2024
DOI: 10.1002/cpe.8039
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LoHDP: Adaptive local differential privacy for high‐dimensional data publishing

Guohua Shen,
Mengnan Cai,
Zhiqiu Huang
et al.

Abstract: SummaryThe increasing availability of high‐dimensional data collected from numerous users has led to the need for multi‐dimensional data publishing methods that protect individual privacy. In this paper, we investigate the use of local differential privacy for such purposes. Existing solutions calculate pairwise attribute marginals to construct probabilistic graphical models for generating attribute clusters. These models are then used to derive low‐dimensional marginals of these clusters, allowing for an appr… Show more

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References 42 publications
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