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A Novel Method of Correlated Laplace Noise Generation for Differential Privacy on Time-Series Data

Lihui Mao & Zhengquan Xu

International Journal of Information Security and Privacy2025https://doi.org/10.4018/ijisp.372683article
ABDC C
Weight
0.50

What the paper says

Data correlation is crucial to privacy protection of time series data. Series indistinguishability provides a theoretical basis for ensuring differential privacy on correlated time series data and is implemented with the correlated Laplace mechanism (CLM), which has become a novel privacy-preserving method. CLM requires generating Laplace noise series with original data correlation. However, the existing method (CLM-S) can generate only Laplace noise series with nonnegative autocorrelation, which prevents it from achieving series indistinguishability on negatively correlated data, potentially compromising privacy guarantees in such scenarios. This study proposes a new method named CLM-M as well as its effective implementation (CLM-M-Delta) for generating correlated Laplace noise series through multiplication combination of four Gaussian noises. It has been theoretically proven that CLM-M can match negative correlations. The experimental results demonstrate that CLM-M-Delta effectively adapts to various data correlations and provides improved privacy performance over CLM-S.

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https://doi.org/https://doi.org/10.4018/ijisp.372683

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@article{lihui2025,
  title        = {{A Novel Method of Correlated Laplace Noise Generation for Differential Privacy on Time-Series Data}},
  author       = {Lihui Mao & Zhengquan Xu},
  journal      = {International Journal of Information Security and Privacy},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijisp.372683},
}

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A Novel Method of Correlated Laplace Noise Generation for Differential Privacy on Time-Series Data

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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