A Provably Secure and Efficient Range Query Scheme for Outsourced Encrypted Uncertain Data From Cloud-Based Internet of Things Systems
Cheng Guo, Shenghao Su, Kim‐Kwang Raymond Choo, Pengxu Tian, Xinyu Tang
Abstract
The outsourcing of data is becoming increasingly commonplace as data is constantly been synchronized between user systems (e.g., personal computers and sensor devices) and cloud computing servers. However, to ensure data privacy, it is necessary to encrypt sensitive data prior to outsourcing. Limitations such as measurement, network delays, and data obfuscation may, however, result in uncertain data. Compared with searching over encrypted certain data, processing queries for encrypted uncertain data is more challenging. In this article, we propose a secure and efficient range query scheme over outsourced encrypted uncertain data, for example, from Internet of Things (IoT) systems. Specifically, we use pivot mapping to map data to a low-dimensional space to facilitate calculation and processing while preserving some of the original relevance among the data. Additionally, we encode data and then map codes into multiple Bloom filters which are organized by a binary tree-based index. Our scheme achieves data privacy, hides the relevance among data, and also supports efficient queries. We analyze the security and evaluate the performance of our approach using experiments on Microsoft Azure. The analysis and experimental results demonstrate that our proposed approach is secure and efficient.