An Efficient Heap Tree-Based Range Query Scheme Under Local Differential Privacy

Crowdsourcing, which is regarded as one of the most important data collection techniques in Internet of Things (IoT) and Big Data era, has received significant attention in recent years. However, privacy concerns persist across various crowdsourcing scenarios. In this article, aiming to address users’ privacy issues in crowdsourcing scenarios, we propose an efficient and privacy-preserving range query scheme under local differential privacy (LDP) setting. Specifically, given a domain
$\mathcal {V}=\{0, 1, 2,\ldots, d-1\}$
, where
$d=2^{w}$
, our proposed scheme integrates binary heap tree, prefix encoding, randomized response, and pseudo-random number generator techniques to enable each user to report only
$w$
bits as a query response, which is sufficient for a server to efficiently compute the range query result for any range
$[a, b]$
in the domain
$\mathcal {V}$
. Security analysis demonstrates that our proposed scheme can achieve
$\varepsilon $
-LDP, effectively preserving the privacy of users’ private items. In addition to its low communication overhead, performance evaluation also indicates our proposed scheme is computationally efficient when the precomputation is implemented at the server. Furthermore, our proposed scheme exhibits higher accuracy compared to previously reported flat and tree-based methods, especially for a large domain size
$d$
and a large range length
$m = b- a + 1$
.