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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Acknowledgment
Part of this work was supported by JSPS KAKENHI Grant Number JP18H04099, 23K11110 and JST, CREST Grant Number JPMJCR21M1, Japan. Author thanks prof. Josep Domingo-Ferrer and Dr. Jordi Soria-Comas for discussion and suggestions.
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Kikuchi, H. (2024). Privacy-Preserving Clustering for Multi-dimensional Data Randomization Under LDP. In: Meyer, N., Grocholewska-Czuryło, A. (eds) ICT Systems Security and Privacy Protection. SEC 2023. IFIP Advances in Information and Communication Technology, vol 679. Springer, Cham. https://doi.org/10.1007/978-3-031-56326-3_2
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DOI: https://doi.org/10.1007/978-3-031-56326-3_2
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