As edge computing attains tremendous popularity, IoT devices always outsource their data to nearby edge servers for storing and pre-processing, which improves the efficiency of data processing and reduces the required network resources. For privacy-preserving, sensitive data is mostly encrypted before outsourcing. Nevertheless, large volumes of data in edge computing usually comes from multiple data sources, which means that they are encrypted with different secret keys, making it difficult for edge server to query and process. Existing solutions are mostly proposed for this problem in cloud computing, but they do not take into account that the limitations of computing and storage capabilities of edge devices will prevent them from performing computationally expensive operations. In this paper, we propose a lightweight privacy-preserving equality query scheme (LPEQ) in edge computing for the first time, which allows authorized users to perform equality query efficiently and privately on the encrypted data outsourced by multiple IoT devices. We also introduce a formal security model and prove that the LPEQ meets secure requirements against curious entities under this model. Meanwhile, our theoretical analyses and experimental evaluations demonstrate that the LPEQ performs better efficiency in terms of computation and communication while retaining privacy-preserving properties. Therefore, it is practical for applications in edge computing. INDEX TERMS Edge computing, lightweight cryptography, privacy-preserving, equality query. I. INTRODUCTION With the development of Internet of Things, large number of sensor devices explosively grow, such as smart phones, wearable devices, smart homes, etc. According to the latest statistics from Statista, there are expected to be more than 30 billion connected devices in 2020 [1]. This leads to huge amounts of data generated from the physical world. The Cisco Global Cloud Index (GCI) estimates that the data generated by devices, people and machines in the Internet of Things will exceed 500 ZB by 2020 [2]. Although the traditional central cloud computing can process massive data with its huge computing power, the network congestion and delay may occur for delay-sensitive and real-time data processing. Meanwhile, it is predicted global data center IP traffic will reach only 15.3 ZB by 2020 [3], [4]. This dilemma prompts us to not only generate large amounts of data, but also process them near the data sources [5]-[7]. The associate editor coordinating the review of this manuscript and approving it for publication was Tony Thomas.

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Location data have great value for facility location selection. Due to the privacy issues of both location data and user identities, a location service provider can not hand over the private location data to a business or a third party for analysis or reveal the location data for jointly running data analysis with a business. In this paper, we propose a newly constructed PSI filter that can help the two parties privately find the data corresponding to the items in the intersection without any computations and, subsequently, we give the PSI filter generation protocol. We utilize it to construct three types of aggregate protocols for facility location selection with confidentiality. Then we propose a ciphertext matrix compressing method, making one block of cipher contain lots of plaintext data while keeping the homomorphic property valid. This method can efficiently further reduce the computation/communication cost of the query process—the improved query protocol utilizing the ciphertext matrix compressing method is given followed. We show the correctness and privacy of the proposed query protocols. The theoretical analysis of computation/communication overhead shows that our proposed query protocols are efficient both in computation and communication and the experimental results of the efficiency tests show the practicality of the protocols.