Privacy-preserving Location-based Data Queries in Fog-enhanced Sensor Networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Original languageEnglish
Pages (from-to)12285-12299
Journal / PublicationIEEE Internet of Things Journal
Issue number14
Online published14 Dec 2021
Publication statusPublished - 15 Jul 2022


Fog computing has emerged as a promising framework with the rapid growth of Internet of Things (IoT). In fog computing, the new entity named fog device can help the cloud process the large amount of data generated by IoT devices. Along with this trend, a location-based query scheme that collects IoT devices’ data from specific areas is an important application, especially in fog-enhanced sensor networks. However, in this application, the cloud and fog devices require the user’s query, sensors’ locations, and sensor data so that it raises critical privacy and security concerns. In this paper, we devise a privacy-preserving location-based data query scheme in fog-enhanced sensor networks, which allows the cloud and fog devices to collect sensor data from a query area without learning the three kinds of information. Specifically, we resort to a cryptographic primitive named Somewhat Homomorphic Encryption (SHE) with ciphertext-packing to encrypt query, locations, and sensor data, and efficiently calculate the distances between user’s query and sensors. Then we show how to build a hardware-assisted data query scheme to extract the matched data based on the distances. We formally analyze the security strengths and implement the system prototype. In order to implement secure processing within SGX, we make an effort to adapt the existing mathematical libraries to the advanced SGX trusted environment. Evaluation results demonstrate that our proposed design is secure and efficient.

Research Area(s)

  • Cloud computing, Cryptography, Data privacy, Fog computing, Homomorphic encryption, Internet of Things, Internet of Things (IoT), Performance evaluation, Privacy-preserving Data Queries, Servers, Somewhat Homomorphic Encryption (SHE), Trusted Execution Environment