computing paradigm faces the challenges of low capacity, high latency, security and privacy and network failure. To address these challenges, the concept of the fog computing has been proposed by Cisco [8]. Instead of sending all the data to the cloud for processing and storing, fog computing provides local data processing capability and storage to fog devices. The goal of fog computing is to improve efficiency and reduce the amount of data transmitted to the cloud. Nevertheless, IoT still faces some security and privacy challenges. Query service is one of the standard services in IoT applications, It is when, an end user requests a value from an IoT device and the server is responsible for sending the value from the specific IoT device as per the query. In some IoT scenarios, privacy-preservation may be required for both the client and the service provider. Therefore, privacy preserving query schemes are desirable in some IoT applications. In this work, we proposed two privacy preserving query schemes with efficient communications. PQuery is characterized by combining private information retrieval and 1-out-of-m oblivious transfer techniques to preserve privacy for both the end user and the service provider in IoT query service. From the performance analysis, PQuery is very efficient in term of communication overheads, i.e., achieving O(n1/3) between the end user and the fog device. However, we also realize that the computational costs are not efficient in PQuery, especially the computational costs at the fog device. Therefore, in the second work, we tried to achieve a better balance between the communication and computational costs. We proposed XRQuery which is inspired by the XNOR gates in logical circuits to achieve privacy preservation for both the service provider and user in an IoT query service. While XRQuery is highly efficient in terms of communication cost, i.e., achieving O(log n) between the end user and the fog device, extensive performance evaluations show that it is much faster than PQuery in all three stages (end user query, fog device response and end user result checking).