An LDP-Based Privacy-Preserving Longitudinal and Multidimensional Range Query Scheme in IoT

Range queries are extensively used in various Internet of Things (IoT) applications as an essential functional requirement to provide intelligent and personalized services to users. In IoT environments, diverse types of data are generated, necessitating the design of range query schemes for multidimensional data. Privacy preservation is a key concern for range queries, leading to the proposal of several privacy-preserving solutions. However, most of these solutions are either inefficient or impractical. Moreover, existing approaches often suffer from the problem of longitudinal data privacy leakage, posing a serious threat to user privacy. Although some efforts have addressed the privacy issues of longitudinal data, practical implementations have been hesitant. To tackle these challenges, we propose a local differential privacy-based (LDP) privacy-preserving scheme called the privacy-preserving longitudinal and multidimensional range query (PLMRQ) for IoT. Our scheme focuses on lightweight privacy preservation and eliminates the need for a trusted third party (TTP). First, it is designed based on a double randomizer, ensuring effective privacy preservation of longitudinal data over time. Second, to mitigate excessive noise injection, PLMRQ dynamically constructs a binary tree structure by hierarchically decomposing the entire domain. Finally, through the utilization of a post-processing technique, the mean square error is efficiently reduced. Theoretical and experimental results demonstrate that the proposed PLMRQ maintains competitive utility while rigorously satisfying
$\ln {({e^{\epsilon _{1}+t\epsilon _{2}}+1}/{e^{\epsilon _{1}}+e^{t\epsilon _{2}}})}$
-LDP with an upper bound of
$\epsilon _{1}$
and a lower bound of
$\epsilon _{2}$
.