Privacy Enhancement Via Dummy Points in the Shuffle Model

Li, Xiaochen; Liu, Weiran; Feng, Hanwen; Huang, Kunzhe; Hu, Yuke; Liu, Jinfei; Ren, Kui; Qin, Zhan

doi:10.1109/tdsc.2023.3263162

Cited by 3 publications

(1 citation statement)

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“…Compared with central differential privacy and local differential privacy, shuffled differential privacy [17,25] consists of a local perturbation, shuffle and analysis. The specific mathematical definition of shuffled differential privacy is as follows: Definition 2 ((ϵ, δ)-shuffled differential privacy).…”

Section: Shuffled Differential Privacymentioning

confidence: 99%

A Range Query Scheme for Spatial Data with Shuffled Differential Privacy

Li,

Zhang,

et al. 2024

Mathematics

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The existing high-dimensional or multi-dimensional geographic spatial datasets have a large amount of data. When third-party servers collect and publish them, privacy protection is required to prevent sensitive information from being leaked. Local differential privacy can be used to protect location-sensitive information during range queries. However, the accuracy of a range query based on local differential privacy is affected by the distribution and density of spatial data. Based on this, aiming at the distribution and density characteristics of data, we designed a dpKD tree that supports high-precision range queries with shuffled differential privacy, and designed an algorithm KDRQ for range queries based on shuffled differential privacy. First, we employed the dpKD to divide the data. Then, we shuffled the data based on SRRQ and reconstructed the tree. Finally, we used the SDRQ algorithm for the response range query. The experimental results show that the query accuracy of the KDRQ algorithm was at least 1–4.5 times higher than that of the existing algorithms RAPPOR, PSDA and GT-R under the same privacy budget.

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Section: Shuffled Differential Privacymentioning

confidence: 99%