Abstract

The fast proliferation of edge devices for the Internet of Things (IoT) has led to massive volumes of data explosion. The generated data is collected and shared using edge-based IoT structures at a considerably high frequency. Thus, the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge nodes. To address the identified issue, we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing scheme. In particular, we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes, where IoT devices and edge nodes are two parties of the game. IoT devices may make malicious requests to achieve their goals of stealing privacy. Accordingly, edge nodes should deny malicious IoT device requests to prevent IoT data from being disclosed. They dynamically adjust their own strategies according to the opponent’s strategy and finally maximize the payoffs. Built upon a developed application framework to illustrate the concrete data sharing architecture, a novel algorithm is proposed that can derive the optimal evolutionary learning strategy. Furthermore, we numerically simulate evolutionarily stable strategies, and the final results experimentally verify the correctness of the IoT data sharing privacy preservation scheme. Therefore, the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.

Introduction

The Internet of Things (IoT) can be described as a network that connects all entities with the internet through information sensing devices to realize the function of intelligent identification, operation, and management. The IoT is attracting considerable attention with the continuous development of wireless communications, radio frequency identification, and low-cost sensors. However, IoT network problems, such as security and privacy, are rapidly emerging, and thus, privacy protection is of paramount importance [[1], [2], [3], [4], [5]].

     Edge-based IoT [6] is experiencing rapid growth because traditional cloud computing is unable to immediately handle the massive data generated by edge nodes with the rapid development and wide application of the IoT, big data, and 5G/6G networks [7]. In this architecture, edge computing provides parts of cloud services for IoT devices on the edge of the network. It focuses on solving the problems of high latency, network instability, and low bandwidth [8]. Its applications are initiated on the edge side, resulting in the faster response of cloud services, which meets the basic IoT requirements in real-time business, application intelligence, and privacy preservation.

Conclusion and future work

In the current work, we have proposed an edge computing-oriented and evolutionary game-based privacy preservation model to acquire the optimal learning strategy for IoT data sharing. In our scheme, the edge nodes first assess whether the request is normal or malicious and then react with action grants or denies when data is released from the cloud storage system. Under this circumstance, malicious requests can be precisely identified and effectively prohibited from the source. Furthermore, we have analyzed the stability of each equilibrium point via the replication dynamic equations and raised a framework and an algorithm for this model, optimizing the expected gain and receiving the best evolutionary strategy. Additionally, the relevant experimental simulations verify that our scheme is superior from the perspectives of reliability and privacy preservation.

     For future work, we will focus on other game models, such as signaling games and repeated games, to handle privacy preservation during IoT data sharing. In addition, we will take the privacy preservation of a data sender into consideration instead of a data receiver, minimizing the probability of IoT nodes sending malicious requests. Furthermore, it is highly likely to incur malicious attacks in the process of merging data from different IoT devices. Therefore, privacy preservation under IoT data aggregation is another direction with great promise.