Conference Papers Year : 2024

Privacy-Preserving Clustering for Multi-dimensional Data Randomization Under LDP

Abstract

Randomization of multi-dimensional data under local differential privacy is a significant and practical application of big data. Because of the dimensionality issues, most existing works suffer from low accuracy when estimating joint probability distributions. In this paper, a set of attributes is divided into smaller clusters where the attributes are associated in terms of their dependencies. A privacy-preserving algorithm is proposed to estimate the dependencies of an attribute without disclosing the private values in the multi-dimensional data. Local differential privacy is guaranteed in the scheme. Using the clusters of attributes, the joint probabilities for multi-dimensional data can be estimated efficiently using two building blocks, called RR-independent and RR-Ind-Joint schemes. The experiments using some open datasets demonstrate that the dependencies of attributes can be estimated accurately and that the proposed algorithm outperforms existing state-of-the-art schemes in cases where the dimensionality is high.
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Friday, January 1, 2027
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Friday, January 1, 2027
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Dates and versions

hal-04953457 , version 1 (18-02-2025)

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Hiroaki Kikuchi. Privacy-Preserving Clustering for Multi-dimensional Data Randomization Under LDP. 38th IFIP International Conference on ICT Systems Security and Privacy Protection (SEC), Jun 2023, Poznan, Poland. pp.15-29, ⟨10.1007/978-3-031-56326-3_2⟩. ⟨hal-04953457⟩
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